Deep Architecture for Citywide Travel Time Estimation Incorporating Contextual Information-Reference-Cited by-同舟云学术

Deep Architecture for Citywide Travel Time Estimation Incorporating Contextual Information

Published:2019-05-23 Issue:3 Volume:25 Page:313-329
ISSN:1547-2450
Container-title:Journal of Intelligent Transportation Systems
language:en
Short-container-title:Journal of Intelligent Transportation Systems

Author:

Tang Kun¹²³,Chen Shuyan³,Khattak Aemal J.⁴,Pan Yingjiu³

Affiliation:

1. Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing, China

2. Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing, China

3. School of Transportation, Southeast University, Nanjing, China

4. Department of Civil Engineering, Nebraska Transportation Center, University of Nebraska-Lincoln, Lincoln, NE, USA

Funder

the National Science Foundation of China

the Central Universities and the Jiangsu Province University Graduate Student Research and Innovation Program

Publisher

Informa UK Limited

Subject

Applied Mathematics,Computer Science Applications,Aerospace Engineering,Automotive Engineering,Information Systems,Software,Control and Systems Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/15472450.2019.1617141

Reference41 articles.

1. Hybrid Approach for Short-Term Traffic State and Travel Time Prediction on Highways

2. Modeling travel time of car with varying demand on an urban midblock section

3. Long-term travel time prediction using gradient boosting

4. Cui, Z., Henrickson, K., Ke, R. & Wang, Y. (2018). High-order graph convolutional recurrent neural network: A deep learning framework for network-scale traffic learning and forecasting. arXiv preprint arXiv:1802.07007, (February).

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