research study about fare hike

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Abstract : The effects of THE EEFEECTS OF INCREASING FARE OF TRANSPORTATION TO SENIOR HIGH SCHOOL STUDENTS (2018-2019)

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Ejay Gumanas

This study seeks to investigate the level of social security satisfaction of Grade 12 beneficiaries towards Pantawid Pamilyang Pilipino Program (4P's). The researchers employed descriptive; cross-sectional research design and utilized a 22-item self-made questionnaire to measure the level of satisfaction with the use of 5-point Likert Scale, 1 as strongly disagree and 5 as strongly agree. The reliability of the questionnaire was evaluated with the use of Cronbach's alpha coefficient. The calculated alpha coefficient through SPSS software for the questionnaire was 0.981, which is an acceptable reliability. A total sample size of one hundred forty-one respondents were randomly chosen for the study. Mean was used to determine the level of satisfaction of the respondents towards 4P's and Paired t-test was utilized to find out the significant difference between two independent variables. Majority of the respondents were female rather than males and are aged 17-18 years old. Most of the respondents had siblings 4 and above. Furthermore, the study found out that the beneficiaries are satisfied with the cash grant provided by the 4P's and it fulfills their needs in terms of financial and educational. It is recommended for the 4P's students to maintain their spending lifestyle while it is advised for parents and school administration to consider the 4P's students given allowance. Observing different factors that may affect their spending lifestyle and satisfaction is also suggested.

Financial Literacy of Senior High School Students in Bacolod City

Angela Somcio

The research entitled LEVEL OF FINANCIAL LITERACY OF SENIOR HIGH SCHOOL STUDENTS FROM PRIVATE SCHOOLS OF BACOLOD CITY has the purpose to determine the level of financial literacy of senior high school students from different private schools of Bacolod City. The researchers made use of the descriptive-analytical scheme and the comparative correlational scheme to determine the objectives of the study. The study dwells on the areas spending habits, saving habits and financial knowledge in order to determine the overall financial knowledge. It uses the sex, grade level, district and family monthly income as variables. The researchers gathered data through thorough research and survey by form of a questionnaires. A total of 140 out of 9636 students from different private schools of Bacolod City were surveyed for this study. The findings of the study include concluding of having no significant difference in the level of financial literacy when participants are grouped according to grade level, district and family monthly income. But there is a significant difference in the level of financial literacy when grouped according to sex. Further results show that there are existing relationships between spending habits, saving habits and financial knowledge amongst each other. The general recommendation for this study was to educate regarding this matter the students may it be at home or in school. Specifically, schools organize seminars, parents are suggested to be examples and to teach their kids financial management at a young age and the local governments may include financial management as part of the current education system.

Mark Jay Lisay

There are no currently abstract for the research paper is not yet finished. Expect some error.

Cheneth Roque

International Journal of Advanced Research (IJAR)

IJAR Indexing

This qualitative study aimed to better understand the needs and challenges faced by SeniorHighSchool(SHS) Accountancy, Business, and Management(ABM) students in their journey of learning real life business problems. It utilizeda case study method in which the unit of analysis is the SHS Grade 11 and 12 ABM students of Simala National High School, a secondary public school in Cebu, Philippines. Analysis of transcribed interviews and observations from 20 key informants revealed the findings encapsulated in the following themes: a) ?Clamor for Work Immersion? which explains the need to expose the learners to hands on real world business activities; b) ?Demand for Learning Resources and Facilities? which entails the need for adequate print and non-print learner?s material, computers, and unlimited internet access; c) ?Request for Skilled and Experienced Teachers? which relates the need to hire teachers who are business graduates or have firsthand business experience; d) ?Time runs fast? which reveals the challenge of allocating enough time in all their projects particularly those involving data gathering outside school; and e) ?Funds empty fast? which shows the challenge of budgeting their allowances to be sufficient for their school requirement expenses. The school administration and teachers shall then address the needs through enhancing the work immersion plan, intensifying the school improvement plan, and hiring of qualified teachers. To cope with the challenges, the students need to develop time management skills and properly set priorities on where to allocate their available finances.

Ivan Vince Longa

IOSR Journals

Brain Research Bulletin

Sherwin Wilk

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Implementation and Outcomes of Fare-Free Transit Systems (2012)

Chapter: chapter two - literature review.

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

9 IntroductIon This chapter summarizes findings from a literature review related to the subject of fare-free public transit. A TRIS search was conducted to aid the review, using key phrases such as “free transit,” “fare-free public transit,” “no-fare transit,” and “free transit demonstration.” Internet searches applying the same terms were used to discover newspaper articles or other information that might be written by reporters or bloggers interested in this subject. A review was also conducted of any similar research listed in the TRB’s Research in Progress database. Finally, any white papers or agency reports identi- fied by project panel members or discovered through inter- views with managers of fare-free public transit systems were also reviewed. Fare-free public transit has been discussed and considered ever since the federal government became involved in pro- viding capital assistance to local public transit agencies in the 1960s (1, 2). The discussion continues to the present day through Internet blogs posted by passionate transit users and policy analysts who debate why, as a public service, transit is treated differently from other public services such as librar- ies and parks, and whether the charging of fares on transit is simply rooted in the origins of transit systems when they were private, for-profit companies (3). The purpose of this report is not to explore all sides of the debate regarding the philosophy of providing fare-free public transit. As the title of the report clearly states, it is intended to review the implementation and outcomes of fare-free public transit systems. Chapters three and four provide information received directly from representatives of the dozens of agen- cies currently providing fare-free service. However, there have also been reports produced over the years that provide valuable information and insights regarding the experiences of those who have either implemented, or considered imple- menting, fare-free transit (see Table 1). The primary concerns of those who consider implement- ing fare-free transit are: • Whether it is cost-effective to eliminate the fare collec- tion process, • The effect fare-free transit has on ridership and system capacity, and • The effect fare-free transit has on service quality and customer satisfaction. cost-EffEctIvEnEss of ElImInatIng thE farE collEctIon ProcEss Passionate advocates of fare-free public transit argue that the following costs associated with fare collection can exceed the amount of money actually collected (4): • Purchasing and maintaining fareboxes and automated ticket vending machines • Provision of secure money counting rooms, equipment, and cameras • Services to pick up and deposit money securely • Accounting and auditing expenses • Production/purchase of fare media such as passes and smart cards • Commissions to third-party vendors and the staff effort to work with them • On-board fare inspectors • Staff time involved with analyzing modifications to fares and the necessary public hearings • Lost time and productivity for bus trips as a result of having to collect and explain fares. Those advocates also believe that most transit managers do not really know what the total cost of collecting fares is at their agencies. That may or may not be true, but there is sufficient evidence that the cost of fare collection has been examined through research and by a number of agencies. A report reviewing transit systems in Washington State noted that the net cost or income of fare-free transit is an important aspect of a fare-free policy (5). By eliminating fares, the rev- enues collected are reduced to zero. The costs related to fare collection can also be eliminated, potentially cancelling out the loss of revenue. The Seattle bus tunnel and Island County Transit are provided as examples. In both cases the costs of fare collection were greater than or equal to the revenues collected, meaning there was no net income from collecting fares. The costs of fare collection vary widely among public transit agencies. TCRP Report 32 (6) documents that some agencies spend less than 1% of their total fare revenue col- lected on fare collection and related costs. The average for all agencies that responded to that report’s survey was 6.2%. For bus systems, the average was 3.4% for smaller systems and 4.0% for larger systems, although it could range from 0.5% to 22%. Based on 1990 operating statistics for Washington State systems, the gross farebox recovery ratio of most transit systems was below 10%, with only three having a recovery ratio higher than 20%. chapter two lItEraturE rEvIEW

10 In general, the smaller the system, the more likely the net revenue of collecting fares is closer to zero. Many of the small transit agencies that responded to the questionnaire for this TCRP project reported they did not do any formal analysis to determine the cost-benefit of charging a fare. For these small urban and rural systems, it was an easy decision to forego passenger fares owing to the minimal revenues they expected to receive versus the perceived costs associated with collecting fares. Small systems in resort areas respond- ing to this project’s survey indicated that it was imperative to their towns’ economic success to provide fare-free transit, even if fares could create net revenue for the system; that is, it was more important for the towns they serve to remain competitive with other resort communities by providing a convenient service to visitors and an affordable mobility option for relatively low-wage service employees. For some university-dominated towns, it was a perceived matter of equity to allow nonstudents to also board for free, particu- larly when fare-paying nonstudents might represent only a small percentage of all passengers. In the case of Chapel Hill Transit, the administrators of the University of North Carolina believed they were spending an inordinate amount of time with the paperwork involved with subsidizing passes for their students. A fare-free system pre-paid by students that provided them with universal access virtually eliminated all university administrative tasks other than writing a check a few times a year to Chapel Hill Transit. This agreement also negated the need for Chapel Hill Transit to purchase addi- tional equipment to read university ID cards. Although they did not do a specific cost-benefit analysis, they believed that foregoing farebox revenue would result in very low net costs because the additional funding they could receive from both state and federal formula grants would be increased as their ridership increased (C. Elfland, Associate Vice-Chancellor for Student Services, University of North Carolina, personal communication, Apr. 18, 2011). In 2008, in a study conducted by Lane Transit District (LTD) in Eugene, Oregon, staff determined that the cost of fare collection was between $100,000 and $500,000 per year Service Area Dates of Demonstration Population of Service Area Results Asheville, North Carolina 08/06–11/06 70,000 58.5% increase in ridership; some problem riders, schedule adherence suffered, retained an increase of 9% in ridership after demonstrations. Austin, Texas 10/89–12/90 500,000 Credited for ridership increases of 30%–75%; reports of disruptive teenagers and driver complaints. Increased operating costs, but successful in promoting ridership. Chelan–Douglas Counties, Washington 12/91–09/00 100,000 Ridership exceeded forecasts by a factor of 4. Policy ended when state funding source was eliminated by voters. Denver, Colorado (off-peak hours only) 02/78–01/79 1,500,000 Reported increases in ridership of 36% to 49%, although inconclusive because of changes in service made during experiment; decreased schedule reliability, crowding. Mercer County, New Jersey (off-peak hours only) 03/78–02/79 300,000 Ridership increases of 25%–30%; 45% of buses ran late, extra buses required, driver complaints, problem riders. Milton, Canada 06/07–12/07 54,000 Ridership increased 63%; some increased rowdiness among young passengers, but 99% of customers “satisfied” or “very satisfied.” Salt Lake City, Utah October 1979 910,000 13% increase in ridership. Topeka, Kansas May 1988 120,000 Ridership increased 86% and 6% increase in ridership was retained after demonstration. TABLE 1 RESULTS OF SySTEM-WIDE FARE-FREE PUBLIC TRANSIT ExPERIMENTS

11 (although it would appear closer to $100,000) compared with the $5 million in revenue that was collected (7). They found that no employees were dedicated solely to fare collection functions. These employees had several duties, and conse- quently, eliminating fares would not result in the elimination of jobs. For example, a customer service representative sells fare instruments, but also conducts trip planning for tele- phone callers and for walk-in customers. If the sales function were eliminated, those hours might be required to serve pas- sengers in the Customer Service Center, particularly if rider- ship increased as a result of free fares. This same conclusion was reached in reverse by Link Transit in Washington State when they converted from fare-free service to charging a fare in 2001. Link’s manager reported in a telephone interview that the agency was able to spread the responsibility for the fare collection process among many employees and that the cost to the agency was believed to be minimal (see the case study in chapter four of this synthesis). LTD’s fare collection system used very basic farebox tech- nology. The success LTD has had in transitioning passengers to pre-paid fare instruments has meant that cash fare custom- ers represent between only 20% and 30% of total ridership. The less cash that is handled, the lower the cost of the fare collection process, and the less delay there is in the boarding process. LTD empties fareboxes only three days a week. The staff report acknowledged that fare collection costs could be much higher at agencies that use more advanced collection technologies or use honor systems that require fare enforce- ment personnel. It also noted that the cost of fare collection at some small systems that might not receive much in fares could be a much higher percentage of overall revenue, mak- ing it more rational to establish fare-free policies. If LTD became fare-free, the report estimated it would lose between $4.5 and $4.9 million dollars in revenue, without an iden- tifiable alternative source of funds to replace that revenue. This would require a 20% reduction in service at the same time the agency would experience a substantial increase in demand. The report did not estimate the cost of increased service, because LTD had no identifiable funds to pay for it. The San Francisco Municipal Transportation Agency (Muni) utilized a consultant to conduct a detailed analysis of the cost-effectiveness of converting to a fare-free system in 2007–2008 at the request of Mayor Gavin Newsom (8). The study concluded that the costs of fare collection amounted to $8.4 million of the Fy 2006 Operations and Maintenance Budget. This represented 7.5% of the $111.9 million Muni collected in fares. There would be a reduction of 91 full-time employees, representing approximately 2% of the total staff if fares were discontinued. However, the study also examined the results of other free-fare experiments conducted in places such as Austin, Texas, and Denver, Colorado, and developed projections on what their additional costs would be based on three different scenarios of ridership increases. The most likely scenario—a 48% increase in ridership—suggested a probable $69 million increase in the annual operating bud- get would be required to handle the increased demand for capacity. When coupled with the foregone revenue previ- ously collected, the agency would need to find an additional $184 million dollars a year to operate the system. Making matters more challenging, the San Francisco Municipal Transportation Agency would have additional capital costs of $519 million to procure the vehicles, facilities, and infra- structure needed to accommodate the substantial increase in ridership. In 1999, Mayor Vera Katz of Portland, Oregon, requested that a group of citizens, assisted by Tri-Met staff, research the role that making the transit system free might play in helping to keep the area from strangling on auto traffic. At the time of the study, Tri-Met recovered approximately 20% of its operating expenses through fares. The report that sum- marized the financial impact of converting to a fare-free sys- tem noted that the agency would lose $41 million in fares, and need an additional $8 million for operating expenses and $5 million for capital expenses to accommodate the addi- tional passenger demand (9). In summary, an additional $54 million in revenue would be needed to replace foregone fares and handle new demand. Surprisingly, the report did not estimate how much the agency might save by eliminat- ing the cost of fare collection, although its estimate of total costs may have accounted for what savings the agency might realize. The group developing the study researched the pos- sibility of imposing a regional parking tax, but found there were a number of legal, institutional, and economic issues that would be difficult to overcome (see Table 2). Advance Transit in Hanover, New Hampshire, serv- ing small urban and rural areas, has been providing fare- free service since 2002 in the Upper Valley region of New Hampshire and Vermont. Respondents to this project’s sur- vey indicated that a number of transit systems that provide fare-free service are challenged from time to time to justify their continued use of the fare policy. In 2008, the CTAA produced a report that analyzed the cost-benefit of changing Advance Transit to a system that charged a fare (10). The capital costs to outfit their fleet of 33 buses with fareboxes would have amounted to $407,550 (which could be amor- tized over more than 20 years at approximately $20,000 per year). Other one-time costs such as the time to create the policy, hold public hearings, and inform the public about the change were estimated to be $33,900. The estimated cost for ongoing fare collection functions per year (not includ- ing amortization of the new fareboxes) was $53,354. These costs would be offset by the new fares collected. A $0.50 fare would generate an estimated $90,688 a year, whereas a $1.00 fare would produce annual revenue of $145,600, and a $2 fare would produce $175,550. Hence, fares collected would exceed the annual cost of collecting the fares, but only barely in one scenario. The highest estimate for revenue to be collected would represent only 4% of a total annual operating budget of $4.3 million. To date, Advance Transit remains a fare-free service.

12 In Summit County, Colorado, the general manager reported that recent cost-benefit analyses have been undertaken to deter- mine the feasibility of implementing a fare system. These have focused on the infrastructure costs of implementing the fare collection system including fareboxes, money counters, and retrofits to facilities to count and store money that was esti- mated to cost $1 million. The general manager provided an undocumented estimate that the annual ongoing costs would be approximately $225,000 to pay for four employees responsible for farebox maintenance, counting and account- ing for money, and providing security. This would represent 16% of the $1.4 million they estimate a $1.00 fare would gen- erate annually. The Aspen Transit Development Plan produced in 2009 reviewed what the financial impact of establishing a $1 fare would be (11). After careful consideration was given to the number of passengers who ride at a discount and the number of riders that would be lost as a result of the institution of a fare, it was estimated that a $1 fare would generate $447,300 annually. The report noted that there would be some new administrative costs, primarily as the result of the need for marketing and fare media production and distribution. It was also estimated that it would require only two hours per day of one person’s time to count and account for fares. All of these functions were to be absorbed by existing staff. The purchase of 16 fareboxes for its bus fleet was estimated to cost up to $144,000. However, the major cost concern was the effect collecting fares would have on buses’ ability to maintain route schedules. The report calculated the increased dwell time resulting from fare collection would accumulate to between two and four minutes per one-way trip. It was noted that an additional bus would need to be put into ser- vice on up to five routes to maintain the posted headways, or the buses would need to run less frequently. Because the cost to add even one extra bus a year to help routes maintain schedule would be $476,000, the report concluded that estab- lishing a fare would not be cost-effective if current levels of service were to be maintained. It was recommended that fares be established only as a last resort. Fare-free transit is also present in European cities and has been subject to scholarly investigation over many years. In an article written in 1973 entitled “Free Public Transport,” the authors look at the projected costs associated with fare- free transit for several German cities, noting that these costs would range from 22 million Deutschmarks (approximately $15 million) in the city of Kassel to 350 million in a city as large as Hamburg (12). The study took into account lost farebox revenue, remaining advertising revenue, increased capacity required during peak periods, savings from the elim- ination of fare collection, and savings from greater productiv- ity of buses as travel time improves owing to less congestion. The net costs were seen as substantial burdens to municipali- ties and the report casts doubt that the German government would be willing to fill the revenue gaps that fare-free transit would produce. In 2008, the Public Works Department of the city of Ham- ilton, a city of approximately 500,000 in Ontario, Canada, prepared a report for the Public Works Committee of the city addressing the potential of offering fare-free service or some Transit Agency and Year of Analysis Savings from Eliminating Fare Collection Functions Costs of Lost Revenue, New Service, and Additional Vehicles and Facilities Estimated Cost of Implementing Fare-Free Policy Lane Transit–Eugene, Oregon (2008) $100,000–$500,000 $5 million in lost fares $4.5–$5 million in net new costs per year Muni–San Francisco, California (2008) $8,400,000 $112 million in lost fares $72 million for increased service $512 million in capital expenses $184 million in net new operating expenses per year Tri-Met–Portland, Oregon (1998) (not provided, but possibly accounted for in costs column) $41 million in lost fares $8 million for increased service $5 million for additional vehicles $49 million in new operating expenses per year Hamilton, Canada (2008) (not provided, but possibly accounted for in costs column) $900,000 in lost fares $30 million for additional service $30.9 million in additional operating expenses per year TABLE 2 PROjECTED COSTS OF IMPLEMENTING A FARE-FREE POLICy

13 other forms of fare discounts (13). The report stated that, based on a conservative estimate of a 20% increase in rider- ship and the elimination of fares, the increase in its operating budget expenditure would be in the order of $30.9 million per year. This would require an additional tax per household of about $161 per year based on a residential assessment of $250,000 in 2008 dollars. In addition, a capital expenditure in the order of $5 to $10 million for fleet expansion and facil- ities accommodations would be required. EffEct farE-frEE PublIc transIt has on rIdErshIP and systEm caPacIty People may argue about the pros and cons of fare-free transit, but none of the literature reviewed for this project questions the fact that ridership will increase when fare-free policies are implemented. No matter what types of experiments, dem- onstrations, or permanent programs have been implemented, public transit systems have experienced significant increases in ridership when implementing fare-free policies. To estimate the ridership impact of changes in levels of public transit fares, including deep discount fare policies, many transit operators over the years have used the “Simpson– Curtin Rule” as the standard to measure the relationship between fares and ridership termed as “elasticity.” This rule estimates that a 10% fare increase will result in a 3% drop in ridership (denoted as -0.3). Conversely, a 100% decrease in fares (fare-free) would be expected to result in a ridership increase of 30% (13). TCRP Report 95: Traveler Response to Transportation System Changes noted that limited data, including some of which are contradictory, suggest that the ridership responses to fare decreases do not differ significantly from rider responses to fare increases. A review of 23 fare changes in United States cities, selected for similar size, found that the fare elasticities were almost identical for fare increases and fare decreases (14). Dargay and Hanly (1999) studied the effects of U.K. transit bus fare changes over several years using sophisticated statistical techniques to derive elasticity values. They found that demand is slightly more sensitive to rising fares (-0.4 in the short run and -0.7 in the long run) than falling fares (-0.3 in the short run and -0.6 in the long run), and tends to be more price sensitive at higher fare levels (15). In 1991, APTA staff produced a report to verify the accu- racy of the Simpson–Curtin elasticity equation (16). An advanced econometrics model was used to review the results of fare increases and decreases at 52 transit agencies, examin- ing the ridership performance 24 months before a fare change and 24 months after a fare change. The model attempted to isolate the impacts of the fare change from other factors such as employment trends, fuel costs, and labor strikes. APTA’s study showed that transit riders react more severely to changes in fares than the Simpson–Curtin rule would predict, and that their reaction varies depending on the size of cities and time of day the fare change is applied. The fare elastic- ity was found to be -0.36 for systems in urbanized areas of more than one million population, whereas it was -0.43 in urbanized areas with less than one million population, indi- cating that travelers in large cities are less sensitive to fare increases. Further, the average peak hour elasticity was found to be -0.23, whereas the off-peak elasticity was -0.42, indi- cating that peak hour commuters are much less responsive to fare changes than transit travelers during off-peak hours (16). These elasticities can vary significantly depending on local circumstances such as income, driving conditions, level of transit service, and the location of work places in relation to the population. Hence, it should not be a surprise that public transit agencies that offer fare-free service might experience a wide range of ridership increases. However, these analyses still do not fully account for increases experienced by fare-free transit systems that go well beyond these elasticity estimates, such as the 58% increase in Asheville, North Carolina (17), the 86% increase in Topeka, Kansas (18), or the 200% increase reported by the island of Hawaii in response to this project’s survey. An intriguing possible explanation is offered by Hodge et al. (5). In their 1994 report, they postulate that standard elasticity formulas might not apply in the same way when fare-free policies are implemented. They note that there is not just a financial cost associated with transit fares, but a psychological cost associated with the farebox. The removal of the farebox can eliminate a barrier in the minds of potential passengers, many of whom might see the farebox as a source of con- fusion and possible embarrassment. The limited capabili- ties of most fareboxes to accept common forms of payment such as credit cards and/or the requirement to have exact fare can certainly discourage passengers. The report prepared for Portland provides a wonderful hypothetical analogy: “The problem with fares is simple: imagine the result if people had to put $1.40—exact change please—in a farebox in their car each time they wanted to take a trip” (9). The first experiments in fare-free transit were conducted in the late 1970s in Mercer County (Trenton), New jersey, and in Denver, Colorado. These demonstration projects were funded in part by the Urban Mass Transportation Adminis- tration. They were instituted to be in effect only during the off-peak hours between 10 a.m. and 2 p.m. and after 6 p.m. and all weekend because of unused capacity and the thought that marginal costs would be minimal. Peak period fares remained the same. The Denver experiment was more dif- ficult to analyze because the transit agency also implemented major route restructuring during the experiment, had insuffi- cient pre-demonstration data, and changed the off-peak hours during the experiment. The experiment in Mercer County led to a significant increase in ridership during the off-peak peri- ods, with a 25% to 30% increase attributed to the removal of the fare. In all, the fare-free demonstration attracted approxi- mately 2,000 new riders per day. Sixty-nine percent of the new trips were previously made by another mode—half by

14 automobile and one-third by walking. It was estimated that the fare-free off-peak transit service reduced Trenton’s typi- cal weekly 21 million vehicle-miles traveled by 30,000 miles per week (19). The Topeka Metropolitan Transit Authority instituted free fares for one month on the bus system serving Topeka, Kan- sas, during May 1988. Compared with May 1987, ridership increased 83.2% on weekdays, 153.4% on Saturdays, and 93.3% overall. Ridership increased 156% on the downtown circulator route. Only one bus a day was added to address problems of overcrowding, indicating that smaller systems carrying lighter loads of passengers can accommodate rather large increases in ridership without needing to provide addi- tional capacity (18). The next substantial experiment in fare-free transit was implemented in Austin, Texas, and conducted from Octo- ber 1989 to December 1990. This experiment was not lim- ited to off-peak hours. The entire system became fare-free every hour and every day of the week. Ridership exploded, increasing 75% during the demonstration period, although some increased service might have also contributed to a por- tion of that increase. This experiment was not funded by the federal government, and no formal report that provides in- depth analysis is available. However, staff from that time reported that additional equipment was required to carry the heavier loads. Even with additional buses placed into service to help accommodate the new demand, the average cost per rider decreased from $2.51 prior to the fare-free experiment to $1.51 during the 15 months of the experiment. That the average cost per rider rose to only $2.18 in the year after the fare-free program was terminated indicates that some of the new passengers gained during the experiment con- tinued to ride once it concluded (20). Templin, a health resort town located in Brandenburg, Germany, with approximately 14,000 inhabitants, modified their small bus service to be fare-free on December 15, 1997. Since then public transportation has been free for everybody. The declared goal of the fare policy was to reduce auto- mobile usage and its collateral effects such as noise, pollu- tion, and the risk of accidents. Within a year after the transit scheme’s introduction, transit ridership had increased by almost 750%—from 41,360 to 350,000 passengers per year. Two years later, in 2000, ridership was above 512,000—more than 12 times its original amount. The study documenting this fare-free program did not include information on how many more buses were required to carry this substantial increase in ridership. It was more interested in determining the effec- tiveness of the policy’s ability to reduce auto trips. A study carried out on behalf of the Federal Ministry of Transporta- tion investigated transit ridership before and after the fare- free program by surveying passengers (21). The study found that the vast majority of new transit riders were children and adolescents. When asked what means of transportation would be replaced, most people answered they would substitute pub- lic transportation for nonmotorized travel. The study found that 35% to 50% of transit passengers would walk less, 30% to 40% would replace bicycle rides, and 10% to 20% would reduce automobile trips. However, it was unclear whether this referred to the driver or the passenger (22). Perhaps the most astonishing example of successful fare- free transit was implemented in Hasselt, Belgium. In 1997, this financially challenged and car-choked city of 70,000 determined it would completely modify its approach to trans- portation (23). Working on the assumption that you will not get people out of their cars without providing a comprehen- sive public transport system alternative, Hasselt transformed its two-line bus service to a nine-line service, covering every district in the city; and committed to half-hourly service dur- ing the day and a night bus that served every stop in the city. On day one—july 1, 1997—the numbers of passengers rose from the usual 1,000 to 7,832. Ridership increased more than 1,200% by 2001. A ring road near the inner city was con- verted to a pedestrian corridor, and parking in the inner city was restricted. Big car parks were banished to the edge of town, and parking priority within town was given over to resi- dents and the elderly. Parking was allowed for a maximum of one hour. The maximum speed in town was reduced to 30 km. Clearly, more equipment was needed and provided for this major modification to the transportation system of Hasselt. The council was in deep debt in the mid-90s and the radical approach was partly prompted because it could not afford a new ring road. Improving the bus service and making it free was less expensive. In 1996, there were only three bus routes with approximately 18,000 service hours/year. By 2003, the city expanded service to offer 11 routes with more than 95,000 service hours/year. Service frequency now ranges from 5 to 30 minutes throughout the city (see Table 3). Clearly, Hasselt anticipated the need for considerably more transit service with the implementation of free fares and a desire to totally modify its transportation services. The transit system in Hasselt cost local taxpayers approximately $1.9 million in 2006, amounting to 1% of its municipal bud- get and making up about 26% of the total operating cost of the public transit system. Fortunately for Hasselt, the Flem- ish national government covered the rest (approximately $5.4 million) under a long-term agreement (24). Asheville, North Carolina, conducted a totally unrestricted fare-free promotion for three months in 2006. Ridership increased by approximately 60% during the promotion. In spite of the significant increase in ridership, insufficient capacity was not cited as a major problem. However, based on surveys, existing customers were not happy with the crowded buses; that issue represented 21% of all complaints by the 45th day (25). The city of Milton, Canada, near Toronto, was the first municipality in Canada to provide fare-free service for an extended period of time. In 2007, public transit was made

15 free to all users during the midday off-peak time (9:00 a.m. to 3:00 p.m.) from june through December. Ridership increased an average of 63% over the seven-month period. The report did not include information on additional costs or equip- ment needed. Two private companies agreed to pay for lost revenue and additional costs; therefore, the main focus of analysis was on effects on ridership. On-board surveys were conducted during the demonstration and found that of the 80% of riders who used the bus at least two times per week during the fare-free demonstration, 86% would continue to use it as often even after fares were reintroduced. However, only 33% of senior riders indicated that they would continue to use the service as frequently after fares were reintroduced, suggesting that seniors are generally more sensitive to cost increases (26). As noted earlier, in 2008, the city of Hamilton reviewed the potential impacts of providing fare-free transit in the ninth-largest city in Canada. Although the report noted there was no Canadian system-wide experience to draw from, it estimated that ridership increases would conservatively reach 20%, but might reach as high as 50% depending largely on the level of congestion and parking policies adopted (13). This same report included an appendix of a case study of Cha- pel Hill, North Carolina, that included a memorandum pre- pared by the town manager of Chapel Hill in October 2002. In january 2002, Chapel Hill Transit finalized agreements with local universities and townships to offer fare-free pub- lic transit service to all passengers in their service area. The town manager’s report noted that ridership on the fixed-route services had increased by 43% from january 2002 through September 2002. Although the city manager’s report also noted that service hours were increased 11%, the primary reason for the increase in ridership was clearly the fare-free policy (27). One of the most recent instances of implementing fare- free public transit has been in the city of Changning, China, a municipality of approximately 53,000 people located in the central portion of the country. In july 2008, the city began providing fare-free service on the three routes serving the city. Based on information in a paper submitted to TRB in 2010, ridership increased from 11,400 a day to 59,600 per day, representing an increase of almost 550% in less than two years. It was not completely clear from the paper if any service hours were added to handle the additional demand, although it appears likely that it would have been reported if more service hours or buses were added. The paper indicated that an additional 7 million yuan (approximately $1 mil lion dollars) was spent on the program, presumably to replace fares previously paid by passengers (28). Apparently it is the only fare-free public transportation offered in China, and various observers question whether it is something the city can finan- cially sustain given so many other priorities, including health care, education, and housing (29). For the time being, the economy and the public appear to support the fare-free service in this city, small by China’s standards; however, observers believe the concept would not be so feasible in larger cities in the country. EffEct farE-frEE PublIc transIt has on sErvIcE QualIty and customEr satIsfactIon As noted earlier, fare-free transit will attract more passengers to a public transit system. In some experiments, the increased number of passengers not only tested the capacity of the buses, but also the ability of the buses to stay on schedule. When fare-free transit is introduced, the time for each individ- ual passenger to board is reduced, because they do not have to take the time to pay a fare. On average, taking into account that some passengers pay with cash and others with some form of pass, it takes a passenger between 3.0 and 3.5 sec- onds to pay their fare when they board (30, 31). In addition, it is possible that passengers who do not pay a fee can board through all doors, saving additional time. However, because fare-free transit will attract many more passengers, the bus is likely to make more stops than it would if fares were charged. The time a bus takes to decelerate to enter more bus stops and accelerate to regain cruising speed can eliminate any sav- ings from reduced dwell time gained from the elimination of collecting fares (32). Schedule adherence is subject to being negatively affected by a significant number of people riding the bus a short distance who might have otherwise walked Location and Population Description of Program Effect on Ridership Templin, Germany (14,000) Small transit service in health resort town Increase from 41,360 passengers per year to 512,000 per year in two years Hasselt, Belgium (70,000) Total change in transportation policies restricting cars and increasing transit Increase from 1,000 per day to 13,000 per day within four years Changning, China (53,000) Eliminated fares without adding service Increase from 11,400 per day to 59,600 per day within two years TABLE 3 RIDERSHIP RESULTS OF TOTALLy FARE-FREE PROGRAMS OUTSIDE NORTH AMERICA

16 (33). In the fare-free demonstration conducted in Trenton, New jersey, between 5% and 15% more buses entering the downtown area were found to be overcrowded during the time fare-free service was provided. In addition, the number of buses running behind schedule increased to 45% (19). Dur- ing the fare-free experiment in Asheville, North Carolina, the major complaint of riders was poor reliability. Travel time was estimated to have increased by several minutes per hour because of the increased number of stops and longer dwell times associated with the 58.5% increase in ridership (25). It would appear that the potential negative impacts of fare- free transit on schedule adherence could be mitigated to a degree without degrading service frequency or adding to costs by a judicious reduction in the number of bus stops (32). Con- versely, it can be noted that those transit agencies in resort and university-dominated communities that responded to this project’s survey indicated that there would be no way for them to keep their schedules without a fare-free system. Transit agencies in these communities often have bus stops with substantial numbers of passengers boarding, and the boarding process would take much longer if each passenger had to pay a fare or show a pass. Fare-free transit will please many passengers and frustrate others. In Asheville, several reported that some younger peo- ple refused to give up seats for more elderly customers. There was an initial drop in handicapped utilization. A few women reported being uncomfortable with what was described as a rougher than normal customer group; however, no reports of any actual physical abuse were made concerning these fears (25). Because ridership escalates when a fare-free policy is implemented is the clearest indication that passengers, as consumers, appreciate the reduced costs. The seven-month experiment conducted in Milton, Canada, included a survey of passengers that indicated that 99% of all respondents were either “satisfied” or “very satisfied” with the program. The NSI Research Group found that 75% of transit users had a favorable or very favorable reaction to the elimination of fares during the Austin experiment (34). However, that same experiment also was subject to complaints by the system’s bus operators who complained vehemently about excessive rowdi- ness among younger passengers and what they believed were conditions that jeopardized their safety and that of their pas- sengers (20). Similar concerns indicating a decline in morale were expressed by bus operators during the Denver and Tren- ton demonstrations (35). It can be noted that many respon- dents to the survey for this project stated that they believed their bus operators viewed fare-free transit very favorably, and would gladly trade the need to deal with a few more undesirable passengers for being relieved of the duty of col- lecting fares with the attendant fare disputes. In short, fare-free policies have the potential to either improve or detract from the quality of service. As a report on fare-free public transit systems prepared for the Washington State DOT concluded, smaller communities are more likely to encounter fewer problems and more success, as are tran- sit agencies and communities that are committed to the con- cept owing to concerns over the environmental impacts of transportation or social equity (5). The authors of that report noted the importance of instituting education programs to deal with middle and high school students in particular. They also noted that although some larger communities such as Austin might have found it overwhelming to deal with younger students (the former general manager noted how school buses would ride empty while students chose to ride the public buses) (A. Kouneski, General Manager, Austin Transit System, personal communication, june 28, 2011), other communities such as Logan, Utah, and Whidbey Island saw serving youth as one of the agencies’ primary missions. Fare-free public transit relieved parents of the responsibility of serving as chauffeurs, and allowed students to access the many resources in their communities (5). Based on the results of the survey for this project, there are no communities larger than 175,000 residents in the United States that provide fare-free public transit throughout their entire system, nor were any others found in the rest of the world. The primary reasons appear to be the difficulty in find- ing funds to replace the revenue they would lose through the farebox and the additional expenses they would incur in maintaining service quality for greater demand. The literature search has also shown that commuters in private vehicles are not attracted in large numbers to fare-free public transit. Absent other types of transit-supportive policies such as restricting parking, the vast majority of commuters will con- tinue to prefer driving. Hence, without disincentives to using private vehicles, minimal gains toward the goals of reducing congestion and air pollution would usually be expected. However, there are dozens of smaller communities through- out the nation that have implemented fare-free public transit. They are identified in the next chapter, along with the reasons why they have found fare-free public transit to be a posi- tive service in their communities. Other communities such as State College, Pennsylvania, with a regional population of approximately 80,000 in an area dominated by Pennsylvania State University, have hired consultants to review the feasi- bility of establishing a fare-free system for its entire service area (36). The city of Longmont, Colorado, a community of approximately 90,000 people outside of Denver, has made application to the Denver Regional Council of Governments’ Congestion Management Air Quality Regional TDM funding pool in the amount of $300,000 for a two-year fare-free tran- sit demonstration project. Funds would be used to plan for the demonstration, prepare ordinances to deal with disruptive passengers, market the program, and pay the Regional Tran- sit District as a replacement for fares that would have been collected at the farebox (S. McCarey, Alternative Transpor- tation Coordinator, Boulder County Transportation, personal communication, june 23, 2011).

17 The general manager of the Duluth Transit Authority in Duluth, Minnesota, a community with a regional popula- tion of approximately 280,000 on the western most point of Lake Superior, has also indicated that it is strongly con- sidering a fare-free system following review of the total cost of the fare collection process against the amount of revenue being received. Cash fares have become a smaller part of their revenues because of a prepaid program with the University of Minnesota–Duluth (D. jensen, General Manager, Duluth Transit Authority, personal communica- tion, Apr. 20, 2011). Should Duluth proceed with a fare-free system, it would become the largest community, in terms of population, to have such a policy in place. The Corvallis Transit System in Oregon (one of the case studies in chapter four) was con- verted to a fare-free transit agency in February 2011 (37). A bibliography summarizing many of the reports noted in this literature search is included as Appendix C, and the reader is invited to read them for additional details on fare- free experiments and those agencies that analyzed the feasi- bility of establishing fare-free public transit.

TRB’s Transit Cooperative Research Program (TCRP) Synthesis 101: Implementation and Outcomes of Fare-Free Transit Systems highlights the experiences of public transit agencies that have planned, implemented, and operated fare-free transit systems.

The report focuses on public transit agencies that are either direct recipients or subrecipients of federal transit grants and that furnish fare-free services to everyone in a service area on every mode provided.

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ATLANTA TRANSIT PRICING STUDY: MODERATING IMPACT OF FARE INCREASES ON POOR.

Research output : Contribution to journal › Article › peer-review

Alternative methods for moderating the impact of fare increases on low-income groups in Atlanta are described and evaluated. The study, sponsored by the Transportation Systems Center under the Service and Methods Demonstration Program, considers five alternatives to a flat fare increase: direct user subsidies, quality-based fares, reduced fares on designated routes, peak/off-peak fare differentials, and distance-based fares. We evaluate these fare strategies according to a set of standardized criteria that considers the target efficiency, coverage of the target group, administrative cost, total cost, and degree of relief offered by each option. The study finds that a direct user subsidy provides the highest degree of relief to low-income patrons with the lowest revenue loss. This is because user subsidies are more efficient in reaching the target population and offer a higher level of coverage of the poor than do other alternatives.

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• Civil and Structural Engineering
• Mechanical Engineering

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• Demonstrations Engineering & Materials Science 100%
• Costs Engineering & Materials Science 93%

T1 - ATLANTA TRANSIT PRICING STUDY

T2 - MODERATING IMPACT OF FARE INCREASES ON POOR.

AU - Lovely, Mary E.

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AB - Alternative methods for moderating the impact of fare increases on low-income groups in Atlanta are described and evaluated. The study, sponsored by the Transportation Systems Center under the Service and Methods Demonstration Program, considers five alternatives to a flat fare increase: direct user subsidies, quality-based fares, reduced fares on designated routes, peak/off-peak fare differentials, and distance-based fares. We evaluate these fare strategies according to a set of standardized criteria that considers the target efficiency, coverage of the target group, administrative cost, total cost, and degree of relief offered by each option. The study finds that a direct user subsidy provides the highest degree of relief to low-income patrons with the lowest revenue loss. This is because user subsidies are more efficient in reaching the target population and offer a higher level of coverage of the poor than do other alternatives.

UR - http://www.scopus.com/inward/record.url?scp=0020224885&partnerID=8YFLogxK

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Research on Time-Based Fare Discount Strategy for Urban Rail Transit Peak Congestion

• ORIGINAL RESEARCH PAPERS
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• Published: 17 October 2023
• Volume 9 , pages 352–367, ( 2023 )

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• Xiaobing Ding 1 ,
• Chen Hong   ORCID: orcid.org/0009-0009-5076-0351 1 ,
• Jinlong Wu 2 , 3 ,
• Lu Zhao 4 ,
• Gan Shi 1 ,
• Zhigang Liu 1 ,
• Haoyang Hong 1 &
• Zhengyuan Zhao 1  

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To alleviate peak-hour congestion in urban rail transit, this study proposes a new off-peak fare discount strategy to incentivize passengers to shift their departure time from peak to off-peak hours. Firstly, a questionnaire survey of Shanghai metro passengers is conducted to analyze their willingness to change departure time under different fare strategies. Secondly, based on the survey results, a time-differentiated fare discount model is constructed, considering both the company’s revenue and passengers’ travel benefits, and with the optimization objective of achieving balanced peak-hour and off-peak-hour train loads throughout the day. Subsequently, a genetic algorithm with nested fmincon functions is designed and combined with the actual data of Shanghai rail transit line 9 for arithmetic analysis. Finally, the effectiveness of the model is validated using the survey data. The research results show that the off-peak fare discount strategy can incentivize 6.88% of passengers traveling in the morning peak and 6.66% of passengers traveling in the evening peak to shift to off-peak travel. This research provides theoretical support and decision-making guidance for implementing time-differentiated pricing in urban rail transit systems.

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1 Introduction

As the transportation lifeline of major cities, urban rail transit networks bear the primary commuter flow. The average daily passenger flow of urban public transportation in the city reached 13.988 million in 2021, with rail transit accounting for 70.0% of the total, carrying an average of 9.786 million passengers per day, representing a year-on-year growth of 26.5% [ 1 ]. With the accelerated progress of urban rail transit construction, the passenger flow of urban rail transit has experienced explosive growth [ 2 ]. The sharp increase in passenger flow has exacerbated the issues of overcrowded trains and congested stations during peak hours. This not only diminishes passenger comfort during travel but also poses challenges in passenger flow management [ 3 ]. Therefore, alleviating the increasingly serious problem of passenger congestion during peak periods is imperative.

Through in-depth study of passenger flow data, it can be seen that the time distribution of passenger flow in rail transit exhibits significant temporal imbalance [ 4 ]. The most fundamental approach to alleviate peak period congestion is to balance passenger demand. Price regulation serves as an effective method for scientifically managing passenger flow demand.

This paper studies the willingness of passengers to change their travel times under different fare strategies and establishes an effective time-differentiated fare model. It aims to incentivize passengers traveling during peak periods to shift to off-peak periods, which fundamentally solves the peak period congestion problem and achieves a balanced full load rate throughout the day. Furthermore, the effectiveness of the model is validated through a case study using Shanghai Line 9, coupled with a questionnaire survey data.

The significance of this study is its investigation of passengers’ shifting of travel time behavior under different fare strategies and the use of Shanghai Line 9 as a case study. The constructed fare discount model can provide theoretical support and quantitative support for the implementation of time-differentiated pricing for urban rail transit.

The rest of the paper is organized as follows. Section  2  reviews related literature. Section 3 presents an overview of the temporal distribution patterns of urban rail transit passenger flows. Section 4 presents the questionnaire design and survey results. Section 5 constructs a time-differentiated fare discount model. Section 6 provides an example analysis using a genetic algorithm with nested fmincon functions combined with real data. In Sect. 7 , the effectiveness of the model is validated based on the data collected from the questionnaire survey. Finally, Sect. 8  concludes the study.

2 Literature Review

Urban rail transit peak congestion has become a public transportation problem in major cities. Measures to alleviate peak congestion can be broadly categorized into two types: traffic supply management and traffic demand management [ 5 ]. As it becomes increasingly difficult to further increase capacity supply, domestic and foreign experts have begun to study effective measures to relieve peak congestion from the perspective of passenger demand. The most widely applied domestic and international demand management measures mainly include passenger flow control and time-differentiated pricing [ 6 ]. Daniels and Mulley [ 7 ] found that using the University of Sydney as the subject of the study, even if the university and government initiatives which would reduce peak use and encourage peak spreading, they may not achieve either a reduction in peak use or a spread of public transport demand to other times of the day. Passenger flow control measures are currently the primary approach used in China to address peak congestion, such as increasing waiting fences outside stations and temporarily closing some entrance gates [ 8 ]. These flow control measures indirectly alleviate peak-hour passenger congestion by extending passengers' boarding and walking times, which cannot fundamentally solve the problem, but increases the time that passengers wait in line to enter the station and greatly reduces passenger satisfaction.

Therefore, national and international experts have begun to study the management of peak rail congestion through time-differentiated pricing. Jaradiaz [ 9 ] first applied the theory of time-differentiated pricing to subway systems in 1986. By establishing a non-aggregate demand model and using a multi-output cost function, the optimal prices were calculated and the applicability of time-differentiated pricing in the Santiago metro system was demonstrated. Currie and Graham [ 10 ] analyzed data from Melbourne after the implementation of a time-differentiated fare policy and found that time-based pricing had a moderating effect on travelers’ choice of travel behavior. Liu and Charles [ 11 ] reviewed many studies on fare schemes and found that it is possible to shift passenger demand out of the peak period as long as the difference in fares between the off-peak periods and peak periods is significant. Building on successful experiences in foreign cities and combining the fare pricing model in China, Song [ 12 ] validated the effectiveness of time-differentiated pricing using the Hangzhou metro, addressing the issue of uneven spatial and temporal distribution of passenger flow. Yu and others [ 13 ] further analyzed the rail transit passenger departure time elasticity under time-differentiated fares to provide key parameters for differentiated fare programming. Wang [ 14 ], Liu [ 15 ], Zahra [ 16 ], and others have also confirmed through further research that time-differentiated pricing effectively alleviates peak congestion. However, these studies only verified the effectiveness of time-differentiated fares without proposing specific fare schemes.

As a result, Tian and others [ 17 ] conducted a stated preference survey on passengers in Shanghai and proposed fare schemes such as peak-hour price increases and off-peak price reductions. However, they did not analyze the impact of different fare schemes on passenger volumes. Zhang and others [ 18 ] proposed two fare incentive schemes based on a questionnaire survey of the Beijing subway system and examined the effects of incentives on commuters’ travel behavior. The study found that incentive measures had a positive effect on commuters’ avoidance of traveling during the morning peak period. Wang and others [ 19 ] explored the impact of fare increases in three different scenarios (high, medium, and low) on passenger volumes, and the research indicated that raising fares could alleviate rail transit train congestion but may also lead to passenger loss. Tang and others [ 20 ] provide diverse options for commuters with a fare-reward scheme (H-FRS) and a non-rewarding uniform fare scheme (H-UFS). Commuters have the opportunity to join either scheme according to their flexibility in scheduling decisions. Zou and others [ 21 ] further explored the impact of pre-peak discounts on passengers' travel times and pointed out significant differences in the effects of fare discounts on different passenger groups. Therefore, when formulating fare schemes, Liu [ 22 ] combined rail transit passenger fare elastic demand and developed an optimal fare scheme for certain lines in Beijing, with an optimal fare of 4.72 yuan during off-peak hours and 6.41 yuan during peak hours. The above studies have proposed many time-differentiated fare schemes and explored the effects of various fare schemes on passenger flow. However, qualitative analysis is the main focus, and the few quantitative analyses have only investigated fixed fare schemes during peak and flat periods, and few mathematical models have been used to solve dynamic fare schemes that satisfy multi-objective optimization.

In recent years, dual-level programming models have been commonly used in urban rail transit pricing models. Zhang [ 23 ], Ran [ 6 ], Dai [ 24 ] and others have developed dual-level programming models considering both the profit of the rail transit operator and the travel cost of passengers to obtain optimal fare-setting strategies that balance the interests of both parties. However, the impact of fares on passenger travel choices was not considered. Zhou and others [ 25 ] used a discrete choice model to examine the relationship between fare incentives and passenger choice behavior. Based on passenger travel characteristics, Ji [ 26 ] further analyzed passenger travel mode choices using a discrete choice model and established a dual-level programming model considering the operator’s interest at the upper level and the passenger travel cost at the lower level. Although the aforementioned studies solve time-differentiated schemes that satisfy the interests of both the operator and passengers by constructing dual-level programming models, none of them quantifies the passenger flow factors and incorporates them into the fare pricing models.

In summary, many domestic and international scholars have conducted extensive research on regulating urban rail transit peak congestion based on differentiated fares. However, most studies have focused on qualitative analysis of various time-differentiated fare schemes. The small proportion of quantitative analysis has mainly concentrated on constructing dual-level programming models that consider operator benefits and passenger travel costs. There is a lack of research that quantifies the impact of fares on changes in passenger demand and considers passenger flow equilibrium parameters in time-differentiated fare models. Therefore, this paper introduces the user response load model from the power system to represent the impact of fares on passenger demand changes, incorporating the balance of train occupancy rates in different time periods as an objective function in the fare optimization scheme, and constructing a fare optimization model that integrates operator benefits, passenger travel costs, and passenger flow equilibrium.

3 Classification of Operating Hours of Urban Rail Transit

The distribution of passenger flow in urban rail transit over time is uneven due to a variety of factors. The daily time distribution of passenger flow in urban rail transit is influenced primarily by factors such as commuting time demand and travel purpose. This distribution can be categorized into four patterns: double-peak, full-peak, sudden-peak, and no-peak.

By the end of 2022, Shanghai had 20 rail transit lines, totaling 407 rail transit stations and covering a total operating mileage of 825 kilometers. The annual passenger volume in 2022 reached a high of 2,279,261,000 person-times, with an annual inbound volume of 1,254,673,000.

This paper focuses on the Shanghai rail transit system as the research subject and utilizes passenger flow data for weekdays from April 12 (Monday) to April 16 (Friday) of 2021 to analyze the distribution pattern of daily passenger flow. The original automated fare collection (AFC) data sheet contains information including passenger ID, travel date, travel time, in and out stations, travel method, travel cost, and fare discount. Firstly, SQL Server is used to clean the original data and eliminate invalid data such as missing information and redundancy. Then, the operating hours of the day (from 5:00 am to 12:00 pm) are segmented into hourly intervals, and the passenger flow in and out of the station within each hour is counted, as shown in Fig. 1 .

Weekday hourly passenger flow distribution

From Fig. 1 , it is apparent that the passenger flow exhibits a bimodal pattern on working days, with distinct and relatively fixed morning and evening peak periods. The morning peak hour is defined as 7:00 am to 10:00 am, and the average daily passenger flow in and out of the stations during this period is calculated to be 342,053. The evening peak hour is defined as 5:00 pm to 8:00 pm, and the daily average passenger flow in and out of the station during the evening peak is 289,276. These two peak periods account for 32.71% and 27.66% of the total number of passengers in and out of the station during the whole day, respectively, amounting to a total of 60.37%. It is evident that there exists a significant imbalance in the time distribution of urban rail transit passenger flow on working days, leading to difficulties in organizing train operations, issues in managing passenger flow at stations during peak periods, and inadequate utilization of resources during off-peak periods.

In this paper, a time-differentiated fare discount system is constructed with the aim of achieving a balanced train occupancy rate across different time periods. According to the above passenger flow distribution characteristics, Shanghai subway operations are divided into five periods: (1) 5:00–7:00 am, the off-peak period before the morning peak; (2) 7:00–10:00 am, the morning peak; (3) 10:00 am–5:00 pm, the off-peak period between the morning and evening peaks; (4) 5:00–8:00 pm, the evening peak; (5) 8:00–12:00 pm, the off-peak period after the evening peak.

4 Analysis of Passenger Classification and Transfer Intention Based on a Questionnaire Survey

The main objective of the stated preference survey is to design hypothetical scenarios and choice sets for passengers to evaluate and make selections from, in order to analyze their preferences for different options. Fare is a significant factor influencing passengers’ travel behavior, as the fare level can impact passengers’ decision-making regarding travel choices. Passengers with different travel characteristics may have varying levels of acceptance toward fare levels. Therefore, prior to constructing a time-differentiated fare model, it is essential to analyze the travel behavior of different passenger categories under various time-differentiated fare strategies. Due to the unavailability of actual passenger flow data under different time-differentiated fare strategies, this paper adopts a stated preference questionnaire survey to gather the required data. Through the questionnaire survey, it is possible to obtain information regarding passengers’ level of acceptance towards different time-differentiated fare strategies, as well as their willingness to shift their travel behavior accordingly.

4.1 Questionnaire Design and Survey

The survey questionnaire consists of two main parts: passenger travel characteristics and passenger travel choice behavior under different fare strategies. The section on passenger travel characteristics includes the following information: frequency of subway usage per week, the number of round trips taken on the subway per week, the average cost per subway trip, the main reasons for using the subway, sensitivity towards different factors influencing travel choices, and the most common travel times during weekdays. The section on passenger travel choice behavior under different fare strategies covers the following aspects: different fare strategies, including off-peak discounts, peak-hour surcharges, and combination fares, and available travel choices for passengers include advancing to pre-peak hours, delaying to post-peak hours, and maintaining current travel behavior.

The survey was conducted at railway transit stations, specifically targeting passengers who choose to travel by rail transit. A total of 224 valid questionnaires were collected for this survey, and statistical analysis was conducted on these valid questionnaires pertaining to passenger intentions.

4.2 Analysis of Transfer Intentions Among Different Types Of Passengers

4.2.1 analysis of passenger category characteristics.

Using the average number of days of subway usage per week and the most common travel time on weekdays as classification indicators, a second-order clustering method applicable to both continuous and categorical variables is employed to partition the passengers. Second-order clustering of passengers was performed using SPSS software, and an optimal number of three clusters was determined according to the Bayesian information criterion (BIC), with type 1 passengers accounting for 15.2%, type 2 passengers accounting for 51%, and type 3 passengers accounting for 33.8%. Some of the classification data are shown in Table 1 .

Based on the analysis of travel characteristics for different types of passengers using the available travel characteristic data, the following observations can be made:

Type 1 passengers have an average of 1.87 days of subway travel per week, which is lower than the other passengers. Additionally, these passengers have a common travel time during weekdays that falls within the off-peak period. This indicates that type 1 passengers are likely to be students or individuals who are not employed, as their travel patterns are not influenced by peak commuting hours.

Type 2 passengers have an average of 4 days of subway travel per week, which is significantly higher than the other passengers. They can be considered frequent subway passengers. Furthermore, these passengers have a common travel time during weekdays that falls within the peak period, and it tends to be relatively consistent. This suggests that type 2 passengers are predominantly white-collar workers or individuals employed in public institutions, who have fixed commuting times.

Type 3 passengers have an average of 2.39 days of subway travel per week. Their most common travel time during weekdays is primarily within the peak period. However, compared to type 2 passengers, type 3 passengers exhibit more flexibility in their travel times. They also have a higher frequency of subway usage than type 1 passengers. This indicates that type 3 passengers consist mainly of self-employed individuals or those with flexible work schedules who may have the freedom to adjust their travel times within the peak period.

4.2.2 Analysis of Travel Transfer Intention Based on Passenger Classification

Based on the classification results of the passengers in Sect. 4.2.1 , we will analyze the travel choice behavior characteristics of the three types of passengers under different fare strategies. The questionnaire included a total of eight scenarios, where passengers were presented with three fare strategies to choose from, along with an option to maintain their original travel time regardless of the fare strategy. The specific scenario settings and fare strategies can be found in Table 2 .

The results of the classification analysis are shown below:

Analysis of travel choice behavior of type 1 passengers

For this type of passenger, in the scenario of traveling during the morning peak hours, they are significantly influenced by the fare strategy of off-peak discount. On average, 45.31% of the passengers choose to shift their travel time. Among them, 21.88% of the passengers consider shifting their travel to the pre-peak period when there is a 40% fare reduction, while 12.5% of the passengers consider shifting their travel to the post-peak period when there is a 10% fare reduction. However, during the peak-hour surcharge strategy, on average, only 39.84% of the passengers consider shifting their travel time. In the scenario of traveling during the evening peak hours, only 47.66% of the passengers consistently choose not to shift their travel time. Furthermore, the majority of passengers consider shifting their travel to the pre-peak period. In conclusion, type 1 passengers show a higher sensitivity to off-peak discount strategies and are more likely to change their travel time accordingly.

Analysis of travel choice behavior of type 2 passengers

For this type of passenger, in the scenario of traveling during the morning peak hours, regardless of the fare strategy, only an average of 33.5% of the passengers choose to change their travel time. Among them, 37.62% of the passengers consider shifting their travel time when there is an off-peak discount strategy, and 28.27% of the passengers change their travel time when there is a peak-hour surcharge strategy. Furthermore, it is only when there is a 70% fare increase during peak hours that the passengers consider changing their travel time. In the scenario of traveling during the evening peak hours, an average of 63.79% of the passengers do not consider changing their travel time. However, when an off-peak discount strategy is implemented, the majority of these passengers consider shifting their travel to the post-peak period. In conclusion, type 2 passengers are less influenced by fare strategies, especially in the scenario of traveling during the morning peak hours. They are less likely to change their travel time.

Analysis of travel choice behavior of type 3 passengers

In the scenario of traveling during the morning peak hours, an average of 66.55% of the passengers in type 3 consider changing their travel time. Among them, 71.13% of the passengers consider changing their travel time when there is an off-peak discount strategy, while only 61.97% of the passengers change their travel time under the peak-hour surcharge strategy. Furthermore, the passengers in this group are more inclined to shift their travel to the post-peak period when considering a change in travel time during the morning peak hours. In the scenario of traveling during the evening peak hours, an average of 82.04% of the passengers consider changing their travel time. In conclusion, type 3 passengers are more inclined to change their travel time under the off-peak discount strategy.

In conclusion, an average of 48.45% of passengers consider shifting their travel time under all fare strategies, and all types of passengers are more sensitive to off-peak fare discount strategies. Under such strategies, even a modest reduction in fares can persuade a significant number of passengers to shift their departure time. Therefore, this paper focuses exclusively on investigating the impact of off-peak fare discount strategies on passengers' willingness to change their departure time.

5 Model Construction of Time-Differentiated Fare Discount

The results of Sect. 4 show that the time-differentiated pricing strategy is feasible and that passengers of all types of travel characteristics are more sensitive to the off-peak reduction strategy. Therefore, this paper proposes to encourage passengers who travel during peak periods to change their travel time by adopting a fare discount strategy during each off-peak period, so that the departure time can be shifted from peak hours to off-peak hours. The services provided by urban rail transit are public service products, so its fares need to consider the public welfare of public transportation, and ensure the interests of passengers while also considering the revenue of urban rail transit operating enterprises.

5.1 Notations

Basic notations used for modeling the problem are listed in Table 3 .

5.2 Passenger Flow and Fare Relationship Function

Price elasticity

Price elasticity accurately reflects passengers’ sensitivity to fares. Urban rail transit is a primary mode of transportation for intra-city travel and has high fare elasticity. With the implementation of the time-differentiated fare discount scheme, passenger demand during each time slot changes accordingly. The number of passenger trips during a certain period is related not only to the fares for that period but also to the fares during other periods. This indicates a cross-elasticity of fares across different time periods, as shown in Eq. ( 1 ).

User response load model

Aalami et al. [ 27 ] proposed an economic response load model, which was widely applied in the study of time-differentiated electricity prices. Based on this, a responsive load model for urban rail transit users was proposed [ 28 ], as shown in Eq. ( 2 ).

Derivation of \(q\left( i \right)\) yields Eq. ( 3 ):

To maximize the benefit to passengers, the derivative value should be zero, which leads to Eq. ( 4 ):

A commonly employed user benefit function is shown in Eq. ( 5 ):

Derivation of Eq. ( 5 ) and substitution of Eq. ( 4 ) into the derived equation gives the relationship between passenger flow and fare, as shown in Eq. ( 6 ):

That is, the relationship between passenger flow and fare discount after period i , which is affected by the fare discount in period j , can be obtained as shown in Eq. ( 7 ):

From the user demand response function, it can be observed that after the implementation of the fare discount, the total passenger flow in period i is given by Eqs. ( 8 ) and ( 9 ):

5.3 Utility Functions for Urban Rail Transit Operators and Passengers

Utility function for urban rail transit operators

The revenue of urban rail transit operating companies depends on their operating income and operating costs. This study considers only the fare revenue as the operating income, while the operating cost is composed of vehicle maintenance cost, line facility and equipment maintenance cost, and operating service cost [ 29 ]. As the vehicle maintenance cost and line facility and equipment maintenance cost are not affected by fare strategy, the revenue of the operating enterprise in this paper is related only to fare revenue and operating service cost. The calculation formulas are shown in Eqs. ( 10 ) and ( 11 ):

Based on the above analysis, the operating revenue function of the enterprise before the implementation of the fare discount scheme is shown in Eq. ( 12 ):

Following the implementing of the time-differentiated fare discount scheme, the enterprise's revenue function is as in Eq. ( 13 ):

Urban rail transit serves as a mode of transportation for passengers to travel within a city. Therefore, passengers' utility function depends not only on the fare they pay but also on the cost of crowding, which is affected by the level of congestion on the subway.

The cost of crowding represents a measure of passenger discomfort on the subway, and it is mainly influenced by the level of congestion on the subway after boarding. Therefore, we consider the cost of crowding to be related to the passenger flow in each period, and we calculate it using Eq. ( 14 ):

Based on the above analysis, the passenger utility function before the implementation of the fare discount scheme can be expressed as Eq. ( 15 ):

After the implementation of the time-differentiated fare discount scheme, the passenger utility function is as in Eq. ( 16 ):

5.4 Equilibrium Modeling of Full Load Factor by Period

The full load rate equilibrium model aims to achieve the full load rate equilibrium of trains in the urban rail transit line network for each period, serving as the optimization objective. The train full load rate, denoted by r , can be calculated using Eq. ( 17 ):

The objective of this paper is to equalize the full train load rate across the entire day. To achieve this, the minimum sum of squares of the differences of the full load rate in each period is taken as the objective function, as shown in Eq. ( 18 ):

5.5 Construction of the Fare Discount Model

After establishing the relationship between passenger flow and fares through the user fare demand response function, we construct an optimization model for time-differentiated fare discounts for urban rail transit based on this.

To facilitate the analysis, the following assumptions have been made: (1) Only transfers between different time periods within urban rail transit are considered. (2) No transfers occur between different peak periods. (3) Fare discounts are only applied during off-peak periods, and no fare adjustments are made during peak periods. (4) The study only considers transfers of passenger flow within urban rail transit and does not account for transfers from other modes of transportation to urban rail transit.

To ensure the safe and efficient operation of urban rail transit, the optimization objective of the model is to minimize the sum of squared differences of the full load rate of urban rail transit in different periods, subject to constraints related to operating company efficiency, passenger efficiency, and fare adjustments.

Operating enterprise efficiency

To ensure the profitability of the operating companies, the model must also take into account their benefits. Since implementing the fare discount strategy will result in revenue loss for the enterprise, a constraint is added to ensure that the loss of fare revenue does not exceed a certain percentage of the original revenue. This is shown in Eq. ( 19 ):

By substituting Eqs. ( 12 ) and ( 13 ) into Eq. ( 19 ), we obtain Eq. ( 20 ):

Passenger benefits

As a form of public transportation, the public nature of urban rail transit is an important consideration in pricing, and its public nature can be measured by passenger benefits. Therefore, when constructing the fare discount model, the change in total passenger benefits should be considered. The passenger benefit/loss should be controlled within a certain range, as shown in Eq. ( 21 ):

By substituting Eqs. ( 15 ) and ( 16 ) into Eq. ( 21 ), we obtain Eq. ( 22 ):

Fare constraints

To avoid a reduction in passenger benefits due to excessive fare changes, there will be a range for passenger discount rates, as shown in Eq. ( 23 ):

In summary, the constructed time-differentiated fare discount model is shown in Eq. ( 24 ):

6 Solving the Time-Differentiated Fare Discount Model

This section describes the construction of a time-differentiated fare discount model. The objective of the model is to minimize the sum of squares of the difference of the full load rate of urban rail transit in different periods, subject to constraints on the operating enterprise's interests, passenger benefits, and ticket discount rate. In the time-differentiated fare discount model, the passenger flow of each period is affected by the fare discount rate. The passenger fare response function is used to express the relationship between fare and passenger flow, which can be used to calculate the passenger flow for each period after the fare discount. To solve this optimization problem, a genetic algorithm with a nested fmincon function is designed.

6.1 Design of Time-Differentiated Fare Discount Model Solving Algorithm

In the time-differentiated fare discount model constructed in this paper, the objective is to achieve the full load rate equilibrium in each period while considering both the operating company’s interests and passenger travel benefits, with fare serving as the constraint. As discussed in Sect. 5 , the full-day operation period is divided into five periods, including two peak periods and three off-peak periods. The time-differentiated fare discount model mainly provides fare discounts for the three off-peak periods to encourage passengers to shift their travel to these periods. Therefore, the solution to the fare model involves finding the optimal value of the ternary variable function.

Fare discount model combined with actual passenger flow data

According to the actual passenger flow data, to output a fare discount model with only decision variables, follow these specific steps:

Step 1 Input the initial passenger flow for each period, the departure interval for each period, the number of hours for each period, and the price elasticity of the demand coefficient between periods.

Step 2 Calculate the relationship between passenger flow and fare discount in period i after being affected by fare discount in period j using Eq. ( 7 ).

Step 3 Based on Step 2, use Eqs. ( 8 ) and ( 9 ) to calculate the passenger flow for each period after the fare discount.

Step 4 Calculate the change in the operating company revenue before and after the fare discount based on the passenger flow during each period after the fare discount using Eq. ( 20 ).

Step 5 Calculate the initial full load factor of each period using Eq. ( 17 ), based on the passenger flow and the departure time interval of each period, and the number of hours of each period after the fare discount. Then, use Eq. ( 24 ) to calculate the sum of squares of the difference of the full load rate in different periods before and after the fare discount.

Step 6 Calculate the congestion cost using Eq. ( 14 ) and then calculate the change in passenger benefits before and after the fare discount using Eq. ( 22 ).

The pseudocode is shown in Fig. 2 .

Algorithmic flowchart

Designing genetic algorithms with nested fmincon functions

The classical nonlinear programming algorithm is employed primarily to solve problems involving multivariate functions, with the objective of finding the maximum value. However, most of these classical nonlinear programming algorithms rely on the gradient descent method to address the problem. While this method is highly effective for local search, it has limited ability for global search, often resulting in suboptimal rather than optimal solutions. To overcome this limitation, this section proposes a combination of two algorithms, the genetic algorithm for global search and the nonlinear programming algorithm for local search, resulting in the identification of the global optimal solution.

Step 1 Initialization. Given the interval of fare discount rate of urban rail transit in the model constraints, set the population size to N , generate N individuals randomly in the fare discount rate interval to form a discrete population, and then set the maximum genetic generation number MAXGEN, individual length PRECI, crossover probability px , and variation probability pm . And set the current number of iterations gen = 0.

Step 2 Adaptation degree calculation. By nesting fmincon function to calculate the adaptation degree, the fmincon function is used to calculate the optimal value of the function with the enterprise revenue and passenger benefits as constraints and the sum of squares of the difference of the full load rate in different periods as the target, and the optimal objective function value solved fmincon function is used as the individual adaptation degree to calculate the adaptation degree corresponding to each discount rate scheme.

Step 3 Selection operation. Inherit the individuals with higher fitness in the current population to the next generation population according to some rule or model.

Step 4 Crossover operation. The single-point crossover method is used to exchange part of the chromosomes between two individuals with a cross probability px to produce a new individual.

Step 5 Mutation operation. Some genes in the chromosome are mutated according to the mutation probability pm . Go to Step 7.

Step 6 Update the target value. The mutated population is considered the child generation, which is then transformed into a new set of subpopulations using decimal transformation. These subpopulations are then substituted into the objective function to obtain the objective function value of the child generation. The child generation is then reintroduced into the parent generation to create a new population. Finally, the number of iterations is updated as gen = gen + 1.

Step 7 Termination condition judgment. If gen<MAXGEN, return to step 2; otherwise, the algorithm terminates. At this point, the optimal solution obtained is the optimal discount rate combination in the objective function.

6.2 Analysis of Fare Discount Model Solving Results

In this paper, Shanghai rail transit line 9 is selected as a case to verify the feasibility of the constructed time-differentiated fare discount rate model and the effectiveness of the algorithm.

Table 4 shows the values of the parameters in our models. To maintain a strong alignment between experiments and real-life planning, parameter values associated with urban rail transit (e.g., driving speed, departure interval, and price demand elasticity by period) were first collected from previous research, relevant reports, and subway official website, and then carefully trimmed to adapt to our case study.

According to the developed time-dependent fare discount model for urban rail transit, the genetic algorithm combined with the fmincon function is used to solve the problem and implemented with MATLAB software. After the algorithm reaches convergence, the results of the optimal fare discount rate for each period are shown in Table 5 .

From the above time-differentiated fare discount model solution results, the actual fare discount when taking the arithmetic results of the fare discount to more decimal places is difficult to clear, so the arithmetic results use the rounding method to obtain the final fare discount rate. The fare discount strategy by period is as follows: a 40% discount in the off-peak period before the morning peak, a 10% discount in the off-peak period between the morning and evening peaks, and a 70% discount in the off-peak period after the evening peak, which can achieve the most balanced full load rate in all periods of the day based on ensuring passenger travel benefits. Although the off-peak period fare discount strategy may result in some fare loss for the operating companies, it can attract passengers from other modes of transportation to transfer to rail transportation and reduce the operating and service costs of trains during the peak period, both of which can offset the loss of the operating companies to a certain extent.

The loss of revenue caused by the implementation of fare discounts during the off-peak period is mainly the loss of revenue due to the reduction in ticket prices, which is calculated as in Eq. ( 25 ):

In this example, the data obtained in Tables 5 and 6 can be substituted into Eq. ( 25 ), and the revenue loss caused by the fare discount to the enterprise is 10,702 yuan, accounting for 2.1% of the total fare revenue. The results of the calculation of the total passenger flow and full load ratio for each period before and after the implementation of the fare discount strategy are shown in Table 6 .

Table 6 indicates that after the implementation of the fare discount strategy, 6.88% of passengers who previously traveled during the morning peak shifted to travel during the off-peak period before the morning peak or between the morning and evening peaks. Similarly, 6.66% of passengers who previously traveled during the evening peak shifted to travel during the off-peak period between the morning and evening peaks or after the evening peak.

As shown in Fig. 3 , the implementation of discounted fares during the off-peak period significantly reduces the full train load rate during the morning and evening peak hours. This leads to a balanced full train load rate during each operating period, which achieves the goal of “peak shaving and valley filling” for urban rail transit passenger flow.

Comparison of full load rate before and after optimization in each period

6.3 Model Validation

Due to the uniqueness of urban rail transit fares, the effectiveness of the fare discount model can only be verified through fitting with data obtained from questionnaire surveys.

Further analysis of the passengers' travel behavior under the off-peak discount strategy in the section of the questionnaire survey reveals that passengers traveling during the morning peak period tend to prefer advancing their travel time to before the peak. They only consider postponing their travel time to after the peak when the fare discount is significant. On the other hand, passengers traveling during the evening peak period consider delaying their travel time only when the fare discount is substantial. Therefore, in order to achieve a more balanced load factor during different time periods, the fare discount rates for the off-peak before the morning peak and the off-peak after the evening should be higher than the discount rate during the off-peak between the morning and evening peaks. This aligns with the results obtained from the fare discount model calculations in Sect. 6 .

The passenger flow data analyzed by the example are compared, and the questionnaire data are processed and analyzed, as shown in Table 7 .

To calculate the average absolute error value between the questionnaire data and the model solution data in the case analysis, the calculation formula is expressed as Eq. ( 26 ):

The average absolute error is 0.1368, which shows that the fare model constructed in this paper can effectively reflect passengers' willingness to transfer under different fare discounts, and the optimal fare discount rate satisfies the equilibrium full load rate equilibrium of the equilibrium whole day and time slots can be solved through the model operation.

7 Conclusions and Further Studies

This study considers the sensitivity differences of urban rail transit passengers to different time-differentiated fare schemes based on stated preference surveys. Taking into account the interaction between rail transit operators and passengers, a time-differentiated fare discount model is constructed with the operator's interest and passenger benefits as constraints and the balance of train occupancy rates in different time periods as the objective. Based on the characteristics of the model, a genetic algorithm with nested fmincon functions is designed to solve it. Finally, the effectiveness of the model is validated through actual case analysis. The main research conclusions are as follows:

The stated preference survey method is used to investigate passenger travel characteristics and their willingness to choose different travel options under various time-differentiated fare scenarios. Based on the stated preference survey data, passengers are classified according to their travel characteristics, and their sensitivity differences to different time-differentiated fare schemes are analyzed based on their willingness to choose under different fare scenarios. Finally, the three categories of passengers are found to be more sensitive to the flat-peak fare discount scheme.

Based on the conclusions of the stated preference survey analysis, a time-differentiated fare discount model is constructed that comprehensively considers the operator's interest, passenger travel costs, and passenger flow equilibrium distribution, and a genetic algorithm with nested fmincon function is designed to solve the model. Finally, the practical strategy of fare discounts in time slots is obtained as follows: 40% discount for off-peak periods before the morning peak, 10% discount for off-peak periods between the morning and evening peaks, and 70% discount for off-peak periods after the evening peak. Under this fare strategy, 6.88% of morning peak travelers and 6.66% of evening peak travelers choose to shift to off-peak periods.

The results of the case study are further compared and analyzed with the stated preference survey data, and the average absolute error was calculated to be 0.1368. This indicates that the time-differentiated fare discount model constructed in this paper effectively reflects passengers' willingness to shift.

In this study, the effect of fares on changes in passenger demand and passenger flow equilibrium parameters are taken into account in the time-of-day differentiated fare model. A time-differentiated fare discount model is constructed that comprehensively considers the operator’s interest, passenger travel costs, and passenger flow equilibrium based on the stated preference survey. The research results provide theoretical support and decision-making basis for implementing time-differentiated pricing in urban rail transit.

This paper has some limitations and we hope that future research will take a further step. Firstly, after implementing the fare optimization scheme in this study, the passenger volume transfer from other modes of public transportation to the subway is not considered. Secondly, when transferring passengers to off-peak periods, the original number of operating vehicles during off-peak periods is not considered in terms of whether it could accommodate the transferred total passenger flow. In the future, we will continue to research these two aspects to establish a more accurate and effective time-differentiated fare optimization model, so as to achieve a more balanced temporal distribution of passenger flow in urban rail transit throughout the day.

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Wang FW, Feng AJ (2023) Statistics and development analysis of urban rail transit in China in 2022. Tunn Constr 43(3):521

Google Scholar  

Shi YH, Xiong MW, Sun YF (2016) Research on congestion degree index system of urban rail transit network. J Railw Sci Eng 13(11):2290–2298

Huan N, Hess S, Yao E et al (2021) Time-dependent pricing strategies for metro lines considering peak avoidance behaviour of commuters. Transp A Transp Sci 5:1–44

Tang Y, Jiang Y, Yang H et al (2020) Modeling and optimizing a fare incentive strategy to manage queuing and crowding in mass transit systems. Transp Res Part B Methodol 138:247–267

Article   Google Scholar  

Zou QR (2019) Research on peak-congestion control and management for urban rail transit network. Beijing Jiaotong University, Beijing

Ran ZQ (2020) Research on regulation method of passenger flow based on ticket reward for urban rail transit. Beijing Jiaotong University, Beijing

Daniels R, Mulley C (2013) The paradox of public transport peak spreading: Universities and travel demand management. Int J Sustain Transp 7(2):143–165

Zhuang YF (2018) Study on pedestrian characteristics and entry restrictions in typical bottlenecks of subway space. University of Science and Technology of China, Hefei

Jaradiaz SR (1986) Alternative pricing schemes for the Santiago under-ground system. In: The planning and transport research and computation 14th summer annual meeting, pp 12–25

Currie G (2010) Quick and effective solution to rail overcrowding. Transp Res Rec J Transp Res Board 2146:35–42

Liu Y, Charles P (2013) Spreading peak demand for urban rail transit through differential fare policy: a review of empirical evidence. In: Australasian transport research forum 2013 proceedings. Australasian Transport Research Forum, pp 1-35

Song QM (2011) Metro fares research based on time differential pricing. Beijing Jiaotong University, Beijing

Yu DD, Yao XM, Xu HJ et al (2019) Departure time elasticities of transit travelers under pre-peak discount price. J Transp Syst Eng Inf Technol 19(05):156–162

Wang J (2011) The impacts of variable pricing on boarding choice behavior of subway. Southwest Jiaotong University, Chengdu

Liu JW (2018) Research on the effect of time differential pricing of Beijing subway on passenger travel behavior. Beijing Jiaotong University, p Beijing

Zahra S et al (2022) A novel dynamic fare pricing model based on fuzzy bi-level programming for subway systems with heterogeneous passengers. Comput Ind Eng 172:108654

Tian GC, Zhang JT, Hu YH (2013) Research on urban rail transit’s time differential pricing strategy based on passenger behavior. Shanghai Manag Sci 35(05):77–83

Zheng Z, Hidemichi FJ, Shunsuke M (2014) How does commuting behavior change due to incentives? An empirical study of the Beijing subway system. Transp Res Part F 24(3):17–26

Wang ZJ, Li XH, Chen F (2015) Impact evaluation of a mass transit fare change on demand and revenue utilizing smart card data. Transp Res Part A Policy Pract 77:213–224

Tang Y, Yang H, Wang B, Huang J, Bai Y (2020) A Pareto-improving and revenue-neutral scheme to manage mass transit congestion with heterogeneous commuters. Transp Res Part C Emerg Technol 113:245–259

Zou Q, Yao X, Zhao P et al (2019) Measuring retiming responses of passengers to a prepeak discount fare by tracing smart card data: a practical experiment in the Beijing subway. J Adv Transp 3:1–20

Liu XW (2016) Research on fare optimization of urban rail transit based on elastic demand. Beijing Jiaotong University, Beijing

Zhang XQ (2016) Study on the time differential pricing model of urban rail transit. Beijing Jiaotong University, Beijing

Dai RY (2018) A bi-level planning model for urban rail transit fare based on cumulative prospect theory. East China Jiaotong University, Nanchang

Zhou F, Li C, Huang Z et al (2020) Fare incentive strategies for managing peak-hour congestion in urban rail transit networks. Transp A Transp Sci 3:1–37

Ji SF (2019) Study on the fare level of intercity railway base on bi-objective model. Chang’an University, Xian

Aalami HA, Moghaddam MP, Yousefi GR (2010) Modeling and prioritizing demand response programs in power markets. Electr Power Syst Res 80(4):426–435

Wang L, Li RW, Lu HY et al (2019) Optimization of subway fare based on time-sharing pricing theory. J Chongqing Jiaotong Univ (Nat Sci) 38(8):104–110

Wang Q, Xu GM, Deng LB et al (2022) Optimization model of urban rail transit subsidies based on travel distance. J Railw Sci Eng. https://doi.org/10.19713/j.cnki.43-1423/u.T20221349

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This work is supported by the Shanghai Philosophy and Social Science Planning Project (No.2022BGL001).

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Xiaobing Ding, Chen Hong, Gan Shi, Zhigang Liu, Haoyang Hong & Zhengyuan Zhao

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The authors confirm contribution to the paper as follows: XD: Responsible for the overall planning and design of the paper, the model construction, algorithm design, and solution, review, and editing. CH: Responsible for the implementation and data analysis of the questionnaire survey, wrote the main part of the paper, and participated in the revision and review of the paper. JW: Responsible for paper revision and wrote the revised manuscript. LZ: Responsible for responding to expert opinions and wrote the response to reviewers. GS: Assisted the first author in the model construction and algorithm design and solution, and helped to collect and analyze the data of the questionnaire survey. ZL: Responsible for overall planning and control. HH: Language touch-ups for revised manuscript. ZZ: Assisted in paper revision.

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Part of the questionnaire content of the survey on the intention to adjust the travel time of Shanghai Metro's time-differentiated pricing

Dear Sir and Madam:

Hello! We are graduate students at Shanghai University of Engineering Science and are conducting a survey on the impact of fares on passenger travel time. This survey is anonymous and the data is for research purposes only; thank you for your cooperation! Please answer the following questions according to your wishes, and fill in the corresponding options on the left.

The subway is divided into the following five periods: ① Pre-morning off-peak period: before 7am; ② Morning peak period: between 7am and 10am; ③ Off-peak period during the morning and evening peak periods: between 10am and 5pm; ④ Evening peak period: between 5pm and 8pm; ⑤ Post-evening off-peak period: between 8pm and 12pm.

Assuming you ride the subway every day during the morning peak (between 7 and 10am) and you are given a discount on the off-peak fare, which of the following discount strategies would you choose:

Assuming you ride the subway every day during the morning peak (between 7 and 10am) and you are given a fare increase on the peak hour, which of the following fare increase strategies would you choose:

Assuming you ride the subway every day during the evening peak (between 5 and 8 pm) and you are given a discount on the off-peak fare, which of the following discount strategies would you choose:

Assuming you ride the subway every day during the evening peak (between 5 and 8 pm) and you are given a fare increase on the peak hour, which of the following fare increase strategies would you choose:

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Ding, X., Hong, C., Wu, J. et al. Research on Time-Based Fare Discount Strategy for Urban Rail Transit Peak Congestion. Urban Rail Transit 9 , 352–367 (2023). https://doi.org/10.1007/s40864-023-00203-3

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Received : 26 March 2023

Revised : 15 July 2023

Accepted : 05 August 2023

Published : 17 October 2023

Issue Date : December 2023

DOI : https://doi.org/10.1007/s40864-023-00203-3

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The magnitude and rate of climate change and associated risks depend strongly on near-term mitigation and adaptation actions, and projected adverse impacts and related losses and damages escalate with every increment of global warming.

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Some changes (such as droughts, wildfires, and extreme rainfall) are happening faster than scientists previously assessed. In fact, according to the Intergovernmental Panel on Climate Change (IPCC) — the United Nations body established to assess the science related to climate change — modern humans have never before seen the observed changes in our global climate, and some of these changes are irreversible over the next hundreds to thousands of years.

Scientists have high confidence that global temperatures will continue to rise for many decades, mainly due to greenhouse gases produced by human activities.

The IPCC’s Sixth Assessment report, published in 2021, found that human emissions of heat-trapping gases have already warmed the climate by nearly 2 degrees Fahrenheit (1.1 degrees Celsius) since 1850-1900. 1 The global average temperature is expected to reach or exceed 1.5 degrees C (about 3 degrees F) within the next few decades. These changes will affect all regions of Earth.

The severity of effects caused by climate change will depend on the path of future human activities. More greenhouse gas emissions will lead to more climate extremes and widespread damaging effects across our planet. However, those future effects depend on the total amount of carbon dioxide we emit. So, if we can reduce emissions, we may avoid some of the worst effects.

The scientific evidence is unequivocal: climate change is a threat to human wellbeing and the health of the planet. Any further delay in concerted global action will miss the brief, rapidly closing window to secure a liveable future.

Here are some of the expected effects of global climate change on the United States, according to the Third and Fourth National Climate Assessment Reports:

Future effects of global climate change in the United States:

U.S. Sea Level Likely to Rise 1 to 6.6 Feet by 2100

Global sea level has risen about 8 inches (0.2 meters) since reliable record-keeping began in 1880. By 2100, scientists project that it will rise at least another foot (0.3 meters), but possibly as high as 6.6 feet (2 meters) in a high-emissions scenario. Sea level is rising because of added water from melting land ice and the expansion of seawater as it warms. Image credit: Creative Commons Attribution-Share Alike 4.0

Climate Changes Will Continue Through This Century and Beyond

Global climate is projected to continue warming over this century and beyond. Image credit: Khagani Hasanov, Creative Commons Attribution-Share Alike 3.0

Hurricanes Will Become Stronger and More Intense

Scientists project that hurricane-associated storm intensity and rainfall rates will increase as the climate continues to warm. Image credit: NASA

More Droughts and Heat Waves

Droughts in the Southwest and heat waves (periods of abnormally hot weather lasting days to weeks) are projected to become more intense, and cold waves less intense and less frequent. Image credit: NOAA

Longer Wildfire Season

Warming temperatures have extended and intensified wildfire season in the West, where long-term drought in the region has heightened the risk of fires. Scientists estimate that human-caused climate change has already doubled the area of forest burned in recent decades. By around 2050, the amount of land consumed by wildfires in Western states is projected to further increase by two to six times. Even in traditionally rainy regions like the Southeast, wildfires are projected to increase by about 30%.

Changes in Precipitation Patterns

Climate change is having an uneven effect on precipitation (rain and snow) in the United States, with some locations experiencing increased precipitation and flooding, while others suffer from drought. On average, more winter and spring precipitation is projected for the northern United States, and less for the Southwest, over this century. Image credit: Marvin Nauman/FEMA

Frost-Free Season (and Growing Season) will Lengthen

The length of the frost-free season, and the corresponding growing season, has been increasing since the 1980s, with the largest increases occurring in the western United States. Across the United States, the growing season is projected to continue to lengthen, which will affect ecosystems and agriculture.

Global Temperatures Will Continue to Rise

Summer of 2023 was Earth's hottest summer on record, 0.41 degrees Fahrenheit (F) (0.23 degrees Celsius (C)) warmer than any other summer in NASA’s record and 2.1 degrees F (1.2 C) warmer than the average summer between 1951 and 1980. Image credit: NASA

Arctic Is Very Likely to Become Ice-Free

Sea ice cover in the Arctic Ocean is expected to continue decreasing, and the Arctic Ocean will very likely become essentially ice-free in late summer if current projections hold. This change is expected to occur before mid-century.

U.S. Regional Effects

Climate change is bringing different types of challenges to each region of the country. Some of the current and future impacts are summarized below. These findings are from the Third 3 and Fourth 4 National Climate Assessment Reports, released by the U.S. Global Change Research Program .

• Northeast. Heat waves, heavy downpours, and sea level rise pose increasing challenges to many aspects of life in the Northeast. Infrastructure, agriculture, fisheries, and ecosystems will be increasingly compromised. Farmers can explore new crop options, but these adaptations are not cost- or risk-free. Moreover, adaptive capacity , which varies throughout the region, could be overwhelmed by a changing climate. Many states and cities are beginning to incorporate climate change into their planning.
• Northwest. Changes in the timing of peak flows in rivers and streams are reducing water supplies and worsening competing demands for water. Sea level rise, erosion, flooding, risks to infrastructure, and increasing ocean acidity pose major threats. Increasing wildfire incidence and severity, heat waves, insect outbreaks, and tree diseases are causing widespread forest die-off.
• Southeast. Sea level rise poses widespread and continuing threats to the region’s economy and environment. Extreme heat will affect health, energy, agriculture, and more. Decreased water availability will have economic and environmental impacts.
• Midwest. Extreme heat, heavy downpours, and flooding will affect infrastructure, health, agriculture, forestry, transportation, air and water quality, and more. Climate change will also worsen a range of risks to the Great Lakes.
• Southwest. Climate change has caused increased heat, drought, and insect outbreaks. In turn, these changes have made wildfires more numerous and severe. The warming climate has also caused a decline in water supplies, reduced agricultural yields, and triggered heat-related health impacts in cities. In coastal areas, flooding and erosion are additional concerns.

1. IPCC 2021, Climate Change 2021: The Physical Science Basis , the Working Group I contribution to the Sixth Assessment Report, Cambridge University Press, Cambridge, UK.

2. IPCC, 2013: Summary for Policymakers. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

3. USGCRP 2014, Third Climate Assessment .

4. USGCRP 2017, Fourth Climate Assessment .

Related Resources

A Degree of Difference

So, the Earth's average temperature has increased about 2 degrees Fahrenheit during the 20th century. What's the big deal?

What’s the difference between climate change and global warming?

“Global warming” refers to the long-term warming of the planet. “Climate change” encompasses global warming, but refers to the broader range of changes that are happening to our planet, including rising sea levels; shrinking mountain glaciers; accelerating ice melt in Greenland, Antarctica and the Arctic; and shifts in flower/plant blooming times.

Is it too late to prevent climate change?

Humans have caused major climate changes to happen already, and we have set in motion more changes still. However, if we stopped emitting greenhouse gases today, the rise in global temperatures would begin to flatten within a few years. Temperatures would then plateau but remain well-elevated for many, many centuries.

Discover More Topics From NASA

Explore Earth Science

Earth Science in Action

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Facts About Earth

Virginia Tech study considers ways to increase accessibility for all wildlife enthusiasts

The study conducted by researchers in the College of Natural Resources and Environment focused on ways to include people with disabilities in wildlife-related recreation.

• Jenise L. Jacques

26 Mar 2024

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One in three birders experiences accessibility challenges to participation in birding, according to Virginia Tech researchers Emily Sinkular and Ashley Dayer.

“I like to think of our research as blending together two previously unconnected fields: disability studies and wildlife recreation,” said Sinkular, a Ph.D. student and lead author of the study published March 26 in the journal Human Dimensions of Wildlife . “There’s been quite a lot of research on disability and lots of research on birding, but very few researchers have combined these two topics together.”

The researchers used a nationwide survey of U.S. wildlife viewers to compare the challenges and needs for birders with and without disabilities. Along with co-authors Freya McGregor, research associate in the Department of Fish and Wildlife Conservation , and Morgan Karns ’23, they analyzed open-ended responses using models of disabilities, or different frames of reference, to better understand how to talk about and think about disability so it resonates with disabled people.

“We suggest agencies and organizations reflect on how to make their programs more accessible and train staff or volunteers to do so as well,” said Dayer, associate professor in the Department of Fish and Wildlife Conservation. “Acknowledging that the responsibility to support participation of disabled birders rests on society and institutions, not on disabled people themselves, is essential.”

According to the Centers for Disease Control and Prevention , one in four Americans has a disability and that number is expected to rise with an aging population. Evidence further shows that people with disabilities are also historically underserved in wildlife-related recreation, including birding.

While the researchers found that birders with disabilities experienced more constraints than their peers, including lack of accessible features, safety concerns, and crowds at birding sites, commonalities in their needs for support of their recreational activity were also shown. Birders with and without disabilities expressed interest in access to more high-quality birding locations and information about where and when to view wildlife. This suggests strategies to improve wildlife viewing opportunities can benefit both groups.

“This shows us that agencies or organizations making changes to better include birders with disabilities can actually benefit everyone,” said Sinkular, who is also a student in the Department of Fish and Wildlife Conservation. 

Ultimately, studying and planning for including people with disabilities in recreation will support broader social inclusion for this large population. The benefits of birding are multifaceted, including to mental well-being, social connections, and ultimately conservation actions. This research helps to bring these benefits to people with disabilities.

Dayer’s Human Dimensions Lab  has partnered with the U.S. Fish and Wildlife Service Multistate Conservation Grant Program to increase research on wildlife viewers with disabilities and to support state fish and wildlife agencies in learning how to better support these populations. Her lab specializes in enhancing conservation success through applying social science to effectively engage people and works to ensure that all voices are represented in research and conservation.

Dayer, an affiliated faculty member of Fralin Life Sciences Institute’s  Global Change Center , said she hopes the work broadcasts a message of both inclusion and hope.

“My message for neurodiverse or disabled people: you are not alone in experiencing a desire to access nature and also facing additional challenges to doing so,” Dayer said. “And your challenges are increasingly being seen and addressed.”

Lindsey Haugh

• Accessibility
• College of Natural Resources and Environment
• Fish and Wildlife Conservation
• Fralin Life Sciences Institute
• Global Change Center
• Good Health and Well-Being
• Graduate Research
• Reduce Inequalities

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What the data says about abortion in the u.s..

Pew Research Center has conducted many surveys about abortion over the years, providing a lens into Americans’ views on whether the procedure should be legal, among a host of other questions.

In a  Center survey  conducted nearly a year after the Supreme Court’s June 2022 decision that  ended the constitutional right to abortion , 62% of U.S. adults said the practice should be legal in all or most cases, while 36% said it should be illegal in all or most cases. Another survey conducted a few months before the decision showed that relatively few Americans take an absolutist view on the issue .

Find answers to common questions about abortion in America, based on data from the Centers for Disease Control and Prevention (CDC) and the Guttmacher Institute, which have tracked these patterns for several decades:

How many abortions are there in the U.S. each year?

How has the number of abortions in the u.s. changed over time, what is the abortion rate among women in the u.s. how has it changed over time, what are the most common types of abortion, how many abortion providers are there in the u.s., and how has that number changed, what percentage of abortions are for women who live in a different state from the abortion provider, what are the demographics of women who have had abortions, when during pregnancy do most abortions occur, how often are there medical complications from abortion.

This compilation of data on abortion in the United States draws mainly from two sources: the Centers for Disease Control and Prevention (CDC) and the Guttmacher Institute, both of which have regularly compiled national abortion data for approximately half a century, and which collect their data in different ways.

The CDC data that is highlighted in this post comes from the agency’s “abortion surveillance” reports, which have been published annually since 1974 (and which have included data from 1969). Its figures from 1973 through 1996 include data from all 50 states, the District of Columbia and New York City – 52 “reporting areas” in all. Since 1997, the CDC’s totals have lacked data from some states (most notably California) for the years that those states did not report data to the agency. The four reporting areas that did not submit data to the CDC in 2021 – California, Maryland, New Hampshire and New Jersey – accounted for approximately 25% of all legal induced abortions in the U.S. in 2020, according to Guttmacher’s data. Most states, though,  do  have data in the reports, and the figures for the vast majority of them came from each state’s central health agency, while for some states, the figures came from hospitals and other medical facilities.

Discussion of CDC abortion data involving women’s state of residence, marital status, race, ethnicity, age, abortion history and the number of previous live births excludes the low share of abortions where that information was not supplied. Read the methodology for the CDC’s latest abortion surveillance report , which includes data from 2021, for more details. Previous reports can be found at  stacks.cdc.gov  by entering “abortion surveillance” into the search box.

For the numbers of deaths caused by induced abortions in 1963 and 1965, this analysis looks at reports by the then-U.S. Department of Health, Education and Welfare, a precursor to the Department of Health and Human Services. In computing those figures, we excluded abortions listed in the report under the categories “spontaneous or unspecified” or as “other.” (“Spontaneous abortion” is another way of referring to miscarriages.)

Guttmacher data in this post comes from national surveys of abortion providers that Guttmacher has conducted 19 times since 1973. Guttmacher compiles its figures after contacting every known provider of abortions – clinics, hospitals and physicians’ offices – in the country. It uses questionnaires and health department data, and it provides estimates for abortion providers that don’t respond to its inquiries. (In 2020, the last year for which it has released data on the number of abortions in the U.S., it used estimates for 12% of abortions.) For most of the 2000s, Guttmacher has conducted these national surveys every three years, each time getting abortion data for the prior two years. For each interim year, Guttmacher has calculated estimates based on trends from its own figures and from other data.

The latest full summary of Guttmacher data came in the institute’s report titled “Abortion Incidence and Service Availability in the United States, 2020.” It includes figures for 2020 and 2019 and estimates for 2018. The report includes a methods section.

In addition, this post uses data from StatPearls, an online health care resource, on complications from abortion.

An exact answer is hard to come by. The CDC and the Guttmacher Institute have each tried to measure this for around half a century, but they use different methods and publish different figures.

The last year for which the CDC reported a yearly national total for abortions is 2021. It found there were 625,978 abortions in the District of Columbia and the 46 states with available data that year, up from 597,355 in those states and D.C. in 2020. The corresponding figure for 2019 was 607,720.

The last year for which Guttmacher reported a yearly national total was 2020. It said there were 930,160 abortions that year in all 50 states and the District of Columbia, compared with 916,460 in 2019.

• How the CDC gets its data: It compiles figures that are voluntarily reported by states’ central health agencies, including separate figures for New York City and the District of Columbia. Its latest totals do not include figures from California, Maryland, New Hampshire or New Jersey, which did not report data to the CDC. ( Read the methodology from the latest CDC report .)
• How Guttmacher gets its data: It compiles its figures after contacting every known abortion provider – clinics, hospitals and physicians’ offices – in the country. It uses questionnaires and health department data, then provides estimates for abortion providers that don’t respond. Guttmacher’s figures are higher than the CDC’s in part because they include data (and in some instances, estimates) from all 50 states. ( Read the institute’s latest full report and methodology .)

While the Guttmacher Institute supports abortion rights, its empirical data on abortions in the U.S. has been widely cited by  groups  and  publications  across the political spectrum, including by a  number of those  that  disagree with its positions .

These estimates from Guttmacher and the CDC are results of multiyear efforts to collect data on abortion across the U.S. Last year, Guttmacher also began publishing less precise estimates every few months , based on a much smaller sample of providers.

The figures reported by these organizations include only legal induced abortions conducted by clinics, hospitals or physicians’ offices, or those that make use of abortion pills dispensed from certified facilities such as clinics or physicians’ offices. They do not account for the use of abortion pills that were obtained  outside of clinical settings .

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The annual number of U.S. abortions rose for years after Roe v. Wade legalized the procedure in 1973, reaching its highest levels around the late 1980s and early 1990s, according to both the CDC and Guttmacher. Since then, abortions have generally decreased at what a CDC analysis called  “a slow yet steady pace.”

Guttmacher says the number of abortions occurring in the U.S. in 2020 was 40% lower than it was in 1991. According to the CDC, the number was 36% lower in 2021 than in 1991, looking just at the District of Columbia and the 46 states that reported both of those years.

(The corresponding line graph shows the long-term trend in the number of legal abortions reported by both organizations. To allow for consistent comparisons over time, the CDC figures in the chart have been adjusted to ensure that the same states are counted from one year to the next. Using that approach, the CDC figure for 2021 is 622,108 legal abortions.)

There have been occasional breaks in this long-term pattern of decline – during the middle of the first decade of the 2000s, and then again in the late 2010s. The CDC reported modest 1% and 2% increases in abortions in 2018 and 2019, and then, after a 2% decrease in 2020, a 5% increase in 2021. Guttmacher reported an 8% increase over the three-year period from 2017 to 2020.

As noted above, these figures do not include abortions that use pills obtained outside of clinical settings.

Guttmacher says that in 2020 there were 14.4 abortions in the U.S. per 1,000 women ages 15 to 44. Its data shows that the rate of abortions among women has generally been declining in the U.S. since 1981, when it reported there were 29.3 abortions per 1,000 women in that age range.

The CDC says that in 2021, there were 11.6 abortions in the U.S. per 1,000 women ages 15 to 44. (That figure excludes data from California, the District of Columbia, Maryland, New Hampshire and New Jersey.) Like Guttmacher’s data, the CDC’s figures also suggest a general decline in the abortion rate over time. In 1980, when the CDC reported on all 50 states and D.C., it said there were 25 abortions per 1,000 women ages 15 to 44.

That said, both Guttmacher and the CDC say there were slight increases in the rate of abortions during the late 2010s and early 2020s. Guttmacher says the abortion rate per 1,000 women ages 15 to 44 rose from 13.5 in 2017 to 14.4 in 2020. The CDC says it rose from 11.2 per 1,000 in 2017 to 11.4 in 2019, before falling back to 11.1 in 2020 and then rising again to 11.6 in 2021. (The CDC’s figures for those years exclude data from California, D.C., Maryland, New Hampshire and New Jersey.)

The CDC broadly divides abortions into two categories: surgical abortions and medication abortions, which involve pills. Since the Food and Drug Administration first approved abortion pills in 2000, their use has increased over time as a share of abortions nationally, according to both the CDC and Guttmacher.

The majority of abortions in the U.S. now involve pills, according to both the CDC and Guttmacher. The CDC says 56% of U.S. abortions in 2021 involved pills, up from 53% in 2020 and 44% in 2019. Its figures for 2021 include the District of Columbia and 44 states that provided this data; its figures for 2020 include D.C. and 44 states (though not all of the same states as in 2021), and its figures for 2019 include D.C. and 45 states.

Guttmacher, which measures this every three years, says 53% of U.S. abortions involved pills in 2020, up from 39% in 2017.

Two pills commonly used together for medication abortions are mifepristone, which, taken first, blocks hormones that support a pregnancy, and misoprostol, which then causes the uterus to empty. According to the FDA, medication abortions are safe  until 10 weeks into pregnancy.

Surgical abortions conducted  during the first trimester  of pregnancy typically use a suction process, while the relatively few surgical abortions that occur  during the second trimester  of a pregnancy typically use a process called dilation and evacuation, according to the UCLA School of Medicine.

In 2020, there were 1,603 facilities in the U.S. that provided abortions,  according to Guttmacher . This included 807 clinics, 530 hospitals and 266 physicians’ offices.

While clinics make up half of the facilities that provide abortions, they are the sites where the vast majority (96%) of abortions are administered, either through procedures or the distribution of pills, according to Guttmacher’s 2020 data. (This includes 54% of abortions that are administered at specialized abortion clinics and 43% at nonspecialized clinics.) Hospitals made up 33% of the facilities that provided abortions in 2020 but accounted for only 3% of abortions that year, while just 1% of abortions were conducted by physicians’ offices.

Looking just at clinics – that is, the total number of specialized abortion clinics and nonspecialized clinics in the U.S. – Guttmacher found the total virtually unchanged between 2017 (808 clinics) and 2020 (807 clinics). However, there were regional differences. In the Midwest, the number of clinics that provide abortions increased by 11% during those years, and in the West by 6%. The number of clinics  decreased  during those years by 9% in the Northeast and 3% in the South.

The total number of abortion providers has declined dramatically since the 1980s. In 1982, according to Guttmacher, there were 2,908 facilities providing abortions in the U.S., including 789 clinics, 1,405 hospitals and 714 physicians’ offices.

The CDC does not track the number of abortion providers.

In the District of Columbia and the 46 states that provided abortion and residency information to the CDC in 2021, 10.9% of all abortions were performed on women known to live outside the state where the abortion occurred – slightly higher than the percentage in 2020 (9.7%). That year, D.C. and 46 states (though not the same ones as in 2021) reported abortion and residency data. (The total number of abortions used in these calculations included figures for women with both known and unknown residential status.)

The share of reported abortions performed on women outside their state of residence was much higher before the 1973 Roe decision that stopped states from banning abortion. In 1972, 41% of all abortions in D.C. and the 20 states that provided this information to the CDC that year were performed on women outside their state of residence. In 1973, the corresponding figure was 21% in the District of Columbia and the 41 states that provided this information, and in 1974 it was 11% in D.C. and the 43 states that provided data.

In the District of Columbia and the 46 states that reported age data to  the CDC in 2021, the majority of women who had abortions (57%) were in their 20s, while about three-in-ten (31%) were in their 30s. Teens ages 13 to 19 accounted for 8% of those who had abortions, while women ages 40 to 44 accounted for about 4%.

The vast majority of women who had abortions in 2021 were unmarried (87%), while married women accounted for 13%, according to  the CDC , which had data on this from 37 states.

In the District of Columbia, New York City (but not the rest of New York) and the 31 states that reported racial and ethnic data on abortion to  the CDC , 42% of all women who had abortions in 2021 were non-Hispanic Black, while 30% were non-Hispanic White, 22% were Hispanic and 6% were of other races.

Looking at abortion rates among those ages 15 to 44, there were 28.6 abortions per 1,000 non-Hispanic Black women in 2021; 12.3 abortions per 1,000 Hispanic women; 6.4 abortions per 1,000 non-Hispanic White women; and 9.2 abortions per 1,000 women of other races, the  CDC reported  from those same 31 states, D.C. and New York City.

For 57% of U.S. women who had induced abortions in 2021, it was the first time they had ever had one,  according to the CDC.  For nearly a quarter (24%), it was their second abortion. For 11% of women who had an abortion that year, it was their third, and for 8% it was their fourth or more. These CDC figures include data from 41 states and New York City, but not the rest of New York.

Nearly four-in-ten women who had abortions in 2021 (39%) had no previous live births at the time they had an abortion,  according to the CDC . Almost a quarter (24%) of women who had abortions in 2021 had one previous live birth, 20% had two previous live births, 10% had three, and 7% had four or more previous live births. These CDC figures include data from 41 states and New York City, but not the rest of New York.

The vast majority of abortions occur during the first trimester of a pregnancy. In 2021, 93% of abortions occurred during the first trimester – that is, at or before 13 weeks of gestation,  according to the CDC . An additional 6% occurred between 14 and 20 weeks of pregnancy, and about 1% were performed at 21 weeks or more of gestation. These CDC figures include data from 40 states and New York City, but not the rest of New York.

About 2% of all abortions in the U.S. involve some type of complication for the woman , according to an article in StatPearls, an online health care resource. “Most complications are considered minor such as pain, bleeding, infection and post-anesthesia complications,” according to the article.

The CDC calculates  case-fatality rates for women from induced abortions – that is, how many women die from abortion-related complications, for every 100,000 legal abortions that occur in the U.S .  The rate was lowest during the most recent period examined by the agency (2013 to 2020), when there were 0.45 deaths to women per 100,000 legal induced abortions. The case-fatality rate reported by the CDC was highest during the first period examined by the agency (1973 to 1977), when it was 2.09 deaths to women per 100,000 legal induced abortions. During the five-year periods in between, the figure ranged from 0.52 (from 1993 to 1997) to 0.78 (from 1978 to 1982).

The CDC calculates death rates by five-year and seven-year periods because of year-to-year fluctuation in the numbers and due to the relatively low number of women who die from legal induced abortions.

In 2020, the last year for which the CDC has information , six women in the U.S. died due to complications from induced abortions. Four women died in this way in 2019, two in 2018, and three in 2017. (These deaths all followed legal abortions.) Since 1990, the annual number of deaths among women due to legal induced abortion has ranged from two to 12.

The annual number of reported deaths from induced abortions (legal and illegal) tended to be higher in the 1980s, when it ranged from nine to 16, and from 1972 to 1979, when it ranged from 13 to 63. One driver of the decline was the drop in deaths from illegal abortions. There were 39 deaths from illegal abortions in 1972, the last full year before Roe v. Wade. The total fell to 19 in 1973 and to single digits or zero every year after that. (The number of deaths from legal abortions has also declined since then, though with some slight variation over time.)

The number of deaths from induced abortions was considerably higher in the 1960s than afterward. For instance, there were 119 deaths from induced abortions in  1963  and 99 in  1965 , according to reports by the then-U.S. Department of Health, Education and Welfare, a precursor to the Department of Health and Human Services. The CDC is a division of Health and Human Services.

Note: This is an update of a post originally published May 27, 2022, and first updated June 24, 2022.

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Key facts about the abortion debate in America

Public opinion on abortion, three-in-ten or more democrats and republicans don’t agree with their party on abortion, partisanship a bigger factor than geography in views of abortion access locally, do state laws on abortion reflect public opinion, most popular.

About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .

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Is Intermittent Fasting Bad for Your Heart? Here’s What We Know.

By Alice Callahan

You may have seen the headlines: “Intermittent fasting linked to 91 percent increase in risk of death from heart disease”; “The intermittent fasting trend may pose risks to your heart.”

The news came from an abstract presented Monday at an American Heart Association conference in Chicago. The study has not yet been published in a peer-reviewed journal, and experts cautioned that it had many limitations. Here’s what we know.

The Background

Intermittent fasting involves cycling between eating and fasting for specific periods of time. A common approach, for example, is to eat only within an eight-hour window each day, said Krista Varady, a professor of nutrition at the University of Illinois Chicago.

Several short-term trials have suggested that this eating style can lead to some weight loss and may lower blood pressure and improve blood sugar control in certain people, she said.

But the longest intermittent fasting trial lasted only one year , said Victor Wenze Zhong, lead author of the new study and an epidemiologist at Shanghai Jiao Tong University School of Medicine in China. His aim, he said, was to look at longer term health.

The Research

The new study included more than 20,000 adults from the United States. The participants completed two interviews, less than two weeks apart, about what time they ate on the previous day. The researchers then calculated the participants’ average eating windows and assumed it was their typical schedule for the rest of the study, Dr. Zhong said. The participants were followed for an average of eight years.

During that time, the participants who limited their eating to eight hours in a day had a 91 percent greater chance of dying from cardiovascular disease than those who ate over a 12- to 16-hour time frame, the researchers reported.

But there were just 414 people in the eight-hour eating group, Dr. Zhong said. And they tended to be younger and less educated; have lower income and less access to food; and be more likely to smoke than the other participants.

The researchers accounted for these factors in their analysis, Dr. Zhong said. But the study did not show that this style of eating caused deaths from cardiovascular disease, only that the two were linked.

The Limitations

Since the study has not been published or peer-reviewed, it’s challenging to fully evaluate it, Dr. Varady said.

A “major limitation” is that they used just two reports to accurately represent people’s typical eating pattern, Dr. Varady said; and the study did not seem to evaluate what kinds of foods people ate.

Dr. Dariush Mozaffarian, a cardiologist and professor of medicine at Tufts University, called the study “very problematic.” The eight-hour eating group may have included many people who were very busy, or faced other challenges that forced them to miss meals or eat erratically, he said.

The group also could have included people who were already in poor health — those with eating disorders or illness that reduced their appetite, for instance, which may have resulted in them eating during a shorter window, said Satchidananda Panda, a professor at the Salk Institute for Biological Studies in San Diego.

And if intermittent fasting is really harmful, it’s not clear why that might be. Dr. Zhong said that his study was not designed to answer that question.

What’s Next

More research is needed to evaluate the long term health effects of intermittent fasting, Dr. Zhong said.

Intermittent fasting isn’t a good fit for everyone, said Dr. Pam Taub, a cardiologist at the University of California, San Diego. But many of her patients have enjoyed its benefits, like reduced cholesterol levels.

Now, her patients are “confused and scared” by the headlines they’re reading, Dr. Taub said. But she won’t recommend that they change anything based on this study, she said, adding that people should always talk with their doctor before shifting their diet or lifestyle.

An earlier version of this story misrepresented the way researchers collected diet information with the study participants. It was via two interviews, not two questionnaires.

How we handle corrections

Alice Callahan is a Times reporter covering nutrition and health. She has a Ph.D. in nutrition from the University of California, Davis. More about Alice Callahan

A Guide to Better Nutrition

How much salt is too much? Should I cut back ? We asked experts these and other questions about sodium.

Patients were told for years that cutting calories would ease the symptoms of polycystic ovary syndrome. But research suggests dieting may not help at all .

We asked a nutrition expert how she keeps up healthy habits without stressing about food. Here are seven tips  she shared for maintaining that balance.

There are many people who want to lose a few pounds for whom weight loss drugs are not the right choice. Is old-fashioned dieting a good option ?

Salmon is good for you, but choosing the right type to eat isn’t so easy. Here are answers to all your questions about this nutritional powerhouse .

Read these books to shift into a healthier way of thinking about food .

Sign up for Well’s Mediterranean diet week : Each day, we’ll send guidance and recipes to help make 2024 your most nourishing year yet.