Demand management of congested public transport systems: a conceptual framework and application using smart card data

Abstract

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.

Addressing the “minimum parking” problem for on-demand mobility

“…If human mobility flows were perfectly balanced and evenly distributed throughout the day, no such tradeoff would occur—i.e., if for each passenger delivered at a destination, there were another passenger at the same location available for immediate pickup. In practice, however, there is overwhelming literature showing that human flows are highly unbalanced spatially and temporally 35 , 41 – 43 ; thus, it is important to observe that, unless sharing of rides is considered, the sharing of vehicles cannot directly reduce the total traveled distance.…”

“…Second, human mobility is temporally concentrated, particularly in the morning and afternoon peak commute periods 41 – 43 . Most vehicles will be idle at night, and many during the middle of the day, and require parking.…”

Modelling heterogeneity in behavioral response to peak-avoidance policy utilizing naturalistic data of Beijing subway travelers

“…Modeling traveler responses to peak avoidance policies has been an important focus in these studies. Conventional choice models, such as the multivariate logit model (Ben-Elia & Ettema, 2011a, 2011b), multivariate probit model (Zhang et al, 2014), and binary logit model (Halvorsen et al, 2019) have been used to maximize the utility of choosing peak avoidance by incorporating individual socioeconomic characteristics and mode attributes (Ben-Akiva et al, 1999). However, the naturalistic data sources are typically anonymous and do not have information about the sociodemographic characteristics of the travelers.…”

“…Consequently, such models result in 'one size fits all' policy recommendations. However, the homogeneity assumption ignores the fact that there are different types of subway travelers (Zou et al, 2018;Halvorsen et al, 2019), who are likely to have different sensitivities towards the incentives, leading to substantial taste heterogeneity.…”

Algorithm for Tracing Train Delays to Incident Causes

“…Overcrowding is known to be an issue in major transit systems, including the Underground in London, and major systems in Asia like Hong Kong’s MTR and Tokyo’s railways ( 1 , 2 ). In Tokyo, a recent report found that most delays of less than 10 min were caused by passengers ( 3 ).…”