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Citation |
Hamdar SH, Schorr J. Accid. Anal. Prev. 2013; 55: 22-33. |
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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. Electronic address: hamdar@gwu.edu. |
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Copyright |
(Copyright © 2013, Elsevier Publishing) |
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DOI |
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PMID |
23507432 |
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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. Language: en |