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Vehicle Classification under Congestion using Dual Loop data

Itekyala, Sudhir Reddy
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1288379131

Abstract Details

2010, MS, University of Cincinnati, Engineering and Applied Science: Civil Engineering.
The growing congestion problem on Interstates has been identified as a serious problem for accurate data collection from automatic sensors like Inductive loop detectors (ILD). Traffic speed and vehicle classification data are typically collected by dual-loop detectors on freeways. During congestion, measurement of vehicle lengths which is based on detector ON and OFF timestamps (raw loop event data) often lead to misclassification of vehicle data. Accurate detection of raw event data and modified classification algorithm are increasingly important for higher data accuracy needs for agencies such as Advanced Traffic Management Systems (ATMS) and Advanced Traffic Information Systems (ATIS). Vehicle classification algorithm works on the assumption of constant vehicle speed in the detection area. This assumption is violated during congestion which induces errors in to vehicle length estimates leading to more inaccurate vehicle classification data. This paper unlike in preceding works presents a model which is simple enough to be implemented using existing loop detector hardware. This new model assumes vehicle travels with constant acceleration over loop detection area and thus named as ―Constant Acceleration based Vehicle Classification model (CAVC)‖. This model first identifies traffic flow state and later uses Kinematic equations for estimating vehicle length values. Data is collected by videotaping dual loop station and also simultaneously collecting raw loop event data. Ground truth vehicle data is then extracted using Vehicle Video-Capture Data Collector (VEVID) [Wei et al. 2005] from video data. This improved model (CAVC model) is then validated using ground truth classification data and also compared with the results from existing vehicle classification model for different traffic flow states (under specific scenarios).
Heng Wei, PhD (Committee Chair)
Anant Kukreti, PhD (Committee Member)
Changjoo Kim, PhD (Committee Member)
90 p.
Civil Engineering
vehicle classification; interstate congestion; dual loop detectors

Recommended Citations

Citations

  • Itekyala, S. R. (2010). Vehicle Classification under Congestion using Dual Loop data [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1288379131

    APA Style (7th edition)

  • Itekyala, Sudhir Reddy. Vehicle Classification under Congestion using Dual Loop data. 2010. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1288379131.

    MLA Style (8th edition)

  • Itekyala, Sudhir Reddy. "Vehicle Classification under Congestion using Dual Loop data." Master's thesis, University of Cincinnati, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1288379131

    Chicago Manual of Style (17th edition)

ucin1288379131
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© 2010, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.