Title
Utilizing Microscopic Traffic and Weather Data to Analyze Real-Time Crash Patterns in the Context of Active Traffic Management
Abbreviated Journal Title
IEEE Trans. Intell. Transp. Syst.
Keywords
Active traffic management (ATM); crash-type analysis; intelligent; transportation system (ITS); microscopic crash analysis; random effect; logistic regression; real-time data; LOGISTIC-REGRESSION MODEL; MOUNTAINOUS FREEWAY; PREDICTION; INTERSECTIONS; FREQUENCY; FRAMEWORK; SEVERITY; ROADS; Engineering, Civil; Engineering, Electrical & Electronic; Transportation; Science & Technology
Abstract
This paper investigates the effects of microscopic traffic, weather, and roadway geometric factors on the occurrence of specific crash types for a freeway. The I-70 Freeway was chosen for this paper since automatic vehicle identification (AVI) and weather detection systems are implemented along this corridor. A main objective of this paper is to expand the purpose of the existing intelligent transportation system to incorporate traffic safety improvement and suggest active traffic management (ATM) strategies by identifying the real-time crash patterns. Crashes have been categorized as rear-end, sideswipe, and single-vehicle crashes. AVI segment average speed, real-time weather data, and roadway geometric characteristic data were utilized as explanatory variables in this paper. First, binary logistic regression models were estimated to compare single-with multivehicle crashes and sideswipe with rear-end crashes. Then, a hierarchical logistic regression model that simultaneously fits two conditional logistic regression models for the three crash types has been developed. Results from the models indicate that single-vehicle crashes are more likely to occur in snowy seasons, at moderate slopes, three-lane segments, and under free-flow conditions, whereas the sideswipe crash occurrence differs from rear-end crashes with the visibility situation, segment number of lanes, grades, and their directions (up or down). Furthermore, the innovative way of estimating two conditional logistic regression models simultaneously in the Bayesian framework fits the correlated data structure well. Conclusions from this paper imply that different ATM strategies should be designed for three-and two-lane roadway sections and are also considering the seasonal effects.
Journal Title
Ieee Transactions on Intelligent Transportation Systems
Volume
15
Issue/Number
1
Publication Date
1-1-2015
Document Type
Article
Language
English
First Page
205
Last Page
213
WOS Identifier
ISSN
1524-9050
Recommended Citation
"Utilizing Microscopic Traffic and Weather Data to Analyze Real-Time Crash Patterns in the Context of Active Traffic Management" (2015). Faculty Bibliography 2010s. 6339.
https://stars.library.ucf.edu/facultybib2010/6339
Comments
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