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http://dx.doi.org/10.12815/kits.2022.21.2.17

An Estimation Methodology of Empirical Flow-density Diagram Using Vision Sensor-based Probe Vehicles' Time Headway Data  

Kim, Dong Min (Cho Chun Shik Graduate School of Mobility, KAIST)
Shim, Jisup (Department of Transport and Planning, Delft University of Technology)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.21, no.2, 2022 , pp. 17-32 More about this Journal
Abstract
This study explored an approach to estimate a flow-density diagram(FD) on a link in highway traffic environment by utilizing probe vehicles' time headway records. To study empirical flow-density diagram(EFD), the probe vehicles with vision sensors were recruited for collecting driving records for nine months and the vision sensor data pre-processing and GIS-based map matching were implemented. Then, we examined the new EFDs to evaluate validity with reference diagrams which is derived from loop detection traffic data. The probability distributions of time headway and distance headway as well as standard deviation of flow and density were utilized in examination. As a result, it turned out that the main factors for estimation errors are the limited number of probe vehicles and bias of flow status. We finally suggest a method to improve the accuracy of EFD model.
Keywords
Empirical Flow-density Diagram; Probe Vehicle; Time Headway; VDS; Freeway Environment;
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Times Cited By KSCI : 1  (Citation Analysis)
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