Modeling Taxi Drivers' Behaviour for the Next Destination Prediction

TL;DR

This paper introduces a Recurrent Neural Network model that predicts taxi drivers' next destinations using geographical data, outperforming previous methods and providing precise coordinate predictions.

Contribution

The paper presents a novel RNN-based approach that directly predicts exact destination coordinates, improving accuracy over existing classification methods in human mobility prediction.

Findings

Outperforms the ECML/PKDD 2015 challenge winner in Porto

Achieves better results with less information

Effective on datasets from Manhattan and San Francisco

Abstract

In this paper, we study how to model taxi drivers' behaviour 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' behaviour and encodes the semantics of visited locations by using geographical information from Location-Based Social Networks (LBSNs). In particular, 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.