Empirical study of daily link traffic volume forecasting based on a deep neural network

Jin Ki Eom, Kwang-Sub Lee, Jin Hong Min and Ho-Chan Kwak

PLOS ONE, 2025, vol. 20, issue 7, 1-20

Abstract: Forecasting the daily link traffic volume is critical in transportation demand analysis in feasibility studies for planning transportation facilities. The high purchase and maintenance cost of commercial transport planning software poses a challenge for several underdeveloped and developing countries. Therefore, there is a need for cost-effective methodology to forecast link traffic volume. This study proposes a data-driven approach for modeling traffic assignment and employs a deep neural network to forecast daily link volume derived from transport planning software. The main idea is that link traffic volume is significantly associated with traffic network attributes (i.e., number of lanes, travel speed, lane capacity, and roadway type) and network flow attributes (i.e., number of shortest paths on the corresponding link and origin-destination travel demand). Therefore, a multi-layer perception model is developed to effectively capture the nonlinear relationship among the link traffic volume, traffic network attributes, and network flow attributes. A case study demonstrated that the proposed method achieves comparable performance to commercial software in forecasting long-term link traffic volume. The obtained results indicated that the proposed method has the potential to serve as an alternative to commercialized software, although further studies are required to validate and enhance its application.

Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0327664 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 27664&type=printable (application/pdf)

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:plo:pone00:0327664

DOI: 10.1371/journal.pone.0327664

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().