Alex Lu

Measuring and Controlling Subway Fare Evasion: Improving Safety and Security at New York City Transit Authority

New York City Transit (NYCT) has a comprehensive framework for assessing, managing, and combating subway fare evasion. The automated fare collection system, implemented between 1994 and 1997, features lessons learned from field trials of prototypes specifically designed to limit fare abuse. Subway crime has decreased 68% since 2000, and the annual average subway evasion rate remains low at approximately 1.3%. Today, the transit authority measures fare evasion with independent silent observers who use stratified random sampling techniques and classify passenger entries into 19 categories. Evasion rates peak at 3 p.m., when students are dismissed, but otherwise hover around 0.9% at peak and 1.9% at off-peak hours. Busy times and locations have higher evasions per hour but lower evasions per passenger. More evasions occur in lower-income neighborhoods. Staff presence apparently does not reduce evasions. Results are released to the press on request, which promotes transparency and accountability. As an evasion deterrent, NYCT increased fines from

Entry-Only Automated Fare-Collection System Data Used to Infer Ridership, Rider Destinations, Unlinked Trips, and Passenger Miles

60 to

Using Quantitative Methods in Equity and Demographic Analysis to Inform Transit Fare Restructuring Decisions

100 in 2008. Police issued 68,000 summonses and made 19,000 evasion arrests in 2009. Arrests are a more effective deterrent than summonses; the proportion of arrests versus summonses increased in 2010. Video monitoring equipment is used to identify and apprehend chronic fare abusers, particularly swipers who sell subway entries by abusing unlimited fare media.

Performance Measurements on Mass Transit: Case Study of New York City Transit Authority

All U.S. transit agencies receiving FTA Urbanized Area Formula Program funding under Section 5307 (Section 15) report service consumption statistics (revenue passenger miles and unlinked trips) to the National Transit Database. Passenger miles is an incentive-based funding element that generates millions of dollars annually for New York City Transit (NYCT). Originally, Section 15 random sample data were collected by surveyors gathering passenger destination information, followed by manual distance calculation based on judgment of likely travel paths. This method was costly, inefficient, inconsistent, and not always reproducible despite rigorous auditing and certification. NYCT modernized this process by directly retrieving passenger-origination information from the automated fare-collection (AFC) system, inferring destinations with a second swipe, and automating passenger mile calculation by using schedule-driven shortest-path algorithms. While using state-of-art data collection and computation methods, NYCT retained FTA-approved sampling methodology to maintain comparability of data. Success of automated data reporting is maximized by developing algorithms first by using small data sets, followed by clearly documented parallel testing with full involvement of data consumers, including relevant regulatory authorities. Software development is iterative, and computation time should be monitored to ensure scalability. Building on this work, NYCT is developing AFC-based methodologies to infer bus passenger origins and destinations and train loads and it is adapting signal-system data for routine monitoring of operating performance. Reporting automation to the extent at which live data can be used for scheduling and service planning without modeling or special analyses will allow service to be monitored much more frequently and extensively.

Safeguarding Minority Civil Rights and Environmental Justice in Service Delivery and Reductions: Case Study of New York City Transit Authority Title VI Program

New York City Transit (NYCT) and the Metropolitan Transportation Authority have integrated race and income equity considerations into their extensive public outreach processes for fare changes. Responding to FTA civil rights, Title VI, and environmental justice requirements, NYCT developed two quantitative and analytical approaches for forecasting equity impacts of fare restructuring decisions, in place of more traditional origin–destination surveys. The first approach uses standard aggregate fare elasticity models to estimate diversions between different fare classes and ridership losses resulting from fare adjustments. Average farechanges by fare media type are disaggregated with historical farecard usage patterns (consumption data) by subway station and bus route and translated into demographic variables (minority or non-minority and at, below, or above poverty) on the basis of census data. Overall average fare changes are used to analyze equity impacts. A second, more experimental approach identifies user demographics by daily first swipe locations and estimates daily average fares as actually experienced by each passenger by using sequential transactions on discrete farecards. To meet ongoing requirements, methods were developed to analyze impacts separately for peak and off-peak time periods and to demonstrate equity by using statistical tests. Impact analyses results and historical ridership, revenue, and market share data collected by the MetroCard automated fare collection system all inform fare structure design processes, with particular attention devoted to distributing fare increase burdens equitably.

Algorithm to Measure Daily Bus Passenger Miles Using Electronic Farebox Data for National Transit Database Section 15 Reporting

For all organizations, public or private, it is essential to establish measurements to ensure that the services provided are being done well and, when they are not, that the organization can diagnose problems. The mission of New York City Transit (NYCT) is to provide timely and reliable mass transit to more than 7 million daily riders. NYCT has established three main performance indicators (PIs) to ascertain how closely this mission is being met: en route schedule adherence, headway regularity, and wait assessment. Whereas en route schedule adherence (-1 to +5 min) and wait assessment are easily explained, regularity (±50%) is a useful diagnostic for operations management. NYCT selected 23 subway routes and 42 borough-representative principal bus routes for performance analysis. A stratified sample, designed to prevent undesirable sample bias, is generated by using a fully automated system and achieves an accuracy of 95 ±5% at the route level. Computerized data processing and analysis were implemented in 1995. Recently, paperless data collection was initiated; this further decreases reporting lag and improving data quality. Indicators are reported semiannually to the public, and detailed internal diagnostic reports are issued frequently to help operations management improve service performance. PI statistics are now used by senior management for setting goals and by rider advocacy groups to assess agency performance. A partnership and spirit of cooperation has developed between operating areas and analytical staff in troubleshooting delay issues and continuous quality improvement. The PI infrastructure is tapped by a pilot program to assess performance of operations improvement initiatives.

Subway Productivity, Profitability, and Performance: A Tale of Five Cities

Federal civil rights and environmental justice (EJ) mandates require transit agencies to provide service without racial or income discrimination and to ensure meaningful access by individuals with limited English proficiency. EJ research generally focuses on long-range planning and capital investment decision making. However, for operating agencies, equity in scheduling, service planning, and tactical service delivery operations is critical to compliance with Title VI legislation and FTA Circular C4702.1A. In 2009, New York City Transit (NYCT) designed a service reductions package in response to the economic downturn. EJ considerations were integral to its planning. The use of ridership performance criteria for route selection resulted in fewer impacts on routes with heavily minority or low-income populations. Quantitative analysis ensured that protected demographics were not significantly adversely affected by proposed service rationalizations. Route and frequency modifications and service span changes were evaluated with statistical t-tests during programming stages, resulting in proposals sensitive to equity concerns. Operationally, NYCT actively monitors service using U.S. census, survey, and routine agency data. The t-test and the chi-square test explicitly demonstrate racial and income equity in all aspects of agency operations on the basis of service standards and policies. As an example, t-tests compared the observed load factors with published guidelines; no significant differences in service delivery between demographic groups were found.

Passenger Environment Survey: Representing the Customer Perspective in Quality Control

New York City Transit (NYCT) implemented an automated algorithm to estimate daily bus unlinked trips, infer passenger miles, and compute average trip lengths by route with the use of transaction data from an entry-only automated fare collection (AFC) system. Total onboard miles are inferred from symmetries in bus passengers’ daily activity patterns. NYCTs algorithm uses rigorously tested engineering assumptions to detect common data errors caused by mechanical failures, imperfect driver-farebox interactions, and operational reality and applies statistically measured adjustment factors to correct or interpolate for missing passengers from non-AFC boardings and malfunctions. Surveys revealed that under typical operating conditions, non-AFC passengers and farebox data transmission errors accounted for 12% and 5.5% of missing ridership, respectively. The fault-tolerant algorithm uses non-geographic transaction data from an AFC system without automated vehicle locator functionality and directly computes aggregate passenger miles by inferring origin locations from transaction time stamps with scheduled average speed assumptions and without assigning each passengers precise destination. NYCT focused on fully automatic, production-ready algorithms by rejecting alternatives that required excessive coding effort, processor time, difficult-to-obtain data, or manual intervention in favor of logical inference, statistical estimation, and symmetry. Meticulous parallel testing demonstrated that resultant average trip lengths were stable across days and correlate well with manually collected stop-by-stop ridership data. Annual passenger miles were within -1% to 4% of the National Transit Database (NTD) ±10% sample data and were approved by FTA for NTD Section 15 submission.

Measurement of Subway Service Performance at New York City Transit: Case Study with Automated Train Supervision Track-Occupancy Data

Detailed comparative analyses of New York City subways and four Southeast Asian transit systems revealed that Hong Kongs subways are profitable because of high asset productivity resulting from a strategic “prudent commercial” design for high utilization and traffic density. Hong Kong subways also have a land-grant financing framework, a relatively high degree of commercial freedom, and relatively low overhead costs and asset replacement needs. Conversely, Singapore and Taipei, Taiwan, subways are operated by concessioned carriers (akin to New Yorks historic Interborough Rapid Transit and Brooklyn–Manhattan Transit) and are not profitable when government-provided initial capital construction funds are accounted for at typical government bond interest rates. Infrastructure ownership was separated to attract investors and allow government-controlled carriers to function as quasi-private entities. Network design choices with consequences for density and use explain the higher productivity in Asia. New Yorks system was not designed for maximum capital and operating cost efficiency or productivity but to provide high coverage and service levels at lower traffic densities in a socially conscious and inclusive approach, whereas the Taiwan and Singapore governments made design decisions and chose governance structures that enabled higher productivity. Asian systems operate more reliably than New Yorks 100-year-old subway because of modern design parameters, advanced technology, and different legal frameworks and management processes. State-of-good-repair issues that plagued 1970s New York have yet to surface in Asia. Direct comparisons in performance, profitability, and productivity should be avoided unless care is taken to analyze effects of governance, social contexts, design criteria, and reasons for their differences. Nonetheless, comparisons and benchmarking can yield valuable insights for operations improvement under prevailing local constraints.

Strategic Look at Friday Exceptions in Weekday Schedules for Urban Transit Improving Service, Capturing Leisure Markets, and Achieving Cost Savings by Mining Data on Automated Fare Collection Ridership

The Passenger Environment Survey (PES) conducted by New York City Transit (NYCT) of the Metropolitan Transit Authority uses a quantitative and scientific approach to measure the perceptions of NYCTs 7.3 million daily riders. With 6,485 subway cars, 4,576 buses, and 468 stations in a 321-mi2 service area and a population of more than 8 million, quality assurance is a colossal undertaking. PES takes a passenger-centric approach by measuring indicators from the customers perspective. Since its inception in 1983, PES has evolved to include 68 indicators in four distinct categories measured in four passenger environments: subway cars, subway stations, local buses, and express buses. The consistent and well-defined PES standards are clearly understood by operations personnel. Approximately 25 dedicated surveyors, who do not report to operations management, produce semiannual reports with statistical precision exceeding 95% ± 5%. The data are subject to validation and rigorous quality control by trained statistical analysts. Central to the PES program is NYCTs genuine willingness to understand the customers’ experiences and to represent customers in quality assessment. Internally, PES functions as a performance audit. Monthly reports bring the attention of operations management to deficiencies that have been observed. The results are considered in promotion and merit decision-making processes. Externally, PES serves as a dispassionate and analytical measure of the passengers’ experiences. Reported semiannually, the scorecard demonstrates long-term progress in continuous improvement. NYCT regularly receives requests from agencies nationally and internationally wishing to model their quality control programs after the program conducted through the use of PES. This approach demonstrates the dedication of NYCT staff in maintaining a friendly and comfortable system, in good repair, of which every New Yorker can be proud.