Vehicle trajectory based control delay estimation at intersections using low-frequency floating car sampling data
Control delay is an important parameter that is used in the optimization of traffic signal timings and the estimation of the level of service at signalized intersection. However, it is also a parameter that is very difficult to estimate. In recent years, floating car data has emerged as an important...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Vilnius Gediminas Technical University
2020-12-01
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| Series: | Transport |
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| Online Access: | https://journals.vgtu.lt/index.php/Transport/article/view/11962 |
| _version_ | 1818438020623237120 |
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| author | Hua Wang Changlong Gu |
| author_facet | Hua Wang Changlong Gu |
| author_sort | Hua Wang |
| collection | DOAJ |
| description | Control delay is an important parameter that is used in the optimization of traffic signal timings and the estimation of the level of service at signalized intersection. However, it is also a parameter that is very difficult to estimate. In recent years, floating car data has emerged as an important data source for traffic state monitoring as a result of high accuracy, wide coverage and availability regardless of meteorological conditions, but has done little for control delay estimation. This article proposes a vehicle trajectory based control delay estimation method using low-frequency floating car data. Considering the sparseness and randomness of low-frequency floating car data, we use historical data to capture the deceleration and acceleration patterns. Combined with the low-frequency samples, the spatial and temporal ranges where a vehicle starts to decelerate and stop accelerating are calculated. These are used together with the control delay probability distribution function obtained based on the geometric probability model, to calculate the expected value of the control delay for each vehicle. The proposed method and a reference method are compared with the truth. The results show that the proposed method has a root mean square error of 11.8 s compared to 13.7 s for the reference method for the peak period. The corresponding values for the off-peak period are 9.3 s and 12.5 s. In addition to better accuracy, the mean and standard deviation statistics show that the proposed method outperforms the reference method and is therefore, more reliable. This successful estimation of control delay from sparse data paves the way for a more widespread use of floating car data for monitoring the state of intersections in road networks.
First published online 5 February 2020 |
| first_indexed | 2024-12-14T17:33:56Z |
| format | Article |
| id | doaj.art-4be2bff60a5d438db4d2ff4ba3e50d66 |
| institution | Directory Open Access Journal |
| issn | 1648-4142 1648-3480 |
| language | English |
| last_indexed | 2024-12-14T17:33:56Z |
| publishDate | 2020-12-01 |
| publisher | Vilnius Gediminas Technical University |
| record_format | Article |
| series | Transport |
| spelling | doaj.art-4be2bff60a5d438db4d2ff4ba3e50d662022-12-21T22:53:03ZengVilnius Gediminas Technical UniversityTransport1648-41421648-34802020-12-0135552353210.3846/transport.2020.1196211962Vehicle trajectory based control delay estimation at intersections using low-frequency floating car sampling dataHua Wang0Changlong Gu1Dept of Traffic Information and Control Engineering, Harbin Institute of Technology, ChinaDept of Traffic Information and Control Engineering, Harbin Institute of Technology, ChinaControl delay is an important parameter that is used in the optimization of traffic signal timings and the estimation of the level of service at signalized intersection. However, it is also a parameter that is very difficult to estimate. In recent years, floating car data has emerged as an important data source for traffic state monitoring as a result of high accuracy, wide coverage and availability regardless of meteorological conditions, but has done little for control delay estimation. This article proposes a vehicle trajectory based control delay estimation method using low-frequency floating car data. Considering the sparseness and randomness of low-frequency floating car data, we use historical data to capture the deceleration and acceleration patterns. Combined with the low-frequency samples, the spatial and temporal ranges where a vehicle starts to decelerate and stop accelerating are calculated. These are used together with the control delay probability distribution function obtained based on the geometric probability model, to calculate the expected value of the control delay for each vehicle. The proposed method and a reference method are compared with the truth. The results show that the proposed method has a root mean square error of 11.8 s compared to 13.7 s for the reference method for the peak period. The corresponding values for the off-peak period are 9.3 s and 12.5 s. In addition to better accuracy, the mean and standard deviation statistics show that the proposed method outperforms the reference method and is therefore, more reliable. This successful estimation of control delay from sparse data paves the way for a more widespread use of floating car data for monitoring the state of intersections in road networks. First published online 5 February 2020https://journals.vgtu.lt/index.php/Transport/article/view/11962probe vehiclevehicle trajectoriestraffic control delayssignalized intersectionsglobal positioning systemtraffic engineeringcomputing |
| spellingShingle | Hua Wang Changlong Gu Vehicle trajectory based control delay estimation at intersections using low-frequency floating car sampling data Transport probe vehicle vehicle trajectories traffic control delays signalized intersections global positioning system traffic engineering computing |
| title | Vehicle trajectory based control delay estimation at intersections using low-frequency floating car sampling data |
| title_full | Vehicle trajectory based control delay estimation at intersections using low-frequency floating car sampling data |
| title_fullStr | Vehicle trajectory based control delay estimation at intersections using low-frequency floating car sampling data |
| title_full_unstemmed | Vehicle trajectory based control delay estimation at intersections using low-frequency floating car sampling data |
| title_short | Vehicle trajectory based control delay estimation at intersections using low-frequency floating car sampling data |
| title_sort | vehicle trajectory based control delay estimation at intersections using low frequency floating car sampling data |
| topic | probe vehicle vehicle trajectories traffic control delays signalized intersections global positioning system traffic engineering computing |
| url | https://journals.vgtu.lt/index.php/Transport/article/view/11962 |
| work_keys_str_mv | AT huawang vehicletrajectorybasedcontroldelayestimationatintersectionsusinglowfrequencyfloatingcarsamplingdata AT changlonggu vehicletrajectorybasedcontroldelayestimationatintersectionsusinglowfrequencyfloatingcarsamplingdata |