Lane-Based Traffic Arrival Pattern Estimation Using License Plate Recognition Data

An, Chengchuan; Guo, Xiaoyu; Hong, Rongrong; Lu, Zhenbo; Xia, Jingxin

doi:10.1109/mits.2021.3051489

Cited by 11 publications

(3 citation statements)

References 18 publications

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“…This method used a probability model and assumed the upstream merging motion as a two-stage segmented arrival process. The research results showed that this method could describe traffic arrival models under different traffic scenarios [13]. To detect outlier in traffic flow (TF), X. Wang and other scholars proposed an efficient traffic anomaly detection framework.…”

Section: Related Workmentioning

confidence: 99%

Construction of Dynamic Traffic Pattern Recognition and Prediction Model Based on Deep Learning in the Background of Intelligent Cities

Cui,

Zhao

2024

IEEE Access

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To reduce traffic congestion, it is particularly important to use advanced technology to predict urban traffic flow. Therefore, a dynamic traffic pattern prediction model is proposed, which includes convolutional neural network, long and short term memory network and attention mechanism. The validity of the prediction model is verified by the loss function and the average absolute percentage error. In addition, the study also constructs a model for user travel pattern and parking point recognition based on deep learning and mobile signaling data. The performance of the recognition model is verified by the accuracy and other indicators. The research outcomes demonstrated that the max average absolute percentage error of the dynamic traffic mode prediction model was 7.8%, and the mini value was 2.9%. The average accuracy of the user travel pattern recognition model was 83.34%, and that of the parking point recognition model was 88.56%.The dynamic traffic model recognition and prediction model designed by the research institute has better results, and has practical guiding significance in smart city traffic management.

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Section: Related Workmentioning

confidence: 99%

Construction of Dynamic Traffic Pattern Recognition and Prediction Model Based on Deep Learning in the Background of Intelligent Cities

Cui,

Zhao

2024

IEEE Access

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“…People have subjective factors that interfere with the division of motion cycles. In this study, we propose a traffic cycle allocation method based on an image segmentation algorithm [19].…”

Section: Traffic Period Optimization Algorithmmentioning

confidence: 99%

Traffic Optimization Model Based on Regional Road Network Traffic Diversion Technology and Internet of Things

2022

Mobile Information Systems

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Today’s traffic control system is developing in a more intelligent direction. The progress of electronic technology has created a traffic control and management system using a variety of control methods. The development of computers creates a more intelligent traffic optimization model for optimizing network traffic. In the limited road space, traffic control research will focus on maximizing the use of existing traffic management equipment to improve vehicle circulation efficiency and avoid congestion. In this context, combined with the temporal and spatial characteristics of traffic congestion, this study investigates the storage capacity of existing road space and seeks the optimal control method for local regional road network congestion. For the technical problems of traffic diversion in the existing two-phase control method and the queuing order in the adjacent lanes in the four-phase control method, the optimal control method is proposed, which solves the technical problems of traffic control and the phenomenon of “queuing injustice” in the adjacent lanes in one direction. The safety degree and traffic efficiency of vehicle operation are improved. The traffic flow from the same entrance to the main intersection forms a signal phase while slowing down or blocking these traffic flows can effectively improve traffic safety and traffic efficiency. Combined with the development of traditional traffic control system, this study introduces the development status of Internet of things technology and puts forward an intelligent traffic control system based on Internet of things technology. Real-time vehicle traffic information is sent to the Lora gateway through Lora wireless network, which is responsible for data transmission and protocol conversion.

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“…With the ubiquitous application of automatic vehicle identification (AVI) technology, location-specific license plates can be collected with high real-time and accuracy from vehicles within the urban road network [30]. An et al [31] used the location-specific license plates recognised by AVI not only to collect high real-time information from vehicles but also to estimate a lane-based traffic arrival pattern. Given its rich spatial-temporal information, AVI data has been widely used to provide more advanced intelligent transportation services [32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Relieving the Congestion around a School via Automatic Vehicle Identification Technology-Based Traffic Measures

Sun³

et al. 2021

Journal of Advanced Transportation

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Recently, many parents drive their children to and from schools, leading to serious road congestion around the school gate. The school-related congestion is a special type of congestion caused by periodic impulsive aggregation of specific travellers for certain events. In this study, the individual long short-term traffic behaviours were reconstructed based on automatic vehicle identification (AVI) technologies. The cause and countermeasure of congestion around the service centers were identified through the individual behavioural properties. The vehicles that were primarily responsible for periodic impulsive aggregation congestion (PIAC) around the school gate were precisely targeted via a proposed vehicle grading clustering framework. The road management objectives were updated in the AVI data environment and it was found that only 3%–5% of the total number of vehicles passing by the school gate require specific management such as traffic enforcement activities. A series of traffic measures were formulated based on the results of vehicle grading clustering and achieved positive effects in a periodic impulsive aggregation area. It is an effective way to solve the PIAC by formulating management with different activity levels and resolutions for specific travellers. The methodologies and experience presented in this study may provide a useful tool for relieving such special type of congestion around other service centers faced with similar scenarios.

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Lane-Based Traffic Arrival Pattern Estimation Using License Plate Recognition Data

Cited by 11 publications

References 18 publications

Construction of Dynamic Traffic Pattern Recognition and Prediction Model Based on Deep Learning in the Background of Intelligent Cities

Construction of Dynamic Traffic Pattern Recognition and Prediction Model Based on Deep Learning in the Background of Intelligent Cities

Traffic Optimization Model Based on Regional Road Network Traffic Diversion Technology and Internet of Things

Relieving the Congestion around a School via Automatic Vehicle Identification Technology-Based Traffic Measures

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