Mobility modelling for urban traffic surveillance by a team of unmanned aerial vehicles

Abstract

Use of unmanned aerial vehicles (UAVs) for road traffic surveillance is an exciting idea for improving surveillance quality, as a component of intelligent transportation systems and smart cities. Calibrated mobility models help study and analyse several mobility related issues, for their successful deployment in large urban environments. This paper discusses an energy-aware and scalable mobility model for a team of cooperative UAVs monitoring urban road traffic. It also accompanies an extensible framework for territory distribution, to optimise the number of UAVs required for maximising coverage, without compromising operational performance. Since the problem of territory distribution in general is NP-hard, a genetic algorithm-based strategy, considering edge-disjoint paths, is recommended. Simulation results show that the proposed model considerably improves area coverage in comparison with other mobility models. A published dataset simulating vehicular traffic for a mid-size urban city is used to ascertain scalability to realistic urban territories.