Virginia Tech Transportation InstituteQueiroz, CesarRakha, Hesham A.Du, JianhePark, SangjunGuo, FengDoerzaph, Zachary R.Viita, DerekGolembiewski, Gary A.Katz, Bryan J.Kehoe, NicholasRigdon, H.2015-07-312015-07-312011Rakha, H. A., Du, J., Park, S., Guo, F., Doerzaph, Z. R., Viita, D., Golembiewski, G. A., Katz, B., Kehoe, N., & Rigdon, H. (2011). Feasibility of using in-vehicle video data to explore how to modify driver behavior that causes nonrecurring congestion: Shrp 2. (FHWA-JPO-04-081//NTIS-PB2003100296). Washington, DC: National Research Council (U.S.). Transportation Research Board. Retrieved from http://onlinepubs.trb.org/onlinepubs/shrp2/SHRP2_S2-L10-RR-1.pdf.S2-L10-RR-01http://hdl.handle.net/10919/55099Nonrecurring congestion is traffic congestion due to nonrecurring causes, such as crashes, disabled vehicles, work zones, adverse weather events, and planned special events. According to data from the Federal Highway Administration (FHWA), approximately half of all congestion is caused by temporary disruptions that remove part of the roadway from use, or "nonrecurring" congestion. These nonrecurring events dramatically reduce the available capacity and reliability of the entire transportation system. The objective of this project is to determine the feasibility of using in-vehicle video data to make inferences about driver behavior that would allow investigation of the relationship between observable driver behavior and nonrecurring congestion to improve travel time reliability. The data processing flow proposed in this report can be summarized as (1) collect data, (2) identify driver behavior, (3) identify correctable driver behavior, and (4) model travel time reliability, as shown in Figure ES.1.127 pagesapplication/pdfenIn CopyrightData and information technologyHighwaysOperations and traffic management safetyHuman factorsGovernment documenthttp://onlinepubs.trb.org/onlinepubs/shrp2/SHRP2_S2-L10-RR-1.pdf