Monitoring traffic in future cities with aerial swarms: Developing and optimizing a behavior-based surveillance algorithm

Abstract

Traffic monitoring is a key issue to develop smarter and more sustainable cities in the future, allowing to make a better use of the public space and reducing pollution. This work presents an aerial swarm that continuously monitors the traffic in SwarmCity, a simulated city developed in Unity game engine where drones and cars are modeled in a realistic way. The control algorithm of the aerial swarm is based on six behaviors with twenty-three parameters that must be tuned. The optimization of parameters is carried out with a genetic algorithm in a simplified and faster simulator. The best resulting configurations are tested in SwarmCity showing good efficiencies in terms of observed cars over total cars during time windows. The algorithm reaches a good performance making use of an acceptable computational time for the optimization.