Traffic Optimization by Simultaneous Control of Vehicles Speeds and Routes

Abstract

As vehicle-to-vehicle communication and autonomous vehicles penetrate the market of smart cities, more techniques to solve the problem of traffic flow optimization become available. This paper addresses a new approach to tackle the problem of traffic flow optimization by simultaneously controlling the speeds and routes of vehicles. Both of these quantities can be controlled accurately in self-driving cars. The objective function is set to minimize the collective fuel consumption and traveling time of all drivers in the network. The solution eradicates the usage of traffic lights, and instead, employs constraints that disallow vehicles to meet at intersections. This solution can potentially improve the smoothness of traffic flow, because vehicles do not stop at intersections. This results in the reduction of travelling time and fuel consumption due to the process of acceleration and deceleration. Simulations were performed using MATLAB mixed-integer linear programming solver and were shown to give the optimal paths and speeds for 8 groups of cars in a 16-intersection network with a run time of less than 10 seconds.