title: Interrupted versus uninterrupted flow: a safety propensity index for driver behavior.
abstract: The objective of this paper is to develop a quantitative safety propensity index (SPI) that captures the overall propensity of a given surrounding environment to cause unsafe driving. The study is conducted in two different flow conditions: interrupted and uninterrupted. Using structural modeling techniques, the index can be estimated from observed geometric, weather-related, vehicular, driver-related, and traffic-related characteristics. To illustrate the adopted approach, extensive effort was conducted to "sync" data from different sources including the Virginia Department of Transportation and the FARS/GES crash data libraries. The Virginia Department of Transportation provided traffic data for 10 freeway sections with interrupted flow and 9 highway sections with interrupted flow in the Northern Virginia area, USA. Two different structural equations models were found allowing insights to the safety impact of different surrounding elements/dimensions. The SPI provides (a) a basis for quantifying the effects of the aforementioned characteristics on safety, (b) a basis for comparing the differences between the factors affecting safety in different flow scenarios and (c) ranking the corresponding roadway sections/locations for improved safety performance. The framework and methodology used to develop this index have the potential to support safety policy analysis and decision making.
authors.affiliation: Department of Civil and Environmental Engineering, Center for Intelligent Systems Research, Traffic and Networks Research Laboratory, The George Washington University, Exploration Hall, 20101 Academic Way, Ashburn, VA 20147, USA. hamdar@gwu.edu
authors.forename: Samer H
authors.initials: SH
authors.lastname: Hamdar
authors.forename: Justin
authors.initials: J
authors.lastname: Schorr
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D001334:Automobile Driving / Q000706:statistics & numerical data*; D003617:Dangerous Behavior; D004779:Environment Design / Q000706:statistics & numerical data*; D006801:Humans; D015233:Models, Statistical; D018986:Motor Vehicles / Q000706:statistics & numerical data; D012449:Safety; D014768:Virginia; D014887:Weather