Abstract
Autonomous vehicles (AVs) and crowdsourcing big data analytics may dramatically change future intelligent transportation in smart cities. AVs may evolve more like a service than a product. To provide best user experience of such service, three primary factors including waiting time, travel time, and supply of AVs are taken into consideration to for multi-objective optimization facilitated in three steps. With the queueing network model and traffic flow analysis, optimal operation of service could be achieved with minimum average travel time. The optimal number of available AVs could be identified while guaranteeing the waiting time of customers. To manage the supply and demand of the service in each geographical area, bipartite graph matching is adopted to accomplish optimal resource allocation. It is further shown that the optimization of operation of autonomous vehicle fleet can be successfully achieved, to outperform what human- driving vehicles can possibly do. A new intelligent transportation paradigm of great energy saving emerges.
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