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Home > Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 32

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

March 15, 2023

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


Published:

2018-02-08

Proceedings:

Proceedings of the AAAI Conference on Artificial Intelligence, 32

Volume

Issue:

Thirty-Second AAAI Conference on Artificial Intelligence 2018

Track:

Main Track: Machine Learning Applications

Downloads:

Download PDF

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.

DOI:

10.1609/aaai.v32i1.11877


AAAI

Thirty-Second AAAI Conference on Artificial Intelligence 2018


ISSN 2374-3468 (Online) ISSN 2159-5399 (Print)


Published by AAAI Press, Palo Alto, California USA Copyright © 2018, Association for the Advancement of Artificial Intelligence All Rights Reserved.

Topics: AAAI

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