Traffic big data based path planning strategy in public vehicle systems
Public vehicle (PV) systems will be efficient traffic-management platforms in future smart cities, where PVs provide ridesharing trips with balanced QoS (quality of service). PV systems differ from traditional ridesharing due to that the paths and scheduling tasks are calculated by a server according to passengers' requests, and all PVs corporate with each other to achieve higher transportation efficiency. Path planning is the primary problem. The current path planning strategies become inefficient especially for traffic big data in cities of large population and urban area. To ensure real-time scheduling, we propose one efficient path planning strategy with balanced QoS (e.g., waiting time, detour) by restricting search area for each PV, so that a large number of computation is saved. Simulation results based on the Shanghai (China) urban road network show that, the computation can be reduced by 34% compared with the exhaustive search method since many requests violating QoS are excluded.