Modeling Taxi Drivers' Behaviour for the Next Destination Prediction

Taxi destination prediction is a very important task for optimizing the efficiency of electronic dispatching systems, thus allowing relevant advantages for both taxi companies and customers. In fact, during periods of high demand, there should be a taxi whose current ride will end near a requested pick up location from a new customer. If an electronic dispatcher is able to know in advance where all taxi drivers will end their current ride, it will also be able to better allocate its resources, identifying which taxi to assign to each call. Moreover, automatic systems for the taxi mobility monitoring collect data that, integrated with other information sources, can help in understanding daytime human mobility routines. In this paper, we introduce a novel approach for addressing the taxi destination prediction problem, based on Recurrent Neural Networks (RNNs) applied to a regression setting. RNNs are trained based on the individual drivers' history and on geographical information (i.e., points of interest), using only the starting point of each ride (with no knowledge about the whole trajectory). The proposed approach was tested on the dataset of the ECML/PKDD Discovery Challenge 2015 - based on the city of Porto - obtaining better results with respect to the competition winner, whilst using less information, and on Manhattan and San Francisco datasets.