When Will You Arrive? Estimating Travel Time Based on Deep Neural Networks

Authors

  • Dong Wang Duke University
  • Junbo Zhang Microsoft Research
  • Wei Cao Tsinghua University, Institute for Interdisciplinary Information Sciences
  • Jian Li Tsinghua University, Institute for Interdisciplinary Information Sciences
  • Yu Zheng Microsoft Research

Keywords:

Spatio-temporal data, Deep learning, Applications

Abstract

Estimating the travel time of any path (denoted by a sequence of connected road segments) in a city is of great importance to traffic monitoring, route planning, ridesharing, taxi/Uber dispatching, etc. However, it is a very challenging problem, affected by diverse complex factors, including spatial correlations, temporal dependencies, external conditions (e.g. weather, traffic lights). Prior work usually focuses on estimating the travel times of individual road segments or sub-paths and then summing up these times, which leads to an inaccurate estimation because such approaches do not consider road intersections/traffic lights, and local errors may accumulate. To address these issues, we propose an end-to-end Deep learning framework for Travel Time Estimation called DeepTTE that estimates the travel time of the whole path directly. More specifically, we present a geo-convolution operation by integrating the geographic information into the classical convolution, capable of capturing spatial correlations. By stacking recurrent unit on the geo-convoluton layer, our DeepTTE can capture the temporal dependencies simultaneously. A multi-task learning component is given on the top of DeepTTE, that estimates the travel time of both the entire path and each local path simultaneously during the training phase. The extensive experiments on two large-scale datasets shows our DeepTTE significantly outperforms the state-of-the-art methods.

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Published

2018-04-26

How to Cite

Wang, D., Zhang, J., Cao, W., Li, J., & Zheng, Y. (2018). When Will You Arrive? Estimating Travel Time Based on Deep Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11877

Issue

Section

Main Track: Machine Learning Applications