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Estimation of Truck Traffic Volume from Single Loop Detectors Using Lane-to-Lane Speed Correlation

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  • Kwon, Jaimyoung
  • Varaiya, Pravin
  • Skabardonis, Alexander

Abstract

An algorithm for real time estimation of truck traffic in multi-lane freeway is proposed. The algorithm uses data from single loop detectors-the most widely installed surveillance technology for urban freeways in the US. The algorithm works for those freeway locations that have a truck-free lane, and exhibit high lane-to-lane speed correlation. These conditions are met by most urban freeway locations. The algorithm produces real time estimates of the truck traffic volumes at the location. It can also be used to produce alternative estimate of the mean effective vehicle length, which can improve speed estimates from single loop detector data. The algorithm is tested with real freeway data and produces estimates of truck traffic volumes with only 5.7% error. It also captures the daily patterns of truck traffic and mean effective vehicle length. Applied to loop data on I-710 near Long Beach during the dockworkers lockout October 1-9, 2002, the algorithm finds a 32 % reduction in 5-axle truck volume.

Suggested Citation

  • Kwon, Jaimyoung & Varaiya, Pravin & Skabardonis, Alexander, 2003. "Estimation of Truck Traffic Volume from Single Loop Detectors Using Lane-to-Lane Speed Correlation," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt5h70x5j9, Institute of Transportation Studies, UC Berkeley.
    • Handle: RePEc:cdl:itsrrp:qt5h70x5j9
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    Cited by:

    1. Cheung, Sing Yiu & Coleri, Sinem & Dundar, Baris & Ganesh, Sumitra & Tan, Chin-Woo & Varaiya, Pravin, 2004. "Traffic Measurement and Vehicle Classification with a Single Magnetic Sensor," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt2gv111tv, Institute of Transportation Studies, UC Berkeley.
    2. Chen Feng Ng & Elaine F. Frey, 2013. "The recession and truck traffic on the Long Beach Freeway in Los Angeles," Economics Bulletin, AccessEcon, vol. 33(4), pages 2518-2527.
    3. Xing, Jiping & Wu, Wei & Cheng, Qixiu & Liu, Ronghui, 2022. "Traffic state estimation of urban road networks by multi-source data fusion: Review and new insights," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 595(C).
    4. Shao, Jing & Yang, Hangjun & Xing, Xiaoqiang & Yang, Liu, 2016. "E-commerce and traffic congestion: An economic and policy analysis," Transportation Research Part B: Methodological, Elsevier, vol. 83(C), pages 91-103.
    5. Zheng, Zuduo & Ahn, Soyoung & Chen, Danjue & Laval, Jorge, 2011. "Applications of wavelet transform for analysis of freeway traffic: Bottlenecks, transient traffic, and traffic oscillations," Transportation Research Part B: Methodological, Elsevier, vol. 45(2), pages 372-384, February.
    6. Varaiya, Pravin, 2004. "Assessment of MeMS Sensors in an Urban Traffic Environment," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt82p3t7gx, Institute of Transportation Studies, UC Berkeley.
    7. Chow, Andy H.F. & Lu, Xiao-Yun & Qiu, Tony Z., 2009. "Empirical Analysis of Traffic Breakdown Probability Distribution with Respect to Speed and Occupancy," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt35j7r3t5, Institute of Transportation Studies, UC Berkeley.

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