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Title:

Real-Time Analysis of Visibility Related Crashes: Can Loop Detector and AVI Data Predict Them Equally?

Accession Number:

01371260

Record Type:

Component

Abstract:

More researchers started using real-time traffic surveillance data, collected from loop/radar detectors (LDs), for proactive crash risk assessment. However, there is a lack of prior studies that investigated the links between real-time traffic data and crash risk of reduced visibility related (VR) crashes. Two issues that have not explicitly been addressed in prior studies are: (1) the possibility of predicting VR crashes using traffic data collected from the Automatic Vehicle Identification (AVI) sensors installed on expressways and (2) which traffic data are advantageous for predicting VR crashes: LDs or AVIs. Thus, this study attempts to examine the relationships between VR crash risk and real-time traffic data collected from LDs installed on two freeways in Central Florida (I-4 and I-95) and from AVI sensors installed on two expressways (SR 408 and SR 417). Also, it investigates which data are better for predicting VR crashes. The approach adopted here involves developing Bayesian matched case-control logistic regression using the historical crashes, LDs and AVI data. Regarding models estimated based on LDs data, the average speed observed at the nearest downstream station along with the coefficient of variation in speed observed at the nearest upstream station, all at 5-10 minutes prior to the crash time, were found to have significant effect on VR crash risk. However, for the model developed based on AVI data, the coefficient of variation in speed observed at the crash segment, at 5-10 minutes prior to the crash time, affected the likelihood of VR crash occurrence. Argument concerning which traffic data (LDs or AVI) are better for predicting VR crashes is also provided and discussed.

Supplemental Notes:

This paper was sponsored by TRB committee ANB20 Safety Data, Analysis and Evaluation

Monograph Accession #:

01362476

Report/Paper Numbers:

12-0113

Language:

English

Corporate Authors:

Transportation Research Board

500 Fifth Street, NW
Washington, DC 20001 United States

Authors:

Abdel-Aty, Mohamed
Hassan, Hany M
Ahmed, Mohamed

Pagination:

19p

Publication Date:

2012

Conference:

Transportation Research Board 91st Annual Meeting

Location: Washington DC, United States
Date: 2012-1-22 to 2012-1-26
Sponsors: Transportation Research Board

Media Type:

Digital/other

Features:

Figures; References; Tables

Uncontrolled Terms:

Crash risk; Reduced visibility

Geographic Terms:

Florida

Subject Areas:

Data and Information Technology; Highways; Planning and Forecasting; Safety and Human Factors; I81: Accident Statistics; I83: Accidents and the Human Factor

Source Data:

Transportation Research Board Annual Meeting 2012 Paper #12-0113

Files:

TRIS, TRB

Created Date:

Feb 8 2012 4:52PM