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Broadway, NY, United States

Reddy A.V.,System Data and Research | Kuhls J.,Office of Management and Budget | Lu A.,Cubicle A17.111
Transportation Research Record

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 $60 to $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. Source

Suchkov B.,Cubicle A17.111 | Boguslavsky M.,Cubicle A17.110 | Reddy A.,System Data and Research
Transportation Research Record

In 2013, with real-time train arrival data becoming widely available through its general transit feed specification real-time (GTFS-RT) feed, New York City Transit (NYCT) recognized the potential for new service management tools capable of on-the-fly performance visualization and reporting. This paper describes the process taken by NYCT to develop and evaluate a transit analysis tool that uses information from automatic train supervision countdown clocks, transmitted as GTFS-RT data, to visualize train spacing, movement, and related parameters. The main part of this tool is a new web application that uses stringline (time-distance) charts for monitoring operations in the system as they happen. Operational problems (e.g., delays, train bunching, or gaps in service) can be identified more easily on stringline charts than on the traditional model board display to help managers and operating personnel deliver continuous service performance improvements. The application was built in-house with the use of existing data resources (the GTFS-RT feed of predicted train arrivals) and open-source tools. In-house development minimized cost and allowed for maximum flexibility to add features in the future. The development team successfully employed a methodology of iterative development to incrementally review, add features, and test the application without having to go through very long design and requirements cycles. As more transit agencies release GTFS-RT data to the public, they may see opportunities to take a similar approach to develop new tools for internal use quickly and with little additional cost. Source

Lu A.,System Data and Research | Reddy A.,System Data and Research
Transportation Research Record

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. NYCT's 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 passenger's 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. Source

Berkovich A.,System Data and Research | Lu A.,Metropolitan Transportation Authority | Levine B.,System Data and Research | Reddy A.,Metropolitan Transportation Authority
Transportation Research Record

An observational sampling methodology was used to explore seat occupancy patterns in New York City subway cars. The study was performed under uncrowded conditions on the basis of special attributes of what otherwise were highly homogeneous plastic bench seats. Onboard seating patterns, measured as relative seat occupancy probabilities, were explained in terms of interactions between railcar design, layout, customer preferences, and resulting behavior. Earlier research focused in general on passenger distribution between cars within long trains or on the desirability of attributes common to all seats, rather than on passenger seating patterns within a single car. Results of the study reported here had their basis in seating-and standing-room occupancy statistics and showed that customers had a clear preference for seats adjacent to doors, no real preference for seats adjacent to support stanchions, and disdain for bench spots between two other seats. On cars that featured transverse seating, customers preferred window seats, but their preference was almost equal for backward-or forward-facing seats. No gender bias in all seated passengers was detected, but as load factor increased, the chance of standing was higher for men than for women. Use of 90% of the seats was achieved only at a 120% load factor. Customers who stood strongly preferred to crowd vestibule areas between doors (particularly in cars with symmetric door arrangements) and to hold on to vertical poles. These findings were consistent with published anecdotes. In future, cars should be designed with asymmetric doors, 2 + 2 + 2 partitioned, longitudinal seats, and no stanchions or partitions near doorways. To understand customer seating preferences further, research should be conducted in commuter rail vehicles with suburban layouts and booth seating and in the subways of other cities. Source

Hickey R.L.,System Data and Research | Lu A.,System Data and Research | Reddy A.,System Data and Research
Transportation Research Record

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 fore-casting 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. Source

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