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• W4381617811 endingPage "9881" @default.
• W4381617811 startingPage "9881" @default.
• W4381617811 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." @default.
• W4381617811 created "2023-06-23" @default.
• W4381617811 creator A5016753232 @default.
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• W4381617811 date "2023-06-21" @default.
• W4381617811 modified "2023-10-09" @default.
• W4381617811 title "Residual Neural Networks for Origin–Destination Trip Matrix Estimation from Traffic Sensor Information" @default.
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• W4381617811 doi "https://doi.org/10.3390/su15139881" @default.
• W4381617811 hasPublicationYear "2023" @default.
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