Implementing Kinematic Wave Theory to Reconstruct Vehicle Trajectories from Fixed and Probe Sensor Data
A data fusion framework is examined to reproduce vehicle trajectories on urban arterials by combining probe and fixed sensor data, and signal timing parameters. The methodology is based on the kinematic wave theory and employs the variational theory for the solution method. However, the original methodology cannot deal with the vehicles coming in and out in the middle of the study section despite the frequent existence of such vehicles in the real world. Therefore, the methodology is extended so as to incorporate the vehicles coming in and out. The proposed method is then applied to real world data and its robustness is confirmed by changing the input data characteristics.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/18770428
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Supplemental Notes:
- Abstract reprinted with permission from Elsevier.
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Authors:
- Mehrana, Babak
- Kuwaharaa, Masao
- Naznina, Farhana
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Conference:
- 19th International Symposium on Transportation and Traffic Theory
- Location: Berkeley CA, United States
- Date: 2011-7-18 to 2011-7-20
- Publication Date: 2011
Language
- English
Media Info
- Media Type: Web
- Features: Figures; References;
- Pagination: pp 247-268
- Monograph Title: International Symposium on Transportation and Traffic Theory, 19th
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Serial:
- Procedia - Social and Behavioral Sciences
- Volume: 17
- Publisher: Elsevier
- ISSN: 1877-0428
- Serial URL: http://www.sciencedirect.com/science/journal/18770428/53
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Publication flags:
Open Access (libre)
Subject/Index Terms
- TRT Terms: Arterial highways; Data fusion; Kinematics; Probe vehicles; Sensors; Traffic signal timing; Travel time; Vehicle trajectories
- Uncontrolled Terms: Kinematic wave theory; Real world data
- Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting; I72: Traffic and Transport Planning;
Filing Info
- Accession Number: 01345404
- Record Type: Publication
- Files: TRIS
- Created Date: Jul 21 2011 10:08AM