Lane-based real-time queue length estimation using license plate recognition data

•Utilizing license plate recognition (LPR) data for arterial queue length estimation.•Hybrid framework combines both machine learning techniques and traffic flow theory.•Detailed lane level queue length estimation can be obtained efficiently in real time.•Real world LPR data from two field experimen...

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Vydané v:	Transportation research. Part C, Emerging technologies Ročník 57; s. 85 - 102
Hlavní autori:	Zhan, Xianyuan, Li, Ruimin, Ukkusuri, Satish V.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier India Pvt Ltd 01.08.2015
Predmet:	Boundary constrained car-following model China Cumulative arrival–departure curve Data sources Estimating Gaussian process Ground truth Lane-based estimation Lanes License plate recognition data Queues Real time Real-time queue length estimation Recognition Real-time queue length estimation Boundary constrained car-following model Gaussian process Cumulative arrival–departure curve License plate recognition data Lane-based estimation
ISSN:	0968-090X, 1879-2359
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Abstract	•Utilizing license plate recognition (LPR) data for arterial queue length estimation.•Hybrid framework combines both machine learning techniques and traffic flow theory.•Detailed lane level queue length estimation can be obtained efficiently in real time.•Real world LPR data from two field experiments are used for testing and validation.•Reasonable estimation accuracy achieved even using LPR data without high precision. License plate recognition (LPR) data are emerging data sources that provide rich information in estimating the traffic conditions of urban arterials. While large-scale LPR system is not common in US, last few years have seen rapid developments and implementations in many other parts of world (e.g. China, Thailand and Middle East). Due to privacy issues, LPR data are seldom available to research communities. However, when available, this data source can be valuable in estimating real-time operational metrics in transportation systems. This paper proposes a lane-based real-time queue length estimation model using the license plate recognition (LPR) data. In the model, an interpolation method based on Gaussian process is developed to reconstruct the equivalent cumulative arrival–departure curve for each lane. The missing information for unrecognized or unmatched vehicles is obtained from the reconstructed arrival curve. With the complete arrival and departure information, a car-following based simulation scheme is applied to estimate the real-time queue length for each lane. The proposed model is validated using ground truth information of the maximum queue lengths from the city of Langfang in China. The results show that the model can capture the variations in queue lengths in the ground truth data, and the maximum queue length for each signal cycle can be estimated with a reasonable accuracy. The estimated queue length information using the proposed model can serve as a useful performance metric for various real-time traffic control applications.
AbstractList	•Utilizing license plate recognition (LPR) data for arterial queue length estimation.•Hybrid framework combines both machine learning techniques and traffic flow theory.•Detailed lane level queue length estimation can be obtained efficiently in real time.•Real world LPR data from two field experiments are used for testing and validation.•Reasonable estimation accuracy achieved even using LPR data without high precision. License plate recognition (LPR) data are emerging data sources that provide rich information in estimating the traffic conditions of urban arterials. While large-scale LPR system is not common in US, last few years have seen rapid developments and implementations in many other parts of world (e.g. China, Thailand and Middle East). Due to privacy issues, LPR data are seldom available to research communities. However, when available, this data source can be valuable in estimating real-time operational metrics in transportation systems. This paper proposes a lane-based real-time queue length estimation model using the license plate recognition (LPR) data. In the model, an interpolation method based on Gaussian process is developed to reconstruct the equivalent cumulative arrival–departure curve for each lane. The missing information for unrecognized or unmatched vehicles is obtained from the reconstructed arrival curve. With the complete arrival and departure information, a car-following based simulation scheme is applied to estimate the real-time queue length for each lane. The proposed model is validated using ground truth information of the maximum queue lengths from the city of Langfang in China. The results show that the model can capture the variations in queue lengths in the ground truth data, and the maximum queue length for each signal cycle can be estimated with a reasonable accuracy. The estimated queue length information using the proposed model can serve as a useful performance metric for various real-time traffic control applications. License plate recognition (LPR) data are emerging data sources that provide rich information in estimating the traffic conditions of urban arterials. While large-scale LPR system is not common in US, last few years have seen rapid developments and implementations in many other parts of world (e.g. China, Thailand and Middle East). Due to privacy issues, LPR data are seldom available to research communities. However, when available, this data source can be valuable in estimating real-time operational metrics in transportation systems. This paper proposes a lane-based real-time queue length estimation model using the license plate recognition (LPR) data. In the model, an interpolation method based on Gaussian process is developed to reconstruct the equivalent cumulative arrival-departure curve for each lane. The missing information for unrecognized or unmatched vehicles is obtained from the reconstructed arrival curve. With the complete arrival and departure information, a car-following based simulation scheme is applied to estimate the real-time queue length for each lane. The proposed model is validated using ground truth information of the maximum queue lengths from the city of Langfang in China. The results show that the model can capture the variations in queue lengths in the ground truth data, and the maximum queue length for each signal cycle can be estimated with a reasonable accuracy. The estimated queue length information using the proposed model can serve as a useful performance metric for various real-time traffic control applications.
Author	Li, Ruimin Zhan, Xianyuan Ukkusuri, Satish V.
Author_xml	– sequence: 1 givenname: Xianyuan surname: Zhan fullname: Zhan, Xianyuan email: zhanxianyuan@purdue.edu organization: Lyles School of Civil Engineering, Purdue University, 550 Stadium Drive, West Lafayette, IN 47907, USA – sequence: 2 givenname: Ruimin surname: Li fullname: Li, Ruimin email: lrmin@tsinghua.edu.cn organization: Department of Civil Engineering, Tsinghua University, Beijing 100084, China – sequence: 3 givenname: Satish V. surname: Ukkusuri fullname: Ukkusuri, Satish V. email: sukkusur@purdue.edu organization: Lyles School of Civil Engineering, Purdue University, 550 Stadium Drive, West Lafayette, IN 47907, USA
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Keywords	Real-time queue length estimation Boundary constrained car-following model Gaussian process Cumulative arrival–departure curve License plate recognition data Lane-based estimation
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Snippet	•Utilizing license plate recognition (LPR) data for arterial queue length estimation.•Hybrid framework combines both machine learning techniques and traffic... License plate recognition (LPR) data are emerging data sources that provide rich information in estimating the traffic conditions of urban arterials. While...
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SubjectTerms	Boundary constrained car-following model China Cumulative arrival–departure curve Data sources Estimating Gaussian process Ground truth Lane-based estimation Lanes License plate recognition data Queues Real time Real-time queue length estimation Recognition
Title	Lane-based real-time queue length estimation using license plate recognition data
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