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Origin-Destination Matrix Estimation Using Traffic Counts and Machine Learning
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AI
DOI：10.1109/ACCESS.2026.3673964.png)
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Abstract
En 中文
The Origin-Destination (OD) matrix is essential for traffic planning as well as for the management of transportation logistics and operations. It represents the movement patterns of individuals and vehicles between different locations. By understanding how traffic flows can interact with the road network, transportation authorities can implement proactive safety measures to reduce accident risks. Effective accident management strategies also rely on knowledge of traffic flow patterns, which the OD matrix provides. However, a sufficiently large dataset is required to accurately estimate an Origin-Destination matrix from traffic counts and to ensure that the resulting predictions are broadly applicable. Due to the lack of publicly available datasets in the area of our investigation, we leveraged the Simulation of Urban Mobility (SUMO), a widely used traffic simulator in the transportation domain, to generate a new dataset and estimate OD matrices from synthetic traffic counts. Our study includes DFRouter, a simulation-based tool, not as a direct benchmark but rather as a reference point to illustrate the effectiveness of machine learning (ML) models in OD matrix estimation. In a comparative analysis, we trained and tested various ML models, including an Artificial Neural Network (ANN) for deep feature extraction combined with a Support Vector Regression (SVR) model in a hybrid ANN+SVR framework. Overall, all ML models employed in our study performed significantly better than DFRouter. The hybrid ANN+SVR model achieved the lowest error rates on our dataset across different OD configurations sizes.
Keywords:
Estimation
Traffic control
Transportation
Machine learning
Detectors
Vehicle dynamics
Optimization
Deep learning
Data models
Surveys
origin destination
road safety
traffic estimation
traffic simulator
Journal
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