Development of a Traffic Congestion Prediction and Emergency Lane Development Strategy Based on Object Detection Algorithms

With rapid economic development and a continuous increase in motor vehicle numbers, traffic congestion on highways has become increasingly severe, significantly impacting traffic efficiency and public safety. This paper proposes and investigates a traffic congestion prediction and emergency lane dev...

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Vydáno v:	Sustainability Ročník 16; číslo 23; s. 10232
Hlavní autoři:	Zhang, Chaokai, Cheng, Hao, Wu, Rui, Ren, Biyun, Zhu, Ye, Peng, Ningbo
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Basel MDPI AG 01.12.2024
Témata:	Accuracy Algorithms Analysis Cameras Data processing Decision making Deep learning Design Emergency transportation Energy consumption Forecasts and trends Highway construction Machine learning Management Methods Quality of life Queuing theory Software Surveillance Traffic congestion Traffic engineering Traffic flow Vehicles China
ISSN:	2071-1050, 2071-1050
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Abstract	With rapid economic development and a continuous increase in motor vehicle numbers, traffic congestion on highways has become increasingly severe, significantly impacting traffic efficiency and public safety. This paper proposes and investigates a traffic congestion prediction and emergency lane development strategy based on object detection algorithms. Firstly, the YOLOv11 object detection algorithm combined with the ByteTrack multi-object tracking algorithm is employed to extract traffic flow parameters—including traffic volume, speed, and density—from videos at four monitoring points on the Changshen Expressway in Nanjing City, Jiangsu Province, China. Subsequently, using an AdaBoost regression model, the traffic density of downstream road sections is predicted based on the density features of upstream sections. The model achieves a coefficient of determination R2 of 0.968, a mean absolute error of 11.2 vehicles/km, and a root mean square error of 19.9 vehicles/km, indicating high prediction accuracy. Building on the interval occupancy rate model, this paper further analyzes the causes of traffic congestion and designs decision-making processes for the activation and deactivation of emergency lanes. By real-time monitoring and calculating the vehicle occupancy rate of the CD interval, threshold conditions for activating emergency lanes are determined. When the interval occupancy rate KCD(t) exceeds 80%, the emergency lane is proactively opened. This method effectively alleviates traffic congestion and reduces congestion duration. Quantitative analysis shows that after activating the emergency lane, the congestion duration in the CD section decreases from 58 min to 30 min, the peak occupancy rate drops from 1 to 0.917, and the congestion duration is shortened by 48.3%. Additionally, for the Changshen Expressway, this paper proposes two optimization points for monitoring point layout, including setting up monitoring points in downstream sections and in the middle of the CD section, to further enhance the scientific and rational management of emergency lanes. The proposed strategy not only improves the real-time extraction and prediction accuracy of traffic flow parameters but also achieves dynamic management of emergency lanes through the interval occupancy rate model, thereby alleviating highway traffic congestion. This has significant practical application value.
AbstractList	With rapid economic development and a continuous increase in motor vehicle numbers, traffic congestion on highways has become increasingly severe, significantly impacting traffic efficiency and public safety. This paper proposes and investigates a traffic congestion prediction and emergency lane development strategy based on object detection algorithms. Firstly, the YOLOv11 object detection algorithm combined with the ByteTrack multi-object tracking algorithm is employed to extract traffic flow parameters—including traffic volume, speed, and density—from videos at four monitoring points on the Changshen Expressway in Nanjing City, Jiangsu Province, China. Subsequently, using an AdaBoost regression model, the traffic density of downstream road sections is predicted based on the density features of upstream sections. The model achieves a coefficient of determination R2 of 0.968, a mean absolute error of 11.2 vehicles/km, and a root mean square error of 19.9 vehicles/km, indicating high prediction accuracy. Building on the interval occupancy rate model, this paper further analyzes the causes of traffic congestion and designs decision-making processes for the activation and deactivation of emergency lanes. By real-time monitoring and calculating the vehicle occupancy rate of the CD interval, threshold conditions for activating emergency lanes are determined. When the interval occupancy rate KCD(t) exceeds 80%, the emergency lane is proactively opened. This method effectively alleviates traffic congestion and reduces congestion duration. Quantitative analysis shows that after activating the emergency lane, the congestion duration in the CD section decreases from 58 min to 30 min, the peak occupancy rate drops from 1 to 0.917, and the congestion duration is shortened by 48.3%. Additionally, for the Changshen Expressway, this paper proposes two optimization points for monitoring point layout, including setting up monitoring points in downstream sections and in the middle of the CD section, to further enhance the scientific and rational management of emergency lanes. The proposed strategy not only improves the real-time extraction and prediction accuracy of traffic flow parameters but also achieves dynamic management of emergency lanes through the interval occupancy rate model, thereby alleviating highway traffic congestion. This has significant practical application value. With rapid economic development and a continuous increase in motor vehicle numbers, traffic congestion on highways has become increasingly severe, significantly impacting traffic efficiency and public safety. This paper proposes and investigates a traffic congestion prediction and emergency lane development strategy based on object detection algorithms. Firstly, the YOLOv11 object detection algorithm combined with the ByteTrack multi-object tracking algorithm is employed to extract traffic flow parameters—including traffic volume, speed, and density—from videos at four monitoring points on the Changshen Expressway in Nanjing City, Jiangsu Province, China. Subsequently, using an AdaBoost regression model, the traffic density of downstream road sections is predicted based on the density features of upstream sections. The model achieves a coefficient of determination R[sup.2] of 0.968, a mean absolute error of 11.2 vehicles/km, and a root mean square error of 19.9 vehicles/km, indicating high prediction accuracy. Building on the interval occupancy rate model, this paper further analyzes the causes of traffic congestion and designs decision-making processes for the activation and deactivation of emergency lanes. By real-time monitoring and calculating the vehicle occupancy rate of the CD interval, threshold conditions for activating emergency lanes are determined. When the interval occupancy rate K[sub.CD] (t) exceeds 80%, the emergency lane is proactively opened. This method effectively alleviates traffic congestion and reduces congestion duration. Quantitative analysis shows that after activating the emergency lane, the congestion duration in the CD section decreases from 58 min to 30 min, the peak occupancy rate drops from 1 to 0.917, and the congestion duration is shortened by 48.3%. Additionally, for the Changshen Expressway, this paper proposes two optimization points for monitoring point layout, including setting up monitoring points in downstream sections and in the middle of the CD section, to further enhance the scientific and rational management of emergency lanes. The proposed strategy not only improves the real-time extraction and prediction accuracy of traffic flow parameters but also achieves dynamic management of emergency lanes through the interval occupancy rate model, thereby alleviating highway traffic congestion. This has significant practical application value.
Audience	Academic
Author	Zhang, Chaokai Cheng, Hao Zhu, Ye Wu, Rui Ren, Biyun Peng, Ningbo
Author_xml	– sequence: 1 givenname: Chaokai surname: Zhang fullname: Zhang, Chaokai – sequence: 2 givenname: Hao orcidid: 0000-0002-9570-7417 surname: Cheng fullname: Cheng, Hao – sequence: 3 givenname: Rui surname: Wu fullname: Wu, Rui – sequence: 4 givenname: Biyun surname: Ren fullname: Ren, Biyun – sequence: 5 givenname: Ye orcidid: 0000-0003-3053-1585 surname: Zhu fullname: Zhu, Ye – sequence: 6 givenname: Ningbo orcidid: 0000-0002-1006-4409 surname: Peng fullname: Peng, Ningbo
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SubjectTerms	Accuracy Algorithms Analysis Cameras Data processing Decision making Deep learning Design Emergency transportation Energy consumption Forecasts and trends Highway construction Machine learning Management Methods Quality of life Queuing theory Software Surveillance Traffic congestion Traffic engineering Traffic flow Vehicles
Title	Development of a Traffic Congestion Prediction and Emergency Lane Development Strategy Based on Object Detection Algorithms
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