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Int. J. Environ. Res. Public Health 2016, 13(6), 609; doi:10.3390/ijerph13060609

Crash Frequency Modeling Using Real-Time Environmental and Traffic Data and Unbalanced Panel Data Models

1
Key Laboratory of Road & Traffic Engineering of the Ministry of Education, Tongji University, 4800 Cao’an Road, Shanghai 201804, China
2
Department of Civil & Environmental Engineering, Colorado State University, Fort Collins, CO 80523, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Harry Timmermans
Received: 3 April 2016 / Revised: 8 June 2016 / Accepted: 13 June 2016 / Published: 18 June 2016
(This article belongs to the Special Issue Traffic Safety and Injury Prevention)
View Full-Text   |   Download PDF [320 KB, uploaded 18 June 2016]

Abstract

Traffic and environmental conditions (e.g., weather conditions), which frequently change with time, have a significant impact on crash occurrence. Traditional crash frequency models with large temporal scales and aggregated variables are not sufficient to capture the time-varying nature of driving environmental factors, causing significant loss of critical information on crash frequency modeling. This paper aims at developing crash frequency models with refined temporal scales for complex driving environments, with such an effort providing more detailed and accurate crash risk information which can allow for more effective and proactive traffic management and law enforcement intervention. Zero-inflated, negative binomial (ZINB) models with site-specific random effects are developed with unbalanced panel data to analyze hourly crash frequency on highway segments. The real-time driving environment information, including traffic, weather and road surface condition data, sourced primarily from the Road Weather Information System, is incorporated into the models along with site-specific road characteristics. The estimation results of unbalanced panel data ZINB models suggest there are a number of factors influencing crash frequency, including time-varying factors (e.g., visibility and hourly traffic volume) and site-varying factors (e.g., speed limit). The study confirms the unique significance of the real-time weather, road surface condition and traffic data to crash frequency modeling. View Full-Text
Keywords: hourly crash frequency; real-time driving environment; unbalanced panel data; zero-inflated negative binomial; refined temporal scale hourly crash frequency; real-time driving environment; unbalanced panel data; zero-inflated negative binomial; refined temporal scale
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Chen, F.; Chen, S.; Ma, X. Crash Frequency Modeling Using Real-Time Environmental and Traffic Data and Unbalanced Panel Data Models. Int. J. Environ. Res. Public Health 2016, 13, 609.

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