Using smart card data to model commuters’ responses upon unexpected train delays

• Main Authors: TIAN, Xiancai, ZHENG, Baihua
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2018
• Subjects: Mass Rapid Transit; unexpected train delays; smart card data; trip chains; individual travel patterns; clustering; DBSCAN; feature engineering; response modeling; feature insights; Databases and Information Systems; Numerical Analysis and Scientific Computing; Transportation
• Online Access: https://ink.library.smu.edu.sg/sis_research/4208; https://ink.library.smu.edu.sg/context/sis_research/article/5211/viewcontent/Usingt_Smart_Card_Data_to_Model_Commuters__Response___Upon_Unexpected_Train_Delays__Camera_Ready___1_.pdf
• Institution: Singapore Management University

The mass rapid transit (MRT) network is playing an increasingly important role in Singapore's transit network, thanks to its advantages of higher capacity and faster speed. Unfortunately, due to aging infrastructure, increasing demand, and other reasons like adverse weather condition, commuters in Singapore recently have been facing increasing unexpected train delays (UTDs), which has become a source of frustration for both commuters and operators. Most, if not all, existing works on delay management do not consider commuters' behavior. We dedicate this paper to the study of commuters' behavior during UTDs. We adopt a data-driven approach to analyzing the six-month' real data collected by automated fare collection system in Singapore and build a classification model to predict whether commuters switch from MRT to other transportation modes because of UTDs.