Hong Kong MTR - MIT Partnership

​Funding: Hong Kong MTR

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. 

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. 

 

Abstract
Demand Management in Public Transportation: A Framework and Application, Halvorsen, Anne, Koutsopoulos Haris, and Zhao Jinhua , Working paper, (2017)

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... more

Reducing Subway Crowding: Analysis of an Off-peak Discount Experiment in Hong Kong, Halvorsen, Anne, Koutsopoulos Haris, and Zhao Jinhua , 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... more

Improving Transit Demand Management with Smart Card Data: General Framework and Applications, Halvorsen, Anne , Dept. of Civil and Environmental Engineering, Volume Master of Science in Transportation, Cambridge, MA, (2015)

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 cus- tomers use the system, getting more out of the capacity they already have. However, while demand management is well researched for personal vehicle use, its applications for public transportation are still emerging. This thesis explores the strategies transit agencies can use to reduce overcrowding, with a particular... more

People

MST Student
Edward H. and Joyce Linde Assistant Professor