Contactless prepaid and bankcards in transit fare collection systems

Thesis (S.M. in Transportation)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering; and, (S.M. in Technology and Policy)--Massachusetts Institute of Technology, Engineering Systems Division, Technology and Policy Program, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 108-113).Many public transit agencies are considering direct acceptance of contactless credit and debit cards (collectively contactless bankcards) at gates in rail stations and on board buses. Concerns have been raised about riders who may not have or may not want to use contactless bankcards for transit fare payments. This thesis presents contactless prepaid cards as a potential fare payment option to meet the needs of these riders, and assesses customer attitudes toward contactless prepaid cards and bankcards. Two case studies are presented of transit agencies planning to implement contactless bankcard fare collection systems: Transport for London and the Chicago Transit Authority. A framework for evaluation of transit prepaid card options addresses two independent policy decisions: card function, or how the prepaid card may be used, and program management, the companies that partner with transit agencies to provide prepaid cards. The two card function options are: open loop cards, accepted at any merchant, or closed loop cards, used only for transit. Five possible program management options are addressed: the transit agency, bill payment companies, prepaid card companies, general payment card companies, and financial institutions. Options are analyzed along three primary dimensions: customer experience, cost, and geographic coverage of card servicing locations. The results show that closed loop and open loop cards may potentially have comparable costs for both the Chicago Transit Authority and Transport for London, although there is substantial uncertainty since no programs have yet been implemented. The cost and revenue uncertainties are higher for open loop cards than for closed loop cards. For all program management options, both transit agencies appear to face a tradeoff between costs and geographic coverage. Transit agency survey data is used to assess demand for contactless prepaid and bankcards. The results show that a small percentage of riders lacks both credit and debit cards and may have to use prepaid cards. Moreover, the majority of riders in London prefer closed loop prepaid cards, and most riders in Chicago prefer current fare media over bankcards. Discrete choice models are used to analyze factors influencing the choice between bankcards, prepaid cards, and other fare media for riders in London and Chicago. While trends among ridership groups are not strong, age and availability of payment instruments appear to influence fare media choice.by Candace Elizabeth Brakewood.S.M.in Technology and PolicyS.M.in Transportatio

Equity and Exclusion Issues in Cashless fare payment systems for public transportation

Many transit agencies in the United States plan to automate their fare collection and limit–or even eliminate–the use of cash fares, with the goals of expediting boarding, collecting data, and lowering costs. Yet about 10% of US adults lack a bank account or credit card, and many rely on restrictive cellphone data plans or do not have access to the internet or a smartphone. These riders will find it difficult to access transit in the future. This paper examines transit users’ experiences with fare technologies using a survey of riders in three cities. Our analysis reveals which riders are most at risk of being excluded, and how mitigation strategies could work to overcome barriers to cash-less transit. We find that a significant number of riders (∼30%) currently use cash on-board buses. If on-board cash fares were to be removed, a significant share of these riders appear able to switch to other options, though many imagine they will continue to use cash in some way (e.g. at retail or ticket vending machines); a small number claim they would no longer be able to ride transit if on-board cash fares were removed. Older and lower-income riders are more at risk of exclusion as they often lack access to smartphones or the internet. A significant number rely on less dependable internet sources, such as public Wi-Fi, potentially inhibiting some from using smartphone and internet-based payment systems. Findings suggest approaches to reduce the number of riders excluded from transit during fare technology adoption

Quantifying the Impact of New Mobility on Transit Ridership

USDOT Grant 69A3552047141This Final Report presents the outcomes of Community Analysis Research Project C3 that analyzed the impacts of new mobility modes \u2013 particularly micromobility \u2013 on transit ridership. Micromobility includes modes such as bicycles, electric bicycles (e-bikes) and electric scooters (e-scooters). This research focused specifically on shared electric scooters (e-scooters) in Nashville, Tennessee because of the availability of detailed e-scooter trip and device location data that were obtained through a data request to Nashville\u2019s Metropolitan Planning Organization. T-SCORE Project C3 was divided into two primary parts. The first part of the research performed an empirical analysis to quantify the impacts of the shared e-scooters on bus ridership in Nashville, Tennessee. Fixed effects regression models were estimated to explore six hypotheses about the relationship between bus ridership and shared e-scooters using both infrastructure-based and trip-based measures. The findings suggest that utilitarian shared e-scooter trips are associated with a decrease of 0.94% in bus ridership in Nashville on a typical weekday, whereas shared e-scooter social trips are associated with an increase of 0.86% in bus ridership in Nashville on a typical weekday. These findings suggest that shared e-scooters were associated with a net decrease of about 0.08% of total bus ridership on a typical weekday in Nashville, which is a minimal impact. The second part of T-SCORE Project C3 proposed a mixed methods approach to select locations to place shared e-scooter corrals near transit stops to encourage the use of shared e-scooters connecting to transit using Nashville, Tennessee as a case study. The method first used machine learning techniques to identify shared e-scooters trips that complement transit. Then, a multi-criteria scoring system was applied to rank bus stops based on shared e-scooter activity and bus service characteristics. Based on this scoring system, bus stops with the 50 highest scores were selected as potential locations for shared e-scooter corrals. Then, the capacity for the potential parking locations was estimated based on the hourly shared e-scooter usage. The results suggest that the 50 proposed corral locations could capture about 44% of shared e-scooter demand. The findings of this part of the research project could guide the implementation of shared e-scooter corrals in Nashville and inform other cities about how to select locations for shared e-scooter corrals near transit

WAZE Data Reporting

This study evaluated the quality of crowdsourced Waze data (including reports and speed) and explored promising use scenarios of Waze data to facilitate the development of intelligent transportation in Tennessee. To this end, the thoroughly assessed Waze reports quality in terms of spatiotemporal accuracy and coverage. The study found Waze users reported crash events about 2.2 minutes sooner, on average, than reports of the same events recorded in the state\u2019s Locate/IM incident log. The reported crash locations per Waze are on average 6 feet from the Locate/IM log reported by the officials. It is found that 26% of crashes reported in Waze was matched with 67% Locate/IM crash reports, with the rest 74% reports pointing to unreported incidents. Waze speed is affected by the Wazers behaviors and tends to be slightly higher than detector speed in free-flow status. This study evaluated several novel use scenarios such as secondary crash detection, end of queue detection and tracking, level of service evaluation, work zone monitoring, wildlife hazards and crashes, and pothole detection and maintenance. Results show that Waze is a suitable data source for incident management, level of service evaluation, work zone management, roadway maintenance management, etc. when properly used and in cooperation with the agency\u2019s other information sources

Tier 1 University Transportation Center Match Funds for the Strategic Implications of Changing Public Transportation Travel Trends

69A3552047141Even before the onset of the COVID-19 pandemic, public transit ridership was declining in many metropolitan areas in the United States. To regain riders, transit agencies and their partners must make decisions about which strategies and policies to pursue within the constraints of their operating environments. To help address this, the Transit-Serving Communities Optimally, Responsively, and Efficiently (T-SCORE) Tier 1 University Transportation Center was set up as a research consortium from 2020 to 2023 led by Georgia Tech with research partners at the University of Kentucky, Brigham Young University and University of Tennessee, Knoxville (UTK). The T-SCORE Center had two primary research tracks: (1) Community Analysis (led by the University of Tennessee; included in this report) and (2) Multi-Modal Optimization and Simulation (led by the University of Kentucky; not included). The Community Analysis research track employed a combination of quantitative and qualitative research methods to assess three main drivers of change that have affected transit ridership: price and socioeconomic factors, the competitive landscape, and system disruptions, including COVID-19. The research approach for the Community Analysis track was divided into separate projects, and the UTK team led three projects that aimed to: (1) quantify the impact of different factors affecting transit ridership - including the COVID-19 pandemic - at a nationwide scale; (2) assess the impacts of shared micromobility, particularly electric scooters, on transit ridership; and (3) evaluate new fare payment technologies and emerging pricing strategies, with the vision of taking a step toward Mobility-as-a-Service (MaaS). The findings of these three Community Analysis projects can help inform transit agencies and city officials making decisions about how to increase transit ridership and plan for a sustainable future

Business Models for Mobile Fare Apps

Five different business models for mobile fare payment apps are examined, as the world of apps used by transit agencies in the United States and Canada continues to steadily grow.The TRB Transit Cooperative Research Program\u27s TCRP Synthesis 148: Business Models for Mobile Fare Apps documents current practices and experiences of transit agencies that offer mobile fare payment applications to transit riders.The report includes case examples from six cities: Santa Monica, Denver, Austin, Chicago, Dallas, and Ontario, Canada

Quantifying the impact of real-time information on transit ridership

Public transit agencies often struggle with service reliability issues; when a bus or train does not arrive on time, passengers become frustrated and may be less likely to choose transit for future trips. To address reliability problems, transit authorities increasingly provide real-time vehicle location and arrival information to riders via web-enabled and mobile devices. Although prior studies have found several benefits of offering this information to passengers, researchers have had difficulty determining if real-time information affects ridership levels. Therefore, the objective of this dissertation is to quantify the impact of real-time information on public transit ridership. Statistical and econometric methods were used to analyze passenger behavior in three American cities that share a common real-time information platform: New York City, Tampa, and Atlanta. New York City was the setting for a natural experiment in which real-time bus information was gradually launched on a borough-by-borough basis over a three year period. Panel regression techniques were used to evaluate route-level bus ridership while controlling for changes in transit service, fares, local socioeconomic conditions, weather, and other factors. In Tampa, a behavioral experiment was performed with a before-after control group design in which access to real-time bus information was the treatment variable and web-based surveys measured behavior changes over a three month period. In Atlanta, a methodology to combine smart card fare collection data with web-based survey responses was developed to quantify changes in transit travel of individual riders in a before-after study. In summary, each study utilized different data sources and quantitative methods to assess changes in transit ridership. The results varied between cities and suggest that the impact of real-time information on transit travel is greatest in locations that have high levels of transit service. These findings have immediate implications for decision-makers at transit agencies, who often face pressure to increase ridership with limited resources.Ph.D

A Synthesis of Mobile Ticketing Applications Used by Commuter Railroads in the United States

Since 2012, many major commuter railroads have deployed mobile ticketing applications (or “apps”) that allow passengers to pay fares directly using their smartphones. In light of this rapid technological change, this research aims to provide a synthesis of the current state of mobile ticketing in the United States. The 14 largest commuter railroads that have launched mobile ticketing apps are compared in four different areas: (1) the ticket validation process; (2) ticket types offered in the mobile app; (3) additional features in the app; and (4) the process for transferring to other modes using the app. The results reveal that all mobile ticketing applications considered in this analysis utilize visual inspection for validation, and that most of them also use quick response (QR) barcodes to validate tickets. Additionally, many of the commuter rail operators examined offer the majority of the ticket types available via traditional fare media in their mobile ticketing apps. The third dimension revealed a large degree of variation in the availability of additional travel-related features, such as trip planners and schedules, in mobile ticketing apps. Last, only a handful of commuter rail operators have fully integrated transfer policies between commuter rail and other nearby transit modes using mobile ticketing, which is an area that warrants further study. These findings are important for other commuter rail and transit operators that are considering deployment of mobile ticketing systems

Longitudinal Analysis of Light Rail and Streetcar Safety in the United States

Many American cities have launched or expanded light rail or streetcar services recently, which has resulted in a 61% increase in light rail and streetcar revenue miles nationwide during the period 2006–2016. Moreover, light rail and streetcars exhibit higher fatality rates per passenger mile traveled compared with other transit modes. In light of these trends, this study explores light rail and streetcar collisions, injuries, and fatalities using data obtained from the National Transit Database. This study applies a two-part methodology. In the first part, descriptive statistics are calculated for light rail and streetcar collisions, injuries, and fatalities, and a comparative analysis of light rail and streetcars is performed. In the second part, multilevel negative binomial regression models are used to analyze light rail and streetcar collisions and injuries. Three key findings have emerged from this study. First, the results generally align with findings from prior studies that show the majority of light rail and streetcar collisions occur in mixed right-of-way or near at-grade crossings. Second, this analysis revealed an issue predominantly at stations: 42% of light rail injuries were people waiting or leaving. Third, suicide was the leading cause of light rail fatalities, which represents 28% of all light rail fatalities. The implications of this study are important for cities that currently operate these modes or are planning to introduce new light rail or streetcar service to improve safety

Modeling Transit Rider Preferences for Contactless Bank Cards as Fare Media Transport for London and the Chicago, Illinois, Transit Authority

Several transit agencies are considering the acceptance of contactless credit and debit cards directly at turnstiles and bus fareboxes. With the expertise and scale economies of the payments industry, agencies may reduce fare collection costs and improve regional interoperability and ease of use. Given these possible advantages, transit agencies want to understand rider demand for this new fare medium. Transit rider preferences for contactless bank cards were evaluated at two major public transit agencies, Transport for London and the Chicago Transit Authority in Illinois. Stated preference survey results from both transit agencies were analyzed, and discrete choice models for fare medium preference were used to assess factors influencing the demand for contactless bank cards. The results showed that approximately 33% of riders in London and 36% of riders in Chicago preferred contactless bank cards over current fare media. Although trends in ridership groups were not strong, a few key factors influenced the choice of fare medium. Riders at both transit agencies who had credit or debit cards tended to prefer contactless bank cards; likewise, younger riders showed a preference for contactless bank cards in both London and Chicago. The results appeared to align with sociology models for consumer adoption of new technologies