A Deep Learning for Optimization and Visualization of Expressway Toll Lane Management
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...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10820321/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850117941223227392 |
|---|---|
| author | Pattarapon Klaykul Wilaiporn Lee Kanabadee Srisomboon Luepol Pipanmekaporn Akara Prayote |
| author_facet | Pattarapon Klaykul Wilaiporn Lee Kanabadee Srisomboon Luepol Pipanmekaporn Akara Prayote |
| author_sort | Pattarapon Klaykul |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e0f9bd2cec934cc08cc9f3e273b64e1d |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-e0f9bd2cec934cc08cc9f3e273b64e1d2025-08-20T02:35:59ZengIEEEIEEE Access2169-35362025-01-01137801781810.1109/ACCESS.2024.352501710820321A Deep Learning for Optimization and Visualization of Expressway Toll Lane ManagementPattarapon Klaykul0https://orcid.org/0009-0002-7066-9677Wilaiporn Lee1https://orcid.org/0000-0001-7484-0246Kanabadee Srisomboon2https://orcid.org/0000-0002-3120-3324Luepol Pipanmekaporn3https://orcid.org/0009-0005-5882-9342Akara Prayote4Department of Computer and Information Science, King Mongkut’s University of Technology North Bangkok, Bangkok, ThailandDepartment of Electrical and Computer Engineering, King Mongkut’s University of Technology North Bangkok, Bangkok, ThailandDepartment of Electrical and Computer Engineering, King Mongkut’s University of Technology North Bangkok, Bangkok, ThailandDepartment of Computer and Information Science, King Mongkut’s University of Technology North Bangkok, Bangkok, ThailandDepartment of Computer and Information Science, King Mongkut’s University of Technology North Bangkok, Bangkok, ThailandManaging 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.https://ieeexplore.ieee.org/document/10820321/Electronic toll collection (ETC)deep learningmulti-objective optimizationqueue length predictiondynamic lane allocationtoll plaza optimization |
| spellingShingle | Pattarapon Klaykul Wilaiporn Lee Kanabadee Srisomboon Luepol Pipanmekaporn Akara Prayote A Deep Learning for Optimization and Visualization of Expressway Toll Lane Management IEEE Access Electronic toll collection (ETC) deep learning multi-objective optimization queue length prediction dynamic lane allocation toll plaza optimization |
| title | A Deep Learning for Optimization and Visualization of Expressway Toll Lane Management |
| title_full | A Deep Learning for Optimization and Visualization of Expressway Toll Lane Management |
| title_fullStr | A Deep Learning for Optimization and Visualization of Expressway Toll Lane Management |
| title_full_unstemmed | A Deep Learning for Optimization and Visualization of Expressway Toll Lane Management |
| title_short | A Deep Learning for Optimization and Visualization of Expressway Toll Lane Management |
| title_sort | deep learning for optimization and visualization of expressway toll lane management |
| topic | Electronic toll collection (ETC) deep learning multi-objective optimization queue length prediction dynamic lane allocation toll plaza optimization |
| url | https://ieeexplore.ieee.org/document/10820321/ |
| work_keys_str_mv | AT pattaraponklaykul adeeplearningforoptimizationandvisualizationofexpresswaytolllanemanagement AT wilaipornlee adeeplearningforoptimizationandvisualizationofexpresswaytolllanemanagement AT kanabadeesrisomboon adeeplearningforoptimizationandvisualizationofexpresswaytolllanemanagement AT luepolpipanmekaporn adeeplearningforoptimizationandvisualizationofexpresswaytolllanemanagement AT akaraprayote adeeplearningforoptimizationandvisualizationofexpresswaytolllanemanagement AT pattaraponklaykul deeplearningforoptimizationandvisualizationofexpresswaytolllanemanagement AT wilaipornlee deeplearningforoptimizationandvisualizationofexpresswaytolllanemanagement AT kanabadeesrisomboon deeplearningforoptimizationandvisualizationofexpresswaytolllanemanagement AT luepolpipanmekaporn deeplearningforoptimizationandvisualizationofexpresswaytolllanemanagement AT akaraprayote deeplearningforoptimizationandvisualizationofexpresswaytolllanemanagement |