Reliability-based journey time prediction via two-stream deep learning with multi-source data-Reference-Cited by-同舟云学术

Reliability-based journey time prediction via two-stream deep learning with multi-source data

Published:2024-01-10 Issue: Volume: Page:1-19
ISSN:1547-2450
Container-title:Journal of Intelligent Transportation Systems
language:en
Short-container-title:Journal of Intelligent Transportation Systems

Author:

Zhuang Li¹²,Wu Xinyue²,Chow Andy H. F.²^ORCID,Ma Wei³,Lam William H.K³,Wong S. C.⁴

Affiliation:

1. School of Cyber Science and Engineering, Southeast University, Nanjing, China

2. Systems Engineering, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, China

3. Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong SAR, China

4. Civil Engineering, The University of Hong Kong, Pokfulam, Hong Kong SAR, China

Funder

Hong Kong Research Grants Council

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/15472450.2023.2301707

Reference71 articles.

1. An integrated feature learning approach using deep learning for travel time prediction

2. Calibration of stochastic link-based fundamental diagram with explicit consideration of speed heterogeneity

3. Bai S. Kolter J. Z. Koltun V. (2018). An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271 .

4. Doubly Dynamic Traffic Assignment

5. A DBSCAN-based framework to mine travel patterns from origin-destination matrices: Proof-of-concept on proxy static OD from Brisbane

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Urban Origin–Destination Travel Time Estimation Using K-Nearest-Neighbor-Based Methods;Mathematics;2024-04-20