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.

Modeling taxi drivers' behaviour for the next destination prediction

Gianni Barlacchi;Bruno Lepri
2020-01-01

Abstract

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11582/319208
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