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文献详情 >A Deep Learning for Optimizati... 收藏

A Deep Learning for Optimization and Visualization of Expressway Toll Lane Management

作     者:Klaykul, Pattarapon Lee, Wilaiporn Srisomboon, Kanabadee Pipanmekaporn, Luepol Prayote, Akara 

作者机构:King Mongkuts Univ Technol North Bangkok Dept Comp & Informat Sci Bangkok 10800 Thailand King Mongkuts Univ Technol North Bangkok Dept Elect & Comp Engn Bangkok 10800 Thailand 

出 版 物:《IEEE ACCESS》 (IEEE Access)

年 卷 期:2025年第13卷

页      面:7801-7818页

核心收录:

基  金:National Science, Research, and Innovation Fund (NSRF) King Mongkut's University of Technology North Bangkok [KMUTNB-FF-68-B-15] 

主  题:Optimization Deep learning Real-time systems Data models Costs Predictive models Vehicle dynamics Resource management Optimization models Manuals Electronic toll collection (ETC) deep learning multi-objective optimization queue length prediction dynamic lane allocation toll plaza optimization real-time traffic data comparative visualization 

摘      要:Managing the integration of Electronic Toll Collection (ETC) and Manual Toll Collection (MTC) systems is complex, especially during peak hours due to high traffic volumes. While ETC is more efficient, an inappropriate proportion of ETC lanes can increase congestion, making optimal ETC lane selection crucial. This paper proposes a novel framework combining deep learning and multi-objective optimization to improve toll plaza efficiency. A Gated Recurrent Unit (GRU) model with Optuna hyperparameter tuning predicts average queue lengths for various ETC lane proportions. The predictions guide the identification of the ideal ETC lane configuration to minimize queue lengths and balance traffic flow between MTC and ETC lanes. The framework dynamically adjusts ETC lane proportions based on real-time traffic data to ensure optimal lane allocations during peak hours. A multi-objective optimization function is applied to balance key objectives such as minimizing queue length, reducing queue time, lowering operational costs, and optimizing ETC utilization. Simulations with real-world data from high-traffic toll plazas demonstrate the framework s effectiveness, reducing queue lengths by up to 95.03% during peak hours, decreasing operational costs by 28.72%, and improving overall toll plaza performance. The results are visualized with graphs showing predicted average queue lengths across different ETC lane proportions, aiding stakeholders in understanding the relationship between ETC adoption and congestion. The framework provides insights into operational costs and resource allocation, enhancing financial and operational planning. Validated through extensive simulations with real-time data, this research advances empirical studies with thorough model validation and introduces innovative visualization techniques for toll plaza management.

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