Chapter
Jul 2, 2019
Investigating Correlation between Personal Route Choice Behaviors and Macroscopic Traffic Flow Distribution Based on Long-Term GPS Trajectory Data of Floating Vehicles
Authors: Xinyu Zhou [email protected], Mengdi Guo [email protected], Guokun Ding [email protected], Kairui Cui [email protected], and Zhao Zhang [email protected]Author Affiliations
Publication: CICTP 2019
Abstract
Personal route choice behaviors in an urban road network is one of the most important research areas in the field of transportation. This paper aims to investigate the statistic relationship between personal route choice behaviors and macroscopic traffic flow distribution, which is innovative to the literature. The study firstly analyzes routing behaviors based on GPS trajectory data. Further, a statistic analysis for traffic flow distribution was applied. The comparison between route choice behavior and traffic flow distribution exhibits interesting phenomena, i.e., there exist a relationship between time-shortest paths and route choice probability, such a relationship can also be quantified as a linear function, which shows potential application for this research in urban traffic management and operation.
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Published In
CICTP 2019
Pages: 5937 - 5947
Copyright
© 2019 American Society of Civil Engineers.
History
Published online: Jul 2, 2019
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School of Transportation Science and Engineering, Beihang Univ., 37 Xueyuan Rd., Haidian District, Beijing 100191, China. E-mail: [email protected]
School of Transportation Science and Engineering, Beihang Univ., 37 Xueyuan Rd., Haidian District, Beijing 100191, China. E-mail: [email protected]
School of Transportation Science and Engineering, Beihang Univ., 37 Xueyuan Rd., Haidian District, Beijing 100191, China. E-mail: [email protected]
School of Transportation Science and Engineering, Beihang Univ., 37 Xueyuan Rd., Haidian District, Beijing 100191, China. E-mail: [email protected]
Beijing Advanced Innovation Center for Big Data and Brian Computing, School of Transportation Science and Engineering, Beihang Univ., 37 Xueyuan Rd., Haidian District, Beijing 100191, China. E-mail: [email protected]
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