Demand-Supply Oriented Taxi Suggestion System for Vehicular Social Networks with Real Time Charging Advisor

Data mining based on large-scale taxi traces has become a hot research topic. A vital direction for analyzing taxi GPS dataset is to suggest cruising areas for taxi drivers. Most of the existing researches merely focus on how to maximize drivers‟ profits while overlooking the profit of passengers. Such imbalance makes the existing solutions do not work well in a real-world environment. This paper constructs a recommendation system by jointly considering the profits of both drivers and passengers. The work first investigates the real-time demand-supply level for taxis, and then makes an adaptive tradeoff between the utilities of drivers and passengers for different hotspots. At last, the qualified candidates are suggested to drivers based on analysis. Results indicate that constructed suggestion system achieves a remarkable improvement on the global utility and make equilibrium between the utilities of drivers and passengers at the same time. It also considers a driver‟s utility with four factors, i.e, expected revenue, searching time for next passenger, travel distance and preference. The work also provides a real-time charging station recommendation system for EV taxis via large-scale GPS data mining. In addition, the proposed system providing the solutions and recommendation for the minimal time as well as for the minimal recharging cost for the Electronic Vehicle taxi drivers. Keywords—Vehicular Social Networks, Hotspot location, Trajectory data mining, Supply-demand level.