Reliability is regularly cited by users of public transportation as one of the most important qualities of service. However, it is not yet well understood how transit riders are affected by unreliability, particularly in the long term. To gain a better understanding of the importance of reliability, a survey focusing on users of San Francisco's public transportation system in California was developed to investigate the link between people's past experiences of unreliability and the adaptation strategies that they used. Respondents were asked to rate the importance of a number of reliability aspects; the aspects found to be most important were the absence of a gap at a transfer stop and the ability to walk up to a stop and leave within 10 min. Users also reported that they considered reliability when planning trips. Common strategies for handling unreliability were using services and routes deemed more reliable and using real-time information. In addition, an ordinal logit model linking past experiences of unreliability to a reduction in transit use was estimated. The most significant negative experiences that drove a reduction in transit use were delays perceived to be the fault of the transit agency, long waits at transfer points, and being prevented from boarding because of crowding. These results have implications in transit planning: passengers may prefer more frequent service with occasional crowding over less frequent buses that are larger and less crowded. In addition, the growing use of real-time information services will continue to affect how people view transit service and perhaps even intensify the unattractiveness of infrequent service.

Increases in ridership are outpacing capacity expansions in several transit systems. By shifting their focus to demand management, agencies can instead influence how customers use the system and get more out of their present capacity. This paper uses Hong Kong’s Mass Transit Railway (MTR) system as a case study to explore the effects of crowding reduction strategies and how to use fare data to support these measures. The MTR system introduced a discount in September 2014 to encourage users to travel before the peak and reduce onboard crowding. To understand the impacts of this intervention, first, existing congestion patterns were reviewed and a clustering analysis was used to reveal typical travel patterns among users. Then, changes to users’ departure times were studied at three levels to evaluate the promotion’s effects. Patterns of all users were measured across both the whole system and for specific rail segments. The travel patterns of the user groups, who have more homogeneous usage characteristics, were also evaluated and revealed groups who had differing responses to the promotion. The incentive was found to have affected morning travel, particularly at the beginning of the peak hour period and among users with commuter-like behavior. Aggregate and group-specific elasticities were developed to inform future promotions and the results were also used to suggest other potential incentive designs.

Transportation Demand Management (TDM), long used to reduce car traffic, is receiving attention among public transport operators as a means to reduce congestion in crowded public transportation systems. Though far less studied, a more structured approach to Public Transport Demand Management (PTDM) can help agencies make informed decisions on the combination of PTDM and infrastructure investments that best manage crowding. Automated fare collection (AFC) data, readily available in many public transport agencies, provide a unique platform to advance systematic approaches for the design and evaluation of PTDM strategies. The paper discusses the main steps for developing PTDM programs: a) problem identification and formulation of program goals; b) program design; c) evaluation; and d) monitoring. The problem identification phase examines bottlenecks in the system based on a spatiotemporal passenger flow analysis. The design phase identifies the main design parameters based on a categorization of potential interventions along spatial, temporal, modal, and targeted user group parameters. Evaluation takes place at the system, group, and individual levels, taking advantage of the detailed information obtained from smart card transaction data. The monitoring phase addresses the longterm sustainability of the intervention and informs potential changes to improve its effectiveness. A case study of a pre-peak fare discount policy in Hong Kong's MTR network is used to illustrate the application of the various steps with focus on evaluation and analysis of the impacts from a behavioral point of view. Smart card data from before and after the implementation of the scheme from a panel of users was used to study policy-induced behavior shifts. A cluster analysis inferred customer groups relevant to the analysis based on their usage patterns. Users who shifted their behavior were identified based on a change point analysis and a logit model was estimated to identify the main factors that contribute to this change: the amount of time a user needed to shift his/her departure time, departure time variability, fare savings, and price sensitivity. User heterogeneity suggests that future incentives may be improved if they target specific groups.

Like many transit agencies, New York City Transit (NYCT) has long relied on operations-focused metrics to measure its performance. Although these metrics, such as capacity provided and terminal on-time performance, are useful internally to indicate the actions needed to improve service, they typically do not represent the customer experience. To improve its transparency and public communications, NYCT launched a new online Subway Dashboard in September 2017. Two new passenger-centric metrics were developed for the dashboard: additional platform time (APT), the extra time passengers spend waiting for a train over the scheduled time, and additional train time (ATT), the extra time they spend riding a train over the scheduled time. Unlike similar existing metrics, NYCT's new methodology is easily transferable to other agencies, even those without exit data from an automated fare collection system. Using a representative origin-destination matrix and daily scheduled and actual train movement data, a simplified train assignment model assigns each passenger trip to a train based on scheduled and actual service. APT and ATT are calculated as the difference in travel times between these two assignments for each individual trip and can then be aggregated based on line or time period. These new customer-centric metrics received praise from transit advocates, academics, other agencies, and the press, and are now used within NYCT for communicating with customers, as well as to understand the customer impacts of operational initiatives.