In this paper, we study how to model taxi drivers' behavior and geographical information for an interesting and challenging task: the next destination prediction in a taxi journey. Predicting the next location is a well-studied problem in human mobility, which finds several applications in real-world scenarios, from optimizing the efficiency of electronic dispatching systems to predicting and reducing the traffic jam. This task is normally modeled as a multiclass classification problem, where the goal is to select, among a set of already known locations, the next taxi destination. We present a Recurrent Neural Network (RNN) approach that models the taxi drivers' behavior and encodes the semantics of visited locations by using geographical information from Location-Based Social Networks (LBSNs). In particular, the RNNs are trained to predict the exact coordinates of the next destination, overcoming the problem of producing, in output, a limited set of locations, seen during the training phase. The proposed approach was tested on the ECML/PKDD Discovery Challenge 2015 dataset--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.
|Titolo:||Modelling taxi drivers' behaviour for the next destination prediction|
|Citazione:||Rossi, A., Barlacchi, G., Bianchini, M., & Lepri, B. (2019). Modelling taxi drivers' behaviour for the next destination prediction. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 1-10.|
|Appare nelle tipologie:||1.1 Articolo in rivista|
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