Academic Journal
Real-Time Traffic Arrival Prediction for Intelligent Signal Control Using a Hidden Markov Model-Filtered Dynamic Platoon Dispersion Model and Automatic License Plate Recognition Data.
| Τίτλος: | Real-Time Traffic Arrival Prediction for Intelligent Signal Control Using a Hidden Markov Model-Filtered Dynamic Platoon Dispersion Model and Automatic License Plate Recognition Data. |
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| Συγγραφείς: | Qin, Hanwu, Wang, Dianhai, Cai, Zhengyi, Zeng, Jiaqi |
| Πηγή: | Applied Sciences (2076-3417); Nov2025, Vol. 15 Issue 21, p11537, 20p |
| Θεματικοί όροι: | Hidden Markov models, Automobile license plates, Traffic flow, Data quality, Traffic engineering, Traffic estimation |
| Περίληψη: | Accurate prediction of downstream vehicle arrivals is pivotal for intelligent signal control, yet many advanced controllers depend on high-resolution trajectories that are rarely available outside connected-vehicle settings. We present a deployable alternative that converts ubiquitous Automatic License Plate Recognition (ALPR) timestamps into the predictive inputs required by modern controllers. The method couples a Hidden Markov Model (HMM) for separating free-flow samples from signal-induced delays with a dynamic platoon-dispersion model that is re-estimated online in a rolling window to forecast downstream arrival profiles in real time. In a Simulation of Urban Mobility (SUMO) corridor testbed, the proposed framework consistently outperforms fixed-kernel dispersion and fixed-travel-time baselines, reducing RMSE by 57–75% and MAE by 53–73% across demand levels; ablation results confirm that HMM-based filtering is the dominant contributor to the gains. Robustness experiments further show stable parameter estimation under low ALPR matching rates, indicating suitability for real-world conditions where data quality fluctuates. Because it operates with existing roadside cameras and lightweight inference, the framework is readily integrable into adaptive signal strategies and broader smart-city traffic management. By turning discrete ALPR events into reliable arrival predictions, it bridges the gap between advanced signal control and today's sensing infrastructure, enabling cost-effective real-time signal optimization in data-constrained urban networks. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Real-Time Traffic Arrival Prediction for Intelligent Signal Control Using a Hidden Markov Model-Filtered Dynamic Platoon Dispersion Model and Automatic License Plate Recognition Data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Qin%2C+Hanwu%22">Qin, Hanwu</searchLink><br /><searchLink fieldCode="AR" term="%22Wang%2C+Dianhai%22">Wang, Dianhai</searchLink><br /><searchLink fieldCode="AR" term="%22Cai%2C+Zhengyi%22">Cai, Zhengyi</searchLink><br /><searchLink fieldCode="AR" term="%22Zeng%2C+Jiaqi%22">Zeng, Jiaqi</searchLink> – Name: TitleSource Label: Source Group: Src Data: Applied Sciences (2076-3417); Nov2025, Vol. 15 Issue 21, p11537, 20p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Hidden+Markov+models%22">Hidden Markov models</searchLink><br /><searchLink fieldCode="DE" term="%22Automobile+license+plates%22">Automobile license plates</searchLink><br /><searchLink fieldCode="DE" term="%22Traffic+flow%22">Traffic flow</searchLink><br /><searchLink fieldCode="DE" term="%22Data+quality%22">Data quality</searchLink><br /><searchLink fieldCode="DE" term="%22Traffic+engineering%22">Traffic engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Traffic+estimation%22">Traffic estimation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Accurate prediction of downstream vehicle arrivals is pivotal for intelligent signal control, yet many advanced controllers depend on high-resolution trajectories that are rarely available outside connected-vehicle settings. We present a deployable alternative that converts ubiquitous Automatic License Plate Recognition (ALPR) timestamps into the predictive inputs required by modern controllers. The method couples a Hidden Markov Model (HMM) for separating free-flow samples from signal-induced delays with a dynamic platoon-dispersion model that is re-estimated online in a rolling window to forecast downstream arrival profiles in real time. In a Simulation of Urban Mobility (SUMO) corridor testbed, the proposed framework consistently outperforms fixed-kernel dispersion and fixed-travel-time baselines, reducing RMSE by 57–75% and MAE by 53–73% across demand levels; ablation results confirm that HMM-based filtering is the dominant contributor to the gains. Robustness experiments further show stable parameter estimation under low ALPR matching rates, indicating suitability for real-world conditions where data quality fluctuates. Because it operates with existing roadside cameras and lightweight inference, the framework is readily integrable into adaptive signal strategies and broader smart-city traffic management. By turning discrete ALPR events into reliable arrival predictions, it bridges the gap between advanced signal control and today's sensing infrastructure, enabling cost-effective real-time signal optimization in data-constrained urban networks. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Applied Sciences (2076-3417) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/app152111537 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 11537 Subjects: – SubjectFull: Hidden Markov models Type: general – SubjectFull: Automobile license plates Type: general – SubjectFull: Traffic flow Type: general – SubjectFull: Data quality Type: general – SubjectFull: Traffic engineering Type: general – SubjectFull: Traffic estimation Type: general Titles: – TitleFull: Real-Time Traffic Arrival Prediction for Intelligent Signal Control Using a Hidden Markov Model-Filtered Dynamic Platoon Dispersion Model and Automatic License Plate Recognition Data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Qin, Hanwu – PersonEntity: Name: NameFull: Wang, Dianhai – PersonEntity: Name: NameFull: Cai, Zhengyi – PersonEntity: Name: NameFull: Zeng, Jiaqi IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 20763417 Numbering: – Type: volume Value: 15 – Type: issue Value: 21 Titles: – TitleFull: Applied Sciences (2076-3417) Type: main |
| ResultId | 1 |