Forecasting Taxi Demands Using Generative Adversarial Networks with Multi-Source Data

Abstract

As a popular transportation mode in urban regions, taxis play an essential role in providing comfortable and convenient services for travelers. For the sake of tackling the imbalance between supply and demand, taxi demand forecasting can help drivers plan their routes and reduce waiting time and oil pollution. This paper proposes a deep learning-based model for taxi demand forecasting with multi-source data using Generative Adversarial Networks. Firstly, main features were extracted from multi-source data, including GPS taxi data, road network data, weather data, and points of interest. Secondly, Generative Adversarial Network, comprised of the recurrent network model and the conventional network model, is adopted for fine-grained taxi demand forecasting. A comprehensive experiment is conducted based on a real-world dataset of the city of Wuhan, China. The experimental results showed that our model outperforms state-of-the-art prediction methods and validates the usefulness of our model. This paper provides insights into the temporal, spatial, and external factors in taxi demand-supply equilibrium based on the results. The findings can help policymakers alter the taxi supply and the taxi lease rents for periods and increase taxi profit.