Dynamic travel time prediction with spatiotemporal features: using a GNN-based deep learning method

Dujuan Wang, Jiacheng Zhu, Yunqiang Yin, Joshua Ignatius, Xiaowen Wei () and Ajay Kumar
Additional contact information
Dujuan Wang: Sichuan University
Jiacheng Zhu: Sichuan University
Yunqiang Yin: University of Electronic Science and Technology of China
Joshua Ignatius: Aston University
Xiaowen Wei: Dongbei University of Finance and Economics
Ajay Kumar: EMLYON Business School

Annals of Operations Research, 2024, vol. 340, issue 1, No 24, 591 pages

Abstract: Abstract Providing accurate travel time prediction plays an important role in Intelligent Transportation System. It is critical in urban travel decision making and significant for traffic control. The main limitation of existing studies is that they do not fully consider the spatiotemporal dependence, exogenous dependence and dynamics of travel time prediction. In this paper, we propose a deep learning model, called DLSF-GR, based on graph neural networks and recurrent neural networks for travel time prediction, which combines multiple learning components to improve learning efficiency. We evaluate the proposed model on the real-world trip dataset in China by comparing with several state-of-the-art methods. The results demonstrate that the developed model performs the best in terms of all considered indicators compared to several state-of-the-art methods, and that the developed specified cross-validation method can enhance the performance of the comparison methods against to the random cross-validation method.

Keywords: Travel time prediction; Deep learning; Graph convolution; Recurrent neural networks (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10479-023-05260-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:annopr:v:340:y:2024:i:1:d:10.1007_s10479-023-05260-2

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10479

DOI: 10.1007/s10479-023-05260-2

Access Statistics for this article

Annals of Operations Research is currently edited by Endre Boros

More articles in Annals of Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().