Development of a Turning Movement Estimator Using CV Data
Turning movement (TM) data of vehicular traffic at intersections are a basic input for signal timing design. Existing methods of collecting TM data are time- and cost-intensive. Using connected vehicle (CV) data is an alternative method. Trajectories of vehicles through an intersection can be constr...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-03-01
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| Series: | Future Transportation |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-7590/3/1/21 |
| _version_ | 1797611610481623040 |
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| author | Somayeh Nazari Enjedani Mandar Khanal |
| author_facet | Somayeh Nazari Enjedani Mandar Khanal |
| author_sort | Somayeh Nazari Enjedani |
| collection | DOAJ |
| description | Turning movement (TM) data of vehicular traffic at intersections are a basic input for signal timing design. Existing methods of collecting TM data are time- and cost-intensive. Using connected vehicle (CV) data is an alternative method. Trajectories of vehicles through an intersection can be constructed using CV data. However, because of the low number of CVs in the traffic stream, it is imprecise to consider TM data from CVs as representative of the whole traffic flow. To address this issue, a Kalman filter (KF) for estimating TM rates at intersections based on CV data under low market penetration levels using commercially available connected vehicle data was developed in this study. This method is independent of intersection geometry or the presence of shared lanes. The algorithm was evaluated using data from an intersection in Salt Lake City, Utah. The manually collected TM counts at this intersection were compared with the raw CV data as well as the results obtained from the developed methodology. The comparison shows that while TM counts based on raw CV data show severe violations in accuracy, making them unreliable, the method developed in this research gives results that have much lower accuracy violations. |
| first_indexed | 2024-03-11T06:31:08Z |
| format | Article |
| id | doaj.art-7f1c2680aff94ff5aeb4d7b87fdb544d |
| institution | Directory Open Access Journal |
| issn | 2673-7590 |
| language | English |
| last_indexed | 2024-03-11T06:31:08Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Transportation |
| spelling | doaj.art-7f1c2680aff94ff5aeb4d7b87fdb544d2023-11-17T11:13:54ZengMDPI AGFuture Transportation2673-75902023-03-013134936710.3390/futuretransp3010021Development of a Turning Movement Estimator Using CV DataSomayeh Nazari Enjedani0Mandar Khanal1Department of Civil Engineering, Boise State University, Boise, ID 83725, USADepartment of Civil Engineering, Boise State University, Boise, ID 83725, USATurning movement (TM) data of vehicular traffic at intersections are a basic input for signal timing design. Existing methods of collecting TM data are time- and cost-intensive. Using connected vehicle (CV) data is an alternative method. Trajectories of vehicles through an intersection can be constructed using CV data. However, because of the low number of CVs in the traffic stream, it is imprecise to consider TM data from CVs as representative of the whole traffic flow. To address this issue, a Kalman filter (KF) for estimating TM rates at intersections based on CV data under low market penetration levels using commercially available connected vehicle data was developed in this study. This method is independent of intersection geometry or the presence of shared lanes. The algorithm was evaluated using data from an intersection in Salt Lake City, Utah. The manually collected TM counts at this intersection were compared with the raw CV data as well as the results obtained from the developed methodology. The comparison shows that while TM counts based on raw CV data show severe violations in accuracy, making them unreliable, the method developed in this research gives results that have much lower accuracy violations.https://www.mdpi.com/2673-7590/3/1/21turning movement countsKalman filteringconnected vehicle datavehicle trajectory |
| spellingShingle | Somayeh Nazari Enjedani Mandar Khanal Development of a Turning Movement Estimator Using CV Data Future Transportation turning movement counts Kalman filtering connected vehicle data vehicle trajectory |
| title | Development of a Turning Movement Estimator Using CV Data |
| title_full | Development of a Turning Movement Estimator Using CV Data |
| title_fullStr | Development of a Turning Movement Estimator Using CV Data |
| title_full_unstemmed | Development of a Turning Movement Estimator Using CV Data |
| title_short | Development of a Turning Movement Estimator Using CV Data |
| title_sort | development of a turning movement estimator using cv data |
| topic | turning movement counts Kalman filtering connected vehicle data vehicle trajectory |
| url | https://www.mdpi.com/2673-7590/3/1/21 |
| work_keys_str_mv | AT somayehnazarienjedani developmentofaturningmovementestimatorusingcvdata AT mandarkhanal developmentofaturningmovementestimatorusingcvdata |