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.
Highlights
Despite all the efforts during the past decades, traffic crashes are still the primary threat on highways in most countries
The present study reports the recent efforts on developing crash frequency models in refined temporal scales using disaggregated and unbalanced panel-data structure
The estimation results of unbalanced panel data zero-inflated negative binomial models suggest that there are many factors influencing the crash frequency on I-25 including time-varying factors and site-varying factors
Summary
Despite all the efforts during the past decades, traffic crashes are still the primary threat on highways in most countries. A better understanding of the critical contributing factors and the ability to predict the crash risk has become the key to various prevention efforts, such as advanced traffic management, proactive law enforcement, and injury mitigation. Most crash frequency models used aggregated information with relatively large time scales (e.g., yearly), rather than detailed, time-varying data in smaller time scales (e.g., hourly, daily, or weekly). Real time traffic and environmental conditions (e.g., weather conditions) have significant impact on crash occurrence, the large scales and aggregated variables may not be sufficient for some complex or adverse driving conditions, such as inclement weather and/or complex terrains. As a result of adopting larger time scales, some important information of critical driving environmental variables over time (e.g., weather or traffic data) is often lost [1]. The crash frequency models developed with aggregated data can only provide the results based on average
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