Traffic control optimization strategy based on license plate recognition data

Li, Ruimin; Wang, Shi; Jiao, Pengpeng; Lin, Shichao

doi:10.1016/j.jtte.2021.12.004

Cited by 3 publications

(1 citation statement)

References 33 publications

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“…Intelligent Traffic Monitoring plays a vital role in the development of smart transportation systems, aiming to enhance the efficiency, safety, and sustainability of modern urban mobility [1], [2]. By utilizing advanced technologies and data-driven approaches, intelligent traffic monitoring enables real-time analysis, prediction, and management of traffic conditions [3], [4].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning-based Framework for Vehicle License Plate Detection

Yang,

Yang

2024

IJACSA

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In the contemporary landscape of smart transportation systems, the imperative role of intelligent traffic monitoring in bolstering efficiency, safety, and sustainability cannot be overstated. Leveraging recent strides in computer vision, machine learning, and data analytics, this study addresses the pressing need for advancements in car license plate recognition within these systems. Employing an innovative approach based on the YOLOv5 architecture in deep learning, the study focuses on refining the accuracy of license plate recognition. A bespoke dataset is meticulously curated to facilitate a comprehensive evaluation of the proposed methodology, with extensive experiments conducted and metrics such as precision, recall, and F1-score employed for assessment. The outcomes underscore the efficacy of the approach in significantly enhancing the precision and accuracy of license plate recognition using performance evaluation of the proposed method. This tailored dataset ensures a rigorous evaluation, affirming the practical viability of the proposed approach in realworld scenarios. The study not only showcases the successful application of deep learning and YOLOv5 in achieving accurate license plate detection and recognition but also contributes to the broader discourse on advancing intelligent traffic monitoring for more robust and efficient smart transportation systems.

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Section: Introductionmentioning

confidence: 99%

A Deep Learning-based Framework for Vehicle License Plate Detection

Yang,

Yang

2024

IJACSA

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Extensions to traffic control modeling store-and-forward

Stoilova

Stoilov

2023

Expert Systems with Applications

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Collaborative Optimization of Dynamic Lane Assignment and Signal Timing for Incident-Affected Intersections

Wang,

Chen,

Feng

et al. 2024

Transportation Research Record: Journal of the Transportation R

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Traffic accidents can lead to rapid changes in the traffic supply and demand at urban intersections, thus causing a severe traffic supply/demand imbalance during specific periods. Responding to accidents requires dynamic and accurate adjustments to optimize intersection resources collaboratively and enhance the real-time reliability and stability of traffic flows during such accidents. Currently, research on traffic incidents rarely considers real-time data-based collaborative optimization theories. Therefore, this study, supported by real-time incident detection technology and accident data, first considers the location and intensity of traffic incidents to update dynamically the changes in intersection traffic demand and supply. Subsequently, a dynamically collaborative optimization method is proposed based on lane assignment and signal timings to minimize the sum of variances of the degree of saturation of various approach lanes. Finally, various traffic demand scenarios are set, and the effectiveness of the proposed model is validated based on numerical and sensitivity analyses. The results demonstrate that compared with signal-only optimization and the highway capacity methods (HCM), the collaborative optimization method presented in this study reduces the average vehicular delay percentages by 8.54% and 16.47%, respectively. Sensitivity analysis indicates that, under various detour rates and detour modes, collaborative optimization methods have effectively mitigated the average vehicular delay at upstream intersections to varying degrees. In the context of real-time accident response, collaborative optimization methods demonstrate a capacity to promptly address the urgency of incidents occurring and maintain a sustained reduction in overall delay levels following detours.

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Traffic control optimization strategy based on license plate recognition data

Cited by 3 publications

References 33 publications

A Deep Learning-based Framework for Vehicle License Plate Detection

A Deep Learning-based Framework for Vehicle License Plate Detection

Extensions to traffic control modeling store-and-forward

Collaborative Optimization of Dynamic Lane Assignment and Signal Timing for Incident-Affected Intersections

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