Sixth International Conference on Transportation Engineering
Passenger Hotspot Mining Based on Taxi GPS Data—Taking Chengdu as an Example
Publication: ICTE 2019
ABSTRACT
To explore the status of operation and travel demand characteristic, we extract the taxi operation and travel demand information based on data mining model. On this basis, the passenger flow volume in each period, travel time distribution, travel distance distribution are discussed. Simultaneously, the spatial distributive characteristic of travel demand is obtained by displaying the location information on Arcgis software. In addition, we verified the characteristic through data analysis. Some significant conclusions are drawn through the taxi operation in Chengdu: (1) the travel demand of taxi in Chengdu is stable, and the travel demand on weekends decreased slightly compared with the travel demand on weekdays. (2) The travel demand is concentrated on the district within the third ring road. (3) A significant difference between Chengdu taxi passenger flow and conventional bus flow is that taxi passenger flow does not show obvious peak characteristics in the morning and evening. The proposed approach can obtain taxi operation and travel demand situations, which can provide aid decision making for analysis and evaluation, operation dispatch, and assignment of vehicles.
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ACKNOWLEDGEMENT
The research was supported by the natural science foundation of China (51578465,71402149), the People’s Republic of China.
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Information & Authors
Information
Published In
ICTE 2019
Pages: 883 - 892
Editors: Xiaobo Liu, Ph.D., Southwest Jiaotong University, Qiyuan Peng, Ph.D., Southwest Jiaotong University, and Kelvin C. P. Wang, Ph.D., Oklahoma State University
ISBN (Online): 978-0-7844-8274-2
Copyright
© 2020 American Society of Civil Engineers.
History
Published online: Jan 13, 2020
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