Authors: Jhang-Jyun Liao*, National Taiwan University
Topics: Transportation Geography, Spatial Analysis & Modeling
Keywords: public transport, smart card, travel behavior, accessibility, data mining
Session Type: Paper
Start / End Time: 3:20 PM / 4:35 PM
Room: Plaza Ballroom F, Sheraton, Concourse Level
Presentation File: No File Uploaded
Having the advantages of releasing traffic congestion and improving environmental quality, public transport systems have gradually become a vital part of urban transportation. However, public transport systems are usually composed of multiple systems, and a comprehensive understanding of transfer patterns among systems is essential in order to satisfy user needs of public transport. Because system-transferring is related to users’ travel behavior, identifying the spatiotemporal variations of transfer patterns can help public transport agencies in realizing current situations hence enhancing the service quality.
Most previous studies on transfer pattern analysis used survey data. Although the information derived from survey data is more detailed, there are still some disadvantages and limits. Thanks to the progress of data science and the automatic fare collection systems, more and more novel methods using smart card data have been developed. This study plans to apply the transit smart card data of the Taipei Metropolitan Area to analyze the spatiotemporal variations of public transport users’ transfer patterns. Two study issues are preliminarily proposed: How has the “1280 monthly pass” scheme affected transfer patterns? And, what are the associations of transfer patterns with build environment and socioeconomic attributes?
The empirical findings will benefit public transport agencies by having a comprehensive understanding of current operating status. Furthermore, in terms of the relationships between transfer patterns and local context, such findings can be used as a reference for modifying operational strategies, so that the integration among public transport systems can be more efficient, making a better allocation of limited resources.