Automatic Estimation Method for Intersection Saturation Flow Rate Based on Video Detector Data
Saturation flow rate (SFR) is a fundamental parameter to the level of service evaluation, lane capacity calculation, and signal timing plan optimization at signalized intersections. It is affected by a variety of factors including weather conditions, lane width, and the type of the driver. How to ac...
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2018/8353084 |
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| _version_ | 1849692557092585472 |
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| author | Linhong Wang Yunhao Wang Yiming Bie |
| author_facet | Linhong Wang Yunhao Wang Yiming Bie |
| author_sort | Linhong Wang |
| collection | DOAJ |
| description | Saturation flow rate (SFR) is a fundamental parameter to the level of service evaluation, lane capacity calculation, and signal timing plan optimization at signalized intersections. It is affected by a variety of factors including weather conditions, lane width, and the type of the driver. How to accurately estimate the SFR remains one of the most important tasks in traffic engineering. Existing studies generally rely on the field measurement method which requires a large number of people collecting data at the intersection. As a result, the method incurs a high economic cost and cannot adapt to the dynamic change of SFR. In recent years, video detectors have been widely installed at intersections which are capable of recording the time each vehicle passes the stop line, the number plate of each vehicle, and the vehicle type. This paper therefore aims to propose an automatic estimation method for the SFR based on video detector data in order to overcome the limitation of the field measurement method. A prerequisite for estimating the SFR is to recognize the saturation headway. We consider the actual vehicle headway as time series and build an auxiliary regression equation whose parameters are estimated through the ordinary least squares method. We employ the Dickey-Fuller test to verify whether the headways in the time series are saturation headways. An iterative method using quantiles is proposed to filter out abnormal data. The SFR is finally calculated using the average value of saturation headways. To demonstrate the proposed method, we conduct a case study using data from an intersection with three entrance lanes in Qujing city, Yunnan Province, China. The overall estimation process is displayed and the impacts of quantile selection and data duration on the estimation accuracy are analyzed. |
| format | Article |
| id | doaj-art-e9f900de4bce43c7bbb5778624981a13 |
| institution | DOAJ |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-e9f900de4bce43c7bbb5778624981a132025-08-20T03:20:40ZengWileyJournal of Advanced Transportation0197-67292042-31952018-01-01201810.1155/2018/83530848353084Automatic Estimation Method for Intersection Saturation Flow Rate Based on Video Detector DataLinhong Wang0Yunhao Wang1Yiming Bie2School of Transportation, Jilin University, Changchun 130022, ChinaSchool of Economics, Northeast Normal University, Changchun 130117, ChinaSchool of Transportation, Jilin University, Changchun 130022, ChinaSaturation flow rate (SFR) is a fundamental parameter to the level of service evaluation, lane capacity calculation, and signal timing plan optimization at signalized intersections. It is affected by a variety of factors including weather conditions, lane width, and the type of the driver. How to accurately estimate the SFR remains one of the most important tasks in traffic engineering. Existing studies generally rely on the field measurement method which requires a large number of people collecting data at the intersection. As a result, the method incurs a high economic cost and cannot adapt to the dynamic change of SFR. In recent years, video detectors have been widely installed at intersections which are capable of recording the time each vehicle passes the stop line, the number plate of each vehicle, and the vehicle type. This paper therefore aims to propose an automatic estimation method for the SFR based on video detector data in order to overcome the limitation of the field measurement method. A prerequisite for estimating the SFR is to recognize the saturation headway. We consider the actual vehicle headway as time series and build an auxiliary regression equation whose parameters are estimated through the ordinary least squares method. We employ the Dickey-Fuller test to verify whether the headways in the time series are saturation headways. An iterative method using quantiles is proposed to filter out abnormal data. The SFR is finally calculated using the average value of saturation headways. To demonstrate the proposed method, we conduct a case study using data from an intersection with three entrance lanes in Qujing city, Yunnan Province, China. The overall estimation process is displayed and the impacts of quantile selection and data duration on the estimation accuracy are analyzed.http://dx.doi.org/10.1155/2018/8353084 |
| spellingShingle | Linhong Wang Yunhao Wang Yiming Bie Automatic Estimation Method for Intersection Saturation Flow Rate Based on Video Detector Data Journal of Advanced Transportation |
| title | Automatic Estimation Method for Intersection Saturation Flow Rate Based on Video Detector Data |
| title_full | Automatic Estimation Method for Intersection Saturation Flow Rate Based on Video Detector Data |
| title_fullStr | Automatic Estimation Method for Intersection Saturation Flow Rate Based on Video Detector Data |
| title_full_unstemmed | Automatic Estimation Method for Intersection Saturation Flow Rate Based on Video Detector Data |
| title_short | Automatic Estimation Method for Intersection Saturation Flow Rate Based on Video Detector Data |
| title_sort | automatic estimation method for intersection saturation flow rate based on video detector data |
| url | http://dx.doi.org/10.1155/2018/8353084 |
| work_keys_str_mv | AT linhongwang automaticestimationmethodforintersectionsaturationflowratebasedonvideodetectordata AT yunhaowang automaticestimationmethodforintersectionsaturationflowratebasedonvideodetectordata AT yimingbie automaticestimationmethodforintersectionsaturationflowratebasedonvideodetectordata |