Multimodal reserve capacity model of optimal signal-controlled road network

This paper proposed a multimodal reserve capacity model in view of the characteristic of mixed traffic flow in road network, which fully considered particularity of public transport traffic flow, interaction of different vehicle classes and influence upon the network reserve capacity by each class. The proposed model was solved by a solution algorithm based on extremum disturbed and simplified Particle Swarm Optimization (dsPSO). Numerical experiment results show that capacity of Origin-Destination(O-D) pairs are in direct ratio with green time proportion of signal-controlled intersections, not all vehicle classespsila traffic flow increase or decrease with O-D total flows, if we pursue the maximum reserve capacity of network all the time, it will damage userspsila profitability on some O-D pairs, if setting a minimum O-D demand multiplier properly it can be avoided. The results indicate that multimodal reserve capacity model can veritably reflect the characteristic of network mixed traffic flow, and that dsPSO is effective, efficient and robust in dealing with the nonlinear constrained bi-level optimization problem.