FDST-GCN: A Fundamental Diagram based Spatiotemporal Graph Convolutional Network for expressway traffic forecasting

Jie Zhang, Chunyue Song, Shan Cao and Chun Zhang

Physica A: Statistical Mechanics and its Applications, 2023, vol. 630, issue C

Abstract: Expressway Traffic forecasting is a crucial issue in Intelligent Transportation System (ITS), and extensive studies have been conducted in this field. However, most existing models do not distinguish between expressway and urban road networks in traffic forecasting, which limits the performance improvement of traffic forecasting in expressways. Moreover, how auxiliary features contribute to prediction models are also hardly discussed in current studies. To deal with the above shortages, we put forward a Fundamental Diagram based Spatiotemporal Graph Convolutional Network (FDST-GCN) in this paper, which incorporates the prior knowledge in Fundamental Diagrams (FD) of the expressway and is dedicated to expressway traffic forecasting. FDST-GCN takes the Sequence to Sequence (Seq2Seq) structure as the backbone and assigns tasks to the encoder and decoder. The encoder aims to extract historical information; note the relations between speed, density, and capacity in FD; FDST-GCN uses two GCNs in the encoder to extract the spatial dependencies of speed and volume simultaneously. Decoder devotes to predicting future traffic status, which integrates the Time Feature, Weather Feature, and FD Feature into the model. In experiments, we validate the performance of FDST-GCN by comparing the prediction accuracy with baselines on two real-world traffic datasets; the results show that FDST-GCN improves 8.23% and 7.49% in RMSE compared to the best baselines in the above two datasets, respectively; 2.94% and 10.41% in MAE, respectively. Besides, we also illustrate the characteristics of auxiliary features and how those features boost the prediction accuracy.

Keywords: Traffic forecasting; Expressway; Fundamental diagram; Auxiliary feature; Data fusion (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437123007288
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

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:eee:phsmap:v:630:y:2023:i:c:s0378437123007288

DOI: 10.1016/j.physa.2023.129173

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().