LAUSR

In order to relieve the traffic jam, the improved particle swarm optimisation is applied in multiple objective optimisation of traffic signal control. Multiple objective optimal model of traffic signal is constructed considering the queue length, vehicle delay, and exhaust emission. The vehicle delay and queue length model under control of traffic signal is constructed through combining the Webster model and High Capacity Manual delay model. The vehicle exhaust emission model under control of traffic signal is also constructed and the objective function and constraint conditions are confirmed. Improved particle swarm optimisation algorithm is established through combining the traditional particle swarm algorithm and genetic algorithm. In every iteration, a number of particles are selected based on hybrid probability to put them into pool. The value of inertia factor can be regulated based on the following non-linear inertia weight decrement function. Finally, the simulation analysis is carried out using an intersection as research objective, flow of straight road ranges from 300 to 450 pcu, the flow of left turn road ranges from 250 to 380 pcu, and the optimal performance index is obtained, the new multiple objective optimisation model can obtain better optimal results than the traditional multiple objective optimisation algorithm, and the better traffic control effect is obtained.