|Title||Improving Transit Demand Management with Smart Card Data: General Framework and Applications|
|Year of Publication||2015|
|Academic Department||Dept. of Civil and Environmental Engineering|
|Degree||Master of Science in Transportation|
|University||Massachusetts Institute of Technology|
|Keywords||smart card, transit demand|
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 focus of how automatically collected fare data can support the design and evaluation of these measures.
A framework for developing demand management policies is introduced to help guide agencies through this process. It includes establishing motivations for the program, as- pects to consider in its design, as well as dimensions and metrics to evaluate its impacts. Additional considerations for updating a policy are also discussed, as are the possible data sources and methods for supporting analysis.
This framework was applied to a fare incentive strategy implemented at Hong Kong’s MTR system. In addition to establishing existing congestion patterns, a customer clas- sification analysis was performed to understand the typical travel patterns among MTR users. These results were used to evaluate the promotion at three levels of customer ag- gregation: all users, user groups, and a panel of high frequency travelers. The incentive was found to have small but non-negligible impacts on morning travel, particularly at the beginning of the peak hour and among users with commuter-like behavior. Through a change point analysis, it was possible to identify the panel members that responded to the promotion and quantify factors that influenced their decision using a discrete choice model. The findings of these analyses are used to recommend potential improvements to MTR’s current scheme.
Co-supervised by Jinhua Zhao and Haris N. Koutsopoulos