ZED-TTE: Zone Embedding and Deep Neural Network based Travel Time Estimation Approach

Abstract

Travel time estimation is an important dynamic measure in developing mobility on the road navigation services of Intelligent Transportation System (ITS). The key challenge is how to accurately assess the time required for a given path that is extensively varied and affected by a wealthy number of spatial, temporal, and road conditions factors. However, former works have focused on capturing the local trajectory patterns for reducing the model's accuracy. In this paper, we introduce a novel approach called Zone Embedding and Deep Neural Network-based Travel Time Estimation Approach (ZED-TTE). The main originality of the latter is that it summarizes the road network into several meaningful zones for extracting global spatial correlations and temporal dependencies. Thus, it has a better overview of the global picture to efficiently gauge the travel time for the full path, by directly providing a source and a destination without intermediate trajectory points involving some road external conditions. Experiments carried out on two large-scale real-world taxi trips datasets show that the proposed approach sharply outperforms the state-of-the-art models.