Demand management of congested public transport systems: a conceptual framework and application using smart card data
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Demand management of congested public transport systems: a conceptual framework and application using smart card data Anne Halvorsen1,2 · Haris N. Koutsopoulos3 · Zhenliang Ma4 · Jinhua Zhao5
© Springer Science+Business Media, LLC, part of Springer Nature 2019
Abstract Transportation demand management, 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 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 long-term 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. Keywords Public transport · Demand management · Smart card data · User segmentation · Fare discount * Zhenliang Ma [email protected] Extended author information available on the last page of the article
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Transportation
Introduction Public transport systems are playing an increasingly important role in today’s cities. Environmental, technological, and economic trends are leading to a rise in public transport use in mature cities (Hurdle 2014), while population growth and urban expansion have spurred developin
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