Self-Aware Traffic Route Planning

Authors

  • David Wilkie University of North Carolina at Chapel Hill
  • Jur van den Berg University of North Carolina at Chapel Hill
  • Ming Lin University of North Carolina at Chapel Hill
  • Dinesh Manocha University of North Carolina at Chapel Hill

Abstract

One of the most ubiquitous AI applications is vehicle route planning. While state-of-the-art systems take into account current traffic conditions or historic traffic data, current planning approaches ignore the impact of their own plans on the future traffic conditions. We present a novel algorithm for self-aware route planning that uses the routes it plans for current vehicle traffic to more accurately predict future traffic conditions for subsequent cars. Our planner uses a roadmap with stochastic, time-varying traffic densities that are defined by a combination of historical data and the densities predicted by the planned routes for the cars ahead of the current traffic. We have applied our algorithm to large-scale traffic route planning, and demonstrated that our self-aware route planner can more accurately predict future traffic conditions, which results in a reduction of the travel time for those vehicles that use our algorithm.

Downloads

Published

2011-08-04

How to Cite

Wilkie, D., van den Berg, J., Lin, M., & Manocha, D. (2011). Self-Aware Traffic Route Planning. Proceedings of the AAAI Conference on Artificial Intelligence, 25(1), 1521-1527. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/7984