Validation of Traffic Simulation Models Using Reinforcement Learning

This paper presents a novel approach to validate a traffic simulation network. The use of reinforcement learning approach is studied for modeling vehicles' gap acceptance decisions at a stop controlled intersection. The proposed formulation translates a simple gap acceptance decision into a reinforcement learning problem, assuming that drivers' ultimate objective in a traffic network is to optimize wait-time and safety. Using an off-the-shelf simulation tool, drivers are simulated without any notion of the outcome of their decisions. From multiple episodes of gap acceptance decisions, they learn from the outcome of their actions i.e., wait-time and safety. A real-world traffic circle simulation network developed in Paramics simulation software is used to conduct experimental analyses. Results show that using Q-learning, a reinforcement learning algorithm, drivers' gap acceptance behavior can easily be validated at a high level of accuracy without extensively relying on field data.

• Corporate Authors:

  Transportation Research Board

  500 Fifth Street, NW
  Washington, DC  United States  20001
• Authors:
  • Bartin, Bekir
  • Ozbay, Kaan
  • Cavus, Ozlem
• Conference:
  • Transportation Research Board 90th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2011-1-23 to 2011-1-27
• Date: 2011

Language

• English

Media Info

• Media Type: DVD
• Features: Figures; References; Tables;
• Pagination: 16p
• Monograph Title: TRB 90th Annual Meeting Compendium of Papers DVD

Subject/Index Terms

• TRT Terms: Decision making; Driving simulators; Gap acceptance; Highway operations; Traffic data; Traffic safety; Traffic simulation; Validation; Waiting time
• Uncontrolled Terms: Real world traffic; Reinforcement learning; Stop controlled intersections
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01334615
• Record Type: Publication
• Report/Paper Numbers: 11-3394
• Files: TRIS, TRB
• Created Date: Mar 31 2011 8:11AM