Simulation-Based Traffic Management System for Connected and Autonomous Vehicles

The paper presents an idea for a simulation-based traffic management system, which may be especially successful in the era of connected and autonomous vehicles (CAVs). The most important aspect of the system is its ability to evaluate traffic conditions for different traffic control strategies (e.g., different traffic signal settings, different route assignments) using fast traffic simulations and neural networks. It also employs metaheuristics (e.g., genetic algorithms) to find (sub) optimal traffic control strategies. Results of initial experiments show that building such traffic management system might be technically feasible and it may be especially successful in the era of CAVs, for which it may be possible to collect required traffic data and make accurate traffic predictions.