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

Jaimyoung Kwon, Pravin Varaiya and Alexander Skabardonis

Institute of Transportation Studies, Research Reports, Working Papers, Proceedings from Institute of Transportation Studies, UC Berkeley

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.

Date: 2003-07-01
References: View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
https://www.escholarship.org/uc/item/5h70x5j9.pdf;origin=repeccitec (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cdl:itsrrp:qt5h70x5j9

Access Statistics for this paper

More papers in Institute of Transportation Studies, Research Reports, Working Papers, Proceedings from Institute of Transportation Studies, UC Berkeley Contact information at EDIRC.
Bibliographic data for series maintained by Lisa Schiff ().