Predicting Taxi-Passenger Demand Using Streaming Data

Informed driving is becoming a key feature to increase the sustainability of taxi companies. The sensors installed in each vehicle are providing new opportunities to automatically discover knowledge, which in return deliver information for realtime decision making. Intelligent transportation systems for taxi dispatching and time-saving route finding are already exploring this sensing data. In this paper, we introduce a novel methodology to predict the spatial distribution of taxi-passenger in a shortterm time horizon using streaming data. We have done so by firstly aggregating the information into a histogram time series. Then, we combined three time series forecasting techniques to output our prediction. Experimental tests were done using the online data transmitted by 441 vehicles of a fleet running in the city of Porto, Portugal. Our results demonstrated that the proposed framework can provide an effective insight into the spatiotemporal distribution of taxi-passenger demand in a 30 minutes horizon.