Ridesourcing Behavior Analysis and Prediction: A Network Perspective

This paper investigates the spatiotemporal characteristics and predictability of the emerging modern traffic behavior, ridesourcing. The authors collect a comprehensive data set of Didi ridesourcing cars on a large geographical scale of a capital city in China, including both the temporal order information and the GPS-recorded spatial trajectories. To extract the features of this kind of traffic behavior, they construct a large-scale network by considering every traffic flow of the orders. Therein, a driver consecutively visiting different regions of the city connects the relationship of these sites. The weighted ridesourcing network shows a consistency of the distribution of trip orders and the Clark model for population distribution. The network also has spatial and temporal features with power laws, sometimes with exponential truncations and log-normal distributions. Furthermore, they propose a general analytical method to quantify the predictability of this kind of behavior by calculating the entropy at a collective level, which can be extended to quantify other traffic behaviors. Finally, by considering the traffic congestion factor, they propose a better neural network based model for predicting dwelling time of the ridesourcing behavior. The authors suggest that the traffic behavior of ridesourcing cars indicates specific non-Markovian characteristics, which can be systematically analyzed from the viewpoint of network sciences.

• Record URL:
  • http://dx.doi.org/10.1109/TITS.2020.3023951
• Record URL:
  • https://ieeexplore.ieee.org/document/9205242
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2022, IEEE.
• Authors:
  • Chen, Duxin
  • Shao, Qi
  • Liu, Zhiyuan
  • Yu, Wenwu
  • Chen, C L Philip
• Publication Date: 2022-2

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 1274-1283
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 23
  • Issue Number: 2
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Behavior; Global Positioning System; Predictive models; Ridesourcing; Traffic data; Trajectory; Urban areas
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01844003
• Record Type: Publication
• Files: TRIS
• Created Date: Apr 25 2022 5:02PM