Measuring accessibility of bus system based on multi-source traffic data

ABSTRACT

Accessibility is a representative indicator for evaluating the supply of bus system. Traditional studies have evaluated the accessibility from different aspects. Considering the interaction among land use, bus timetable arrangement and individual factors, a more holistic accessibility measurement is proposed to combine static and dynamic characteristics from multisource traffic data. The rationale of the proposed model is verified by a case study of bus system in Shenzhen, China, which is carried out to find the spatial and temporal discrepancy of service of bus system. It is found that the adjustment of bus schedule to time-varying travel demand can affect accessibility of bus system and that Land-use development, average bus speed and bus facilities all have positive effects on accessibility of bus system. These findings provide significant reference for transport planning and policy-making. The proposed model is not limited to accessibility measuring of bus system, but also applicable to other travel modes.

KEYWORDS:

• Accessibility
• bus system
• multi-source traffic data
• spatial-temporal distribution

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was jointly supported by the National Key Research and Development Program of China [grant number 2018YFB1600900]; and the National Natural Science Foundation of China [grant number 71601045].

Notes on contributors

Yufan Zuo

Yufan Zuo is a graduate student in School of Transportation, Southeast University. Her research interests include traffic geographic information system and transportation big data analysis and modeling.

Zhiyuan Liu

Zhiyuan Liu is a professor in School of Transportation, Southeast University. He received his Ph.D. in the Department of Civil Engineering, National University of Singapore. His research interests include transportation network planning and management, transportation big data analysis and modeling, public transportation, multi-mode logistics network, intelligent transportation system.

Xiao Fu

Xiao Fu is an associate professor in School of Transportation, Southeast University. She received her Ph.D. in the Department of Civil and Environmental Engineering, Hong Kong Polytechnic University. Her research interests include traffic geographic information system, spatial-temporal big data, multi-mode traffic network analysis and modeling, activity-based models, transport network reliability.