Dynamic Interior Point Method for Vehicular Traffic Optimization

Abstract

The aim of this article is to improve vehicular traffic in terms of both travel time and load balance. To achieve this goal, we propose an optimization model that minimizes the sum of the total travel time in the road network and a time representation of the traffic imbalance effects in the network. This paper presents an analytic formulation of the optimization problem, and an algorithm, Dynamic Interior Point Method (DIPM), that solves this optimization through driver rerouting. Unlike user-optimum traffic optimizations, DIPM leads to better fairness for drivers and works well in case of congestion. Unlike other system-wide traffic optimizations, DIPM considers the effects of the driver behavior on traffic load. Together, these features allow our system to work well in a potential real-world deployment. DIPM benefits from a central server that computes driver routes, which is reachable via cellular networks or vehicular ad hoc networks. Theoretical analysis and simulation results demonstrate that DIPM is fast and can work in real-time. The results of extensive simulations with realistic urban maps and traffic scenarios show that DIPM outperforms other dynamic rerouting algorithms in terms of travel time. DIPM also improves fairness when compared with a user-optimum approach.