Vehicle Trajectory Reconstruction for Signalized Intersections with Low-Frequency Floating Car Data
Floating car data are beneficial in estimating traffic conditions in wide areas and are playing an increasing role in traffic surveillance. However, widespread application is limited by low-sample frequency which makes it hard to get a complete picture of a vehicle’s motion. An accurate and reliable...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2019/9417471 |
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| author | Hua Wang Changlong Gu Washington Yotto Ochieng |
| author_facet | Hua Wang Changlong Gu Washington Yotto Ochieng |
| author_sort | Hua Wang |
| collection | DOAJ |
| description | Floating car data are beneficial in estimating traffic conditions in wide areas and are playing an increasing role in traffic surveillance. However, widespread application is limited by low-sample frequency which makes it hard to get a complete picture of a vehicle’s motion. An accurate and reliable reconstruction of a vehicle’s trajectory could effectively result in a higher sampling frequency enabling a more accurate estimation of road traffic parameters. Existing methods require additional information such as nearby vehicles, signal timing strategies, and queue patterns which are not always available. To address this problem, this paper presents a method used with low-sample frequency data to reconstruct vehicle trajectories through intersections, without the need for extra information. Furthermore, the additional parameters for the speed-time curve distributions for deceleration rate and acceleration rate are generated. A piecewise deceleration and acceleration model is developed to calculate the acceleration rate for different travel modes in the trajectory. The distribution parameters of the acceleration data for each travel mode are then estimated using a new Expectation Maximization (EM) algorithm. The acceleration statistics are then used to reconstruct the corresponding parts of the trajectory. Compared to the reference trajectories (truth), the test results show that the method developed in this paper achieves improvement in accuracy ranging from 16 to 67% over the commonly used linear interpolation method. In addition, the proposed method is not very sensitive to the sampling interval of the floating car data, unlike the linear interpolation method where the error grows rapidly with increasing sampling interval. |
| format | Article |
| id | doaj-art-589dff2d15dd47c28f74e8e2e107dff2 |
| institution | OA Journals |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-589dff2d15dd47c28f74e8e2e107dff22025-08-20T02:05:20ZengWileyJournal of Advanced Transportation0197-67292042-31952019-01-01201910.1155/2019/94174719417471Vehicle Trajectory Reconstruction for Signalized Intersections with Low-Frequency Floating Car DataHua Wang0Changlong Gu1Washington Yotto Ochieng2School of Transportation Science and Engineering, Harbin Institute of Technology, No. 73 Huanghe Road, Nangang District, Harbin 150090, ChinaSchool of Transportation Science and Engineering, Harbin Institute of Technology, No. 73 Huanghe Road, Nangang District, Harbin 150090, ChinaDepartment of Civil and Environmental Engineering, Imperial College London, London SW7 2AZ, UKFloating car data are beneficial in estimating traffic conditions in wide areas and are playing an increasing role in traffic surveillance. However, widespread application is limited by low-sample frequency which makes it hard to get a complete picture of a vehicle’s motion. An accurate and reliable reconstruction of a vehicle’s trajectory could effectively result in a higher sampling frequency enabling a more accurate estimation of road traffic parameters. Existing methods require additional information such as nearby vehicles, signal timing strategies, and queue patterns which are not always available. To address this problem, this paper presents a method used with low-sample frequency data to reconstruct vehicle trajectories through intersections, without the need for extra information. Furthermore, the additional parameters for the speed-time curve distributions for deceleration rate and acceleration rate are generated. A piecewise deceleration and acceleration model is developed to calculate the acceleration rate for different travel modes in the trajectory. The distribution parameters of the acceleration data for each travel mode are then estimated using a new Expectation Maximization (EM) algorithm. The acceleration statistics are then used to reconstruct the corresponding parts of the trajectory. Compared to the reference trajectories (truth), the test results show that the method developed in this paper achieves improvement in accuracy ranging from 16 to 67% over the commonly used linear interpolation method. In addition, the proposed method is not very sensitive to the sampling interval of the floating car data, unlike the linear interpolation method where the error grows rapidly with increasing sampling interval.http://dx.doi.org/10.1155/2019/9417471 |
| spellingShingle | Hua Wang Changlong Gu Washington Yotto Ochieng Vehicle Trajectory Reconstruction for Signalized Intersections with Low-Frequency Floating Car Data Journal of Advanced Transportation |
| title | Vehicle Trajectory Reconstruction for Signalized Intersections with Low-Frequency Floating Car Data |
| title_full | Vehicle Trajectory Reconstruction for Signalized Intersections with Low-Frequency Floating Car Data |
| title_fullStr | Vehicle Trajectory Reconstruction for Signalized Intersections with Low-Frequency Floating Car Data |
| title_full_unstemmed | Vehicle Trajectory Reconstruction for Signalized Intersections with Low-Frequency Floating Car Data |
| title_short | Vehicle Trajectory Reconstruction for Signalized Intersections with Low-Frequency Floating Car Data |
| title_sort | vehicle trajectory reconstruction for signalized intersections with low frequency floating car data |
| url | http://dx.doi.org/10.1155/2019/9417471 |
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