Abstract: A new application of distributed computing for large-scale traffic vehicular assignment and routing problems was proposed for smart mobility and proactive traffic management applications. A range of research needs and realization challenges for parallel computing implementation on multi-central processing units （CPU）through multi process interface (MPI) were discussed. A space-time event-based vehicle routing model was applied in a large scale urban network simulation setting. The primal vehicle routing model was decomposed into a set of computationally efficient sub-problems, which could significantly reduce the simulation time cost and communication overhead. The sub-problems were then assigned to independent distributed CPUs that can execute their tasks simultaneously and maintain excellent load balancing. The proposed method was applied to simulate a pilot study in Beijing metropolitan area, specifically in large scale routing and scheduling cases, the computational efficiency was examined under different number of CPU cores. The results show that the proposed parallel computing method can significantly reduce the computing time and reach a speedup of more than 200 on 512 computation nodes.  

Key words: raffic management , smart mobility system (SMS) ,  traffic assignment ,  tree based algorithm , parallel computing