Extracting Origin-Destination with Vehicle Trajectory Data and Applying to Coordinated Ramp Metering
Ramp metering is an effective measure to alleviate freeway congestion. Traditional methods were mostly based on fixed-sensor data, by which origin-destination (OD) patterns cannot be directly collected. Nowadays, trajectory data are available to track vehicle movements. OD patterns can be estimated...
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| Main Authors: | , , , , , |
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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/10.1155/2019/8469316 |
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| _version_ | 1832568310404743168 |
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| author | Cheng Zhang Jiawen Wang Jintao Lai Xiaoguang Yang Yuelong Su Zhenning Dong |
| author_facet | Cheng Zhang Jiawen Wang Jintao Lai Xiaoguang Yang Yuelong Su Zhenning Dong |
| author_sort | Cheng Zhang |
| collection | DOAJ |
| description | Ramp metering is an effective measure to alleviate freeway congestion. Traditional methods were mostly based on fixed-sensor data, by which origin-destination (OD) patterns cannot be directly collected. Nowadays, trajectory data are available to track vehicle movements. OD patterns can be estimated with weaker assumptions and hence closer to reality. Ramp metering can be improved with this advantage. This paper extracts OD patterns with historical trajectory data. A validation test is proposed to guarantee the sample representativeness of vehicle trajectories and then implement coordinated ramp metering based on the contribution of on-ramp traffic to downstream bottleneck sections. The contribution is determined by the OD patterns. Simulation experiments are conducted under real-life scenarios. Results show that ramp metering with trajectory data increases the throughput by another 4% compared with traditional fixed-sensor data. The advantage is more significant under heavier traffic demand, where traditional control can hardly relieve the situation; in contrast, our control manages to make congestion dissipate earlier and even prevent its forming in some sections. Penetration of trajectory data influences control effects. The minimum required penetration of 4.0% is determined by a t-test and the Pearson correlation coefficient. When penetration is less than the minimum, the correlation between the estimation and the truth significantly drops, OD estimation tends to be unreliable, and control performance becomes more sensitive. The proposed approach is effective in recurrent freeway congestion with steady OD patterns. It is ready for practice and the analysis supports the real-world application. |
| format | Article |
| id | doaj-art-afe0515b78fa41f8b8e3c2de1a01044e |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-afe0515b78fa41f8b8e3c2de1a01044e2025-02-03T00:59:23ZengWileyJournal of Advanced Transportation0197-67292042-31952019-01-01201910.1155/2019/84693168469316Extracting Origin-Destination with Vehicle Trajectory Data and Applying to Coordinated Ramp MeteringCheng Zhang0Jiawen Wang1Jintao Lai2Xiaoguang Yang3Yuelong Su4Zhenning Dong5The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, 4800 Cao’an Road, Shanghai 201804, ChinaDepartment of Traffic Engineering, University of Shanghai for Science and Technology, 516 Jungong Road, Shanghai 200093, ChinaThe Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, 4800 Cao’an Road, Shanghai 201804, ChinaThe Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, 4800 Cao’an Road, Shanghai 201804, ChinaAutoNavi Software Co., Beijing 100102, ChinaAutoNavi Software Co., Beijing 100102, ChinaRamp metering is an effective measure to alleviate freeway congestion. Traditional methods were mostly based on fixed-sensor data, by which origin-destination (OD) patterns cannot be directly collected. Nowadays, trajectory data are available to track vehicle movements. OD patterns can be estimated with weaker assumptions and hence closer to reality. Ramp metering can be improved with this advantage. This paper extracts OD patterns with historical trajectory data. A validation test is proposed to guarantee the sample representativeness of vehicle trajectories and then implement coordinated ramp metering based on the contribution of on-ramp traffic to downstream bottleneck sections. The contribution is determined by the OD patterns. Simulation experiments are conducted under real-life scenarios. Results show that ramp metering with trajectory data increases the throughput by another 4% compared with traditional fixed-sensor data. The advantage is more significant under heavier traffic demand, where traditional control can hardly relieve the situation; in contrast, our control manages to make congestion dissipate earlier and even prevent its forming in some sections. Penetration of trajectory data influences control effects. The minimum required penetration of 4.0% is determined by a t-test and the Pearson correlation coefficient. When penetration is less than the minimum, the correlation between the estimation and the truth significantly drops, OD estimation tends to be unreliable, and control performance becomes more sensitive. The proposed approach is effective in recurrent freeway congestion with steady OD patterns. It is ready for practice and the analysis supports the real-world application.http://dx.doi.org/10.1155/2019/8469316 |
| spellingShingle | Cheng Zhang Jiawen Wang Jintao Lai Xiaoguang Yang Yuelong Su Zhenning Dong Extracting Origin-Destination with Vehicle Trajectory Data and Applying to Coordinated Ramp Metering Journal of Advanced Transportation |
| title | Extracting Origin-Destination with Vehicle Trajectory Data and Applying to Coordinated Ramp Metering |
| title_full | Extracting Origin-Destination with Vehicle Trajectory Data and Applying to Coordinated Ramp Metering |
| title_fullStr | Extracting Origin-Destination with Vehicle Trajectory Data and Applying to Coordinated Ramp Metering |
| title_full_unstemmed | Extracting Origin-Destination with Vehicle Trajectory Data and Applying to Coordinated Ramp Metering |
| title_short | Extracting Origin-Destination with Vehicle Trajectory Data and Applying to Coordinated Ramp Metering |
| title_sort | extracting origin destination with vehicle trajectory data and applying to coordinated ramp metering |
| url | http://dx.doi.org/10.1155/2019/8469316 |
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