Dynamic Travel Time Prediction Using Genetic Programming

The current state-of-practice for predicting travel times assumes that the speeds along the various roadway segments remain constant over the trip duration. This approach produces large prediction errors especially when the segment speeds vary over time. In this paper the authors develop a genetic programming approach for modeling and predicting the expected, lower, and upper bounds of dynamic travel times along freeways. The models obtained from the genetic programming approach are algebraic expressions that provide insights on the spatiotemporal interactions. The use of an algebraic equation also means that the approach is computationally efficient and suitable for real-time applications. The algorithm is tested on a 37-mile freeway section. The prediction error is demonstrated to be significantly lower than that produced by the instantaneous algorithm (p=0.0001). Specifically, the proposed algorithm achieves more than a 25 percent reduction in the prediction error on congested days. When the authors use bagging and genetic programming, the results show that the mean width of the travel interval is less than 5 minutes for the 37-mile trip.

• Supplemental Notes:
  • This paper was sponsored by TRB committee ABJ70 Artificial Intelligence and Advanced Computing Applications.
• Corporate Authors:

  Transportation Research Board

  500 Fifth Street, NW
  Washington, DC  United States  20001
• Authors:
  • Elhenawy, Mohamed
  • Chen, Hao
  • Rakha, Hesham
• Conference:
  • Transportation Research Board 93rd Annual Meeting
  • Location: Washington DC
  • Date: 2014-1-12 to 2014-1-16
• Date: 2014

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: 16p
• Monograph Title: TRB 93rd Annual Meeting Compendium of Papers

Subject/Index Terms

• TRT Terms: Genetic algorithms; Mathematical models; Mathematical prediction; Real time information; Traffic congestion; Travel time; Trip length
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Planning and Forecasting; I71: Traffic Theory; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01516124
• Record Type: Publication
• Report/Paper Numbers: 14-3802
• Files: TRIS, TRB, ATRI
• Created Date: Feb 27 2014 9:06AM