Development of a maximum likelihood regression tree-based model for predicting subway incident delay
Document Type
Journal Article
Publication Date
2015
Subject Area
place - asia, mode - subway/metro, ridership - commuting, planning - travel demand management, planning - safety/accidents
Keywords
Subway incidents, Delay, Maximum likelihood regression tree, Accelerated failure time
Abstract
This study aims to develop a maximum likelihood regression tree-based model to predict subway incident delays, which are major negative impacts caused by subway incidents from the commuter’s perspective. Using the Hong Kong subway incident data from 2005 and 2009, a tree comprising 10 terminal nodes is selected to predict subway incident delays in a case study. An accelerated failure time (AFT) analysis is conducted separately for each terminal node. The goodness-of-fit results show that our developed model outperforms the traditional AFT models with fixed and random effects because it can overcome the heterogeneity problem and over-fitting effects. The developed model is beneficial for subway engineers looking to propose effective strategies for reducing subway incident delays, especially in super-large-sized cities with huge public travel demand.
Rights
Permission to publish the abstract has been given by Elsevier, copyright remains with them.
Recommended Citation
Weng, J., Zheng, Y., Qu, X., & Yan, X. (2015). Development of a maximum likelihood regression tree-based model for predicting subway incident delay. Transportation Research Part C: Emerging Technologies, Vol. 57, pp. 30–41.
Comments
Transportation Research Part C Home Page:
http://www.sciencedirect.com/science/journal/0968090X