Federal Highway Administration Vehicle Classification from Video Data and a Disaggregation Model

Autor:	Ramgiridhar Reddy Kilim, Andrew J. Graettinger, Philip W. Johnson, S. Rocky Durrans, Meghavardhan Reddy Govindu
Rok vydání:	2005
Předmět:	Data processing business.product_category Data collection Computer science Machine vision Real-time computing Process (computing) Transportation Video camera law.invention Traffic count Axle Axle counter law business Simulation Civil and Structural Engineering
Zdroj:	Journal of Transportation Engineering. 131:689-698
ISSN:	1943-5436 0733-947X
DOI:	10.1061/(asce)0733-947x(2005)131:9(689)
Popis:	To assess highway system performance, states are required to submit compositional traffic count reports to the Federal Highway Administration (FHWA). A methodology was developed for obtaining compositional vehicle counts from low-cost portable video technology. Autoscope, an advanced video camera with machine vision processing, was used to capture, process, and store compositional counts based on vehicle length. A maximum of five length-based vehicle classes can be differentiated from video; therefore a methodology was developed that generates the FHWA 13-category vehicle classes. A disaggregation process, typically used in stochastic hydrology, is employed. Results from the disaggregation model are compared with actual FHWA classification data obtained from axle counters. It was observed that less than 1% of the vehicles were not included in the model output and 3.43% were misclassified. In addition, 95 and 99% confidence intervals calculated by the disaggregation model included 92.3 and 100% of the actual axle counter data.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::c7329f1d108726771129916c7c922889 https://doi.org/10.1061/(asce)0733-947x(2005)131:9(689) Zobrazit plný text záznamu Plný text ve formátu PDF