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Bi-level optimization with hybrid algorithms for energy saving and CO2 emissions reduction by wireless sensor networks for transportation management

In general, there are two sorts of strategies of energy saving and CO2 emissions reduction in transportation systems using wireless sensor network for transportation. They are single-level optimization solutions and the bi-level optimization ones. The bi-level optimization solutions are considered a sort of optimization in both road side unit (RSU) side and onboard unit (OBU) one. However, the single-level optimization solutions are considered optimization only in RSU or OBU side. A bi-level optimization model with Genetic Algorithm/Particle Swarm Optimization (GA/PSO) hybrid algorithm is proposed to improve performance instead of non-hybrid algorithms. The upper-level optimization model considers the realtime traffic characteristics of the traffic flows near the signalized road intersection. At the same time, vehicles in the lower-level optimization model retrieve the real-time traffic signals using vehicular networks. Then, the traffic signals update the schedule and the vehicles motion states for greener environment factor respectively. Simulation results indicate that the GA/PSO hybrid algorithm performs much better than non-hybrid ones especially when the traffic is heavy. The fuel consumption and CO2 emissions are greatly reduced.