Residual Neural Networks for Origin–Destination Trip Matrix Estimation from Traffic Sensor Information

Abdullah Alshehri, Mahmoud Owais (), Jayadev Gyani, Mishal H. Aljarbou and Saleh Alsulamy
Additional contact information
Abdullah Alshehri: Department of Civil and Environmental Engineering, College of Engineering, Majmaah University, Majmaah 11952, Saudi Arabia
Mahmoud Owais: Civil Engineering Department, Faculty of Engineering, Assiut University, Assiut 71516, Egypt
Jayadev Gyani: Department of Computer Science, College of Computer and Information Sciences, Majmaah University, Majmaah 11952, Saudi Arabia
Mishal H. Aljarbou: Department of Civil and Environmental Engineering, College of Engineering, Majmaah University, Majmaah 11952, Saudi Arabia
Saleh Alsulamy: Department of Architecture and Planning, College of Engineering, King Khalid University, Abha 61421, Saudi Arabia

Sustainability, 2023, vol. 15, issue 13, 1-21

Abstract: Traffic management and control applications require comprehensive knowledge of traffic flow data. Typically, such information is gathered using traffic sensors, which have two basic challenges: First, it is impractical or impossible to install sensors on every arc in a network. Second, sensors do not provide direct information on origin-to-destination (O–D) demand flows. Consequently, it is essential to identify the optimal locations for deploying traffic sensors and then enhance the knowledge gained from this link flow sample to forecast the network’s traffic flow. This article presents residual neural networks—a very deep set of neural networks—to the problem for the first time. The suggested architecture reliably predicts the whole network’s O–D flows utilizing link flows, hence inverting the standard traffic assignment problem. It deduces a relevant correlation between traffic flow statistics and network topology from traffic flow characteristics. To train the proposed deep learning architecture, random synthetic flow data was generated from the historical demand data of the network. A large-scale network was used to test and confirm the model’s performance. Then, the Sioux Falls network was used to compare the results with the literature. The robustness of applying the proposed framework to this particular combined traffic flow problem was determined by maintaining superior prediction accuracy over the literature with a moderate number of traffic sensors.

Keywords: deep learning; sensors location problem; traffic data prediction; O–D estimation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/13/9881/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/13/9881/ (text/html)

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:gam:jsusta:v:15:y:2023:i:13:p:9881-:d:1176088

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().