Comparing to the existing crash count and conflict risk measures modeling approaches, the proposed modeling framework also contributes to the transportation safety literature by (a) better reflecting safety by treating conflict risk measures and crashes equally (b) better accounting for the impact of potential unobserved factors on crash count and conflict risk measures simultaneously and (c) better accounting for the exposure and traffic risk factors and their heterogenous impact on crash count and conflict risk measures.
The presence of stronger dependences among conflict risk measures than those between conflict risk measures and crash count are revealed and the dependency structure is relatively stable across different conflict risk measure threshold values and sample sizes. Specifically, three longitudinal conflict risk measures extracted from real-world connected vehicle data collected in Ann Arbor, Michigan as well as rear-end crash count are modeled via the multivariate Gaussian copula. As conflict risk measures can either be event counts or continuous random variables, the proposed framework is devised to accommodate mixed count-continuous margins. This current study proposes to model crash count and conflict risk measures jointly by developing a multivariate copula-based modeling framework.
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