Taxi-Cruising Recommendation via Real-Time Information and Historical Trajectory Data

Abstract

With the development of GPS technology, location-based information services are becoming more and more diverse. Using the trajectory data generated by GPS can analyze and study the various needs of taxi drivers. Big data technology such as interpreting, manipulating data and extracting nugget of information from data is crucial in Intelligent Transportation System (ITS). In order to enhance cruising efficiency of drivers, this paper proposes a Taxi-cruising Recommendation strategy based on Real-time information and Historical Trajectory data (TR-RHT). Primarily, we construct a Passenger-Demand predict model based on Historical Hotspot (PDHH) to predict the passengers' demand in hotspot area. Then, an Improved Decision Tree (IDT) predicting algorithm is proposed to construct spatio-temporal index to select suitable historical data. Furthermore, we introduce Hotspot Recommendation based on Historical Trajectory (HRHT), wherein it defines cruising event as the process of taxi searching for passengers. The HRHT model performs statistics and analysis on the different states of taxi operation, which can obtain the probability of catching passengers at each hotspot and calculate the travel time between hotspots. The model selects the statistics result of cruising efficiency and driving time between hotspots based on spatio-temporal index, then analyzes the selected result to provide optimal pick-up hotspots for drivers. Experiment results show that TR-RHT can precisely suggest a cruising path to reduce cruising time for drivers.