Estimated Time of Arrival Prediction via Modeling the Spatial-Temporal Interactions between Links and Crosses

Abstract

The ACM SIGSPATIAL GIS CUP 2021 focuses on Estimated Time of Arrival (ETA) prediction, which is important to the travel scheduling and decision-making of ride-hailing platforms. Accurate ETA prediction is very challenging since ETA is affected by many heterogeneous influencing factors, including static features (e.g., number of links) and dynamic features (e.g., real-time road conditions). Meanwhile, ETA can also be affected by complex spatial-temporal dependencies between links and crosses in the route. To tackle the above challenges, we propose a deep learning method based on the Wide-Deep-Recurrent (WDR) architecture while modeling the interactions between links and crosses. We adopt Neural Factorization Machines (NFM) to memorize the historical patterns and a multiple layer perceptron (MLP) to integrate various heterogeneous influencing factors. We also model links and crosses jointly to learn their spatial-temporal dependencies in the route. Extensive experiments conducted on a real dataset show that our method achieves a high prediction accuracy. The source code is available at: https://github.com/wanhuaiyu/WDR-LC.