Data-Driven Approach for Travel Time Prediction on Urban Road Sections and Its Application

This paper develops a K-Nearest Neighbors (KNN) Model weighted by the importance of characteristic variables of the Random Forest, to predict the travel time between two adjacent signalized intersections. The importance of each characteristic variable is calculated by the Gini coefficient evaluation index based on the Random Forest model, and is weighted into the KNN model to predict the travel time. In the case study, the density and impact of traffic lights are selected as characteristic variables due to their close relationship with travel time. The travel times are clustered by the DBSCAN algorithm to distinguish the number of stops affected by the traffic lights. Experimental results demonstrate that the proposed model provides an effective approach for urban travel time prediction and outperforms the considered competing methods. Combined with the Dijkstra’s Algorithm, the proposed model is applied to the road network to find the shortest travel time path.

• Record URL:
  • https://doi.org/10.1061/9780784483053.006
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784483053
• Supplemental Notes:
  • © 2020 American Society of Civil Engineers.
• Corporate Authors:

  American Society of Civil Engineers

  1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • Zhou, Jinrong
  • Huang, Min
  • Qian, Yuxiang
• Conference:
  • 20th COTA International Conference of Transportation Professionals
  • Location: Xi’an , China
  • Date: 2020-8-14 to 2020-8-16
• Publication Date: 2020

Language

• English

Media Info

• Media Type: Web
• Pagination: pp 60-73
• Monograph Title: CICTP 2020: Transportation Evolution Impacting Future Mobility

Subject/Index Terms

• TRT Terms: Algorithms; Predictive models; Signalized intersections; Traffic signals; Travel time; Urban highways
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01767305
• Record Type: Publication
• ISBN: 9780784483053
• Files: TRIS, ASCE
• Created Date: Mar 22 2021 10:34AM