Hong Kong MTR – MIT Partnership

​Funding: Hong Kong MTR

Topic 1

Passenger Assignment to Journeys (Yiwen Zhu-NEU, Haris N. Koutsopoulos, Nigel Wilson) The research is looking into the problem of assigning individual passengers to train trips. A probabilistic model utilizing detailed AFC and train movement data is under development, incorporating capacity constraints of individual vehicles. The model estimates the probability that a given passenger boarded a specific train itinerary and the probability of being denied boarding. Such a model can be used for the assessment of the capacity utilization of the system, development of detailed performance metrics from the passengers’ point of view (for example, crowding), identification of individual journey time components, and estimation of the (expected) number of passengers denied boarding, as well information that can used by travel planners. 

Topic 2

Tools for Evaluating Future Operations and Design of Demand Management (new student, Haris Koutsopoulos, and Jinhua Zhao) With the future expansion of the network there is a need for better tools to answer what if operating questions and design and evaluate strategies to deal with disruptions, increases in demand, etc.  This activity will build capabilities, based on commercial tools, to evaluate alternatives operating strategies, and strategies to mitigate and relieve system congestion, either recurrent or due to incidents. 

Topic 3

Personalized Customer Information and Customer Segmentation (new student, Jinhua Zhao and Haris Koutsopoulos) Information has long been recognized as an important instrument for behavioral change. However, generic information provision often proves ineffective. This project aims to develop a framework toward individualized information provision to MTR users. Effective personalization includes three components: Sparse use of information; Deep Customization; Data Infrastructure and Predictive Analytics. One methodological component is the demand prediction at the individualized customer level. The research will develop the general requirements for provision of individualized customer information, evaluate technological alternatives for the communication of information, and design potential experiments to evaluate their effectiveness. Customer segmentation will be used as the means for better understanding different passenger groups and their information needs. 

  • Induction_bus_and_car

    Capacity-Constrained Network Performance Model for Urban Rail Systems

    Transportation Research Record
    (
    2020
    )

    This paper proposes a general Network Performance Model (NPM) for urban rail systems performance monitoring using smart card data. NPM is a schedule-based network loading model with strict capacity constraints and boarding priorities. It distributes passengers over the network given origin-destination (OD) demand, operations, route choice, and effective train capacity. A Bayesian simulation-based optimization method for calibrating the effective train capacity is introduced, which explicitly recognizes that capacity may be different at different stations depending on congestion levels. Case studies with data from the Mass Transit Railway (MTR) network in Hong Kong are used to validate the model and illustrate its applicability. NPM is validated using left behind survey data and exit passenger flow extracted from smart card data. The use of NPM for performance monitoring is demonstrated by analyzing the spatial-temporal crowding patterns in the system and evaluating dispatching strategies.

  • Induction_bus_and_car

    Demand Management of Congested Public Transport Systems: A Conceptual Framework and Application Using Smart Card Data

    Transportation
    (
    2019
    )

    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.

  • Induction_bus_and_car

    Demand Management in Public Transportation: A Framework and Application

    Working paper
    (
    2018
    )

    Transportation demand management (TDM), long used to reduce car traffic, receives increasing attention as means to ease congestion in overcrowded public transit systems. A more structured approach to transit-specific TDM can help agencies find better combinations of demand management and infrastructure investments to satisfy customer need. This paper develops a framework for public transportation demand management (PTDM) including problem identification and formulating program goals, program design, and program evaluation. The problem identification phase includes a spatio-temporal passenger flow analysis, while the design phase categorizes and integrates possible interventions along spatial, temporal, modal, and targeted user group parameters. The evaluation examines effectiveness, efficiency, and acceptability, and utilizes detailed smart card transaction data for analysis at system-wide, group, and individual levels. We apply the framework in a case study of the pre-peak pricing policy in Hong Kong’s MTR network. Contrasting data from before and after the implementation of the scheme, we identified six customer groups using cluster analysis. We used a panel of 20,000 users and the change-point analysis to to study policy-induced behavior shifts at the individual level. Estimating a logit model we identified that the duration of required departure time shift, departure time variability, fare savings, and price sensitivity are key factors influencing behavioral change.

    A public transportation demand management framework helps transit agencies structure policy design and evaluation. Multi-level evaluation using disaggregate smart card data reveals heterogeneous policy responses among disparate groups, implying that targeting specific groups can improve incentives. PTDM complements infrastructure or service expansions, providing additional policy tools for transportation planners.

  • Induction_bus_and_car

    Reducing Subway Crowding: Analysis of an Off-peak Discount Experiment in Hong Kong

    Transportation Research Record: Journal of the Transportation Research Board
    Washington, D.C.
    ,
    (
    2016
    )

    Increases in ridership are outpacing capacity expansions in a number of transit systems. By shifting their focus to demand management, agencies can instead influence how customers use the system, getting more out of the capacity they already have. This paper uses Hong Kong's MTR system as a case study to explore the effects of crowding-reduction strategies as well as methods to use automatically collected fare data to support these measures. MTR introduced a pre-peak discount in September 2014 to encourage users to travel before the peak hour and reduce on-board crowding. To understand the impacts of this intervention, existing congestion patterns were first reviewed and a clustering analysis was performed to reveal typical travel patterns among MTR users. Then changes to when users chose to travel were studied at three levels to evaluate the program’s effects. Patterns among 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, revealing differing responses to the promotion among groups. The incentive was found to have small impacts on morning travel, particularly at the beginning of the peak hour 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.

Team Members