Performance of Smart Sensor Detectors for Stop-Bar Detection at Signalized Intersections
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 143, Issue 6
Abstract
Recent years have seen increasing deployment of radar-based technologies for vehicle detection at signalized intersections in the United States, mainly because they are nonintrusive, accurate, and robust to varying lighting, weather, and environmental conditions. In this paper, a radar-based detection technology is evaluated in the context of various weather and environmental conditions. High-resolution (100-ms sampling interval) data were collected in the field from two representative test sites. The detection errors were correlated with varying weather and environmental conditions using data-mining techniques, such as conditional inference trees and regression models. It shown that false and stuck-on call errors tend to increase under more-adverse weather conditions (e.g., rain and thunderstorms). Visibility, glare, and uneven shadows appear to be irrelevant. The near-side mounting location is associated with reduced missed-call, false-call, and dropped-call errors.
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Acknowledgments
This paper is based on a research project sponsored by the Georgia Department of Transportation. Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the sponsoring agency.
References
Cameron, A. C., and Trivedi, P. K. (1998). Regression analysis of count data, Cambridge University Press, Cambridge, U.K.
Cameron, A. C., and Trivedi, P. K. (2005). Microeconometrics: Methods and applications, Cambridge University Press, Cambridge, U.K.
Hothorn, T., Hornik, K., and Zeileis, A. (2006). “Unbiased recursive partitioning: A conditional inference framework.” J.Comput. Graphical Stat., 15(3), 651–674.
Medina, J. C., Benekohal, R. F., and Ramezani, H. (2012). “Field evaluation of smart sensor vehicle detectors at intersections—Volume 1: Normal weather conditions.”, Illinois Dept. of Transportation Bureau of Materials and Physical Research, Springfield, IL.
Medina, J. C., Benekohal, R. F., and Ramezani, H. (2013). “Field evaluation of smart sensor vehicle detectors at intersections—Volume 2: Performance under adverse weather conditions.”, Illinois Dept. of Transportation Bureau of Materials and Physical Research, Springfield, IL.
R Core Team. (2014). “R: A language and environment for statistical computing.” R Foundation for Statistical Computing, Vienna, Austria.
Strasser, H., and Weber, C. (1999). “On the asymptotic theory of permutation statistics.” Math. Methods Stat., 8, 220–250.
Weather Underground. (2016). “Weather company: An IBM business.” San Francisco.
Yang, J., Kim, S., and Zuo, B. (2015). “Evaluation of vehicle detection technologies for applications in Georgia.”, Georgia Dept. of Transportation, Forest Park, GA.
Zeileis, A., Kleiber, C., and Jackman, S. (2008). “Regression models for count data in R.” J. Stat. Software, 27(8), 1–25.
Information & Authors
Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 143 • Issue 6 • June 2017
Copyright
©2017 American Society of Civil Engineers.
History
Received: Apr 2, 2016
Accepted: Nov 17, 2016
Published online: Feb 24, 2017
Published in print: Jun 1, 2017
Discussion open until: Jul 24, 2017
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