APPLYING NEURAL NETWORK TECHNIQUES TO DETERMINE TRAFFIC FLOW REDIRECTION PROPORTIONS IN ROAD NETWORKS
This article explores traffic management strategies for addressing unpredictable events in transportation networks, focusing on situations where road segment capacity is reduced due to factors like traffic accidents or disruptions. The research aims to determine the proportion of traffic flow redist...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Silesian University of Technology
2025-06-01
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| Series: | Scientific Journal of Silesian University of Technology. Series Transport |
| Subjects: | |
| Online Access: | https://sjsutst.polsl.pl/archives/2025/vol127/267_SJSUTST127_2025_Nguyen_Than_Pham_Nguyen_Ha.pdf |
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| Summary: | This article explores traffic management strategies for addressing unpredictable events in transportation networks, focusing on situations where road segment capacity is reduced due to factors like traffic accidents or disruptions. The research aims to determine the proportion of traffic flow redistribution needed to maintain network efficiency under such conditions. A novel method is proposed to mitigate congestion by rerouting vehicles from heavily loaded roads, identified by high network load coefficients, to alternative routes. The approach also calculates the optimal volume of redirected traffic to avoid overloading other parts of the network, thereby minimizing the risk of secondary congestion. To achieve this, neural network-based survey and regression analysis techniques are utilized, offering precise and data-driven solutions for traffic redirection. The study highlights the potential of improving urban traffic flow through enhancements to indirect traffic control systems integrated into Intelligent Transportation Systems. By optimizing vehicle rerouting strategies, the proposed method seeks to increase ITS efficiency, especially in scenarios with high congestion risks or traffic accidents. This approach promises a more resilient and adaptive urban transportation network, ensuring smoother traffic operations and reduced congestion impacts.
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| ISSN: | 0209-3324 2450-1549 |