Studying Route Choice Behavior of Drivers under Day-to-Day Demand Variability

Routing algorithms are often used to assign the demand on to a route connecting their origins and destinations in a network. These algorithms, which generally compute the shortest path for an OD pair, have been designed taking into consideration a single or multiple OD matrices. However, while finding the best route in a single demand period is still plausible, knowing the best path among multiple demand scenarios is quite onerous from a behavioral standpoint. This paper aims at studying driver behavior towards choosing routes across multiple demand scenarios to understand if they can identify the best route across these scenarios. An online experiment was conducted among the staff and students comprising several route choice scenarios involving multiple demand scenarios. A lottery choice experiment was also conducted to determine the risk perceptions of the participants. The results show that around one-quarter of the participants are able to identify the best routes in each of the 9 route choice treatments presented to them. It is also observed that the other one-quarter of the participants preferred the fastest route (shortest in one demand scenario) over the best route (shortest across multiple demand scenarios) across the 9 route choice treatments. The outcomes from this study provide a behavioral evidence to some of the advanced routing algorithms which are increasingly being used in transportation modeling and research.

• Record URL:
  https://annualmeeting.mytrb.org/OnlineProgram/Details/15915
• Supplemental Notes:
  • This paper was sponsored by TRB committee AEP40 Standing Committee on Transportation Network Modeling.
• Corporate Authors:

  Transportation Research Board

  ,    
• Authors:
  • Saxena, Neeraj
  • Dixit, Vinayak
  • Rey, David
• Conference:
  • Transportation Research Board 100th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2021-1-5 to 2021-1-29
• Date: 2021

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: 16p

Subject/Index Terms

• TRT Terms: Algorithms; Automobile drivers; Automobile driving; Behavior; Risk analysis; Route choice; Traffic assignment; Travel demand
• Subject Areas: Highways; Planning and Forecasting;

Filing Info

• Accession Number: 01764084
• Record Type: Publication
• Report/Paper Numbers: TRBAM-21-01557
• Files: TRIS, TRB, ATRI
• Created Date: Feb 4 2021 10:57AM