Affiliations: [a] Faculty of Information Technology, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau | [b] Lawrence Berkeley National Laboratory, University of California, USA
Correspondence:
[*]
Corresponding author. Jianqing Li, Faculty of Information Technology, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau. Tel.: +853 88972194; E-mail: jqli@must.edu.mo.
Abstract: 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 on-board 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 real-time 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.
