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This study proposes a methodical approach to model desired speed distributions under different road-weather and traffic conditions followed by identification of road-weather conditions with potentially higher safety risks in rural divided highways located in extremely cold regions. Desired speed distributions encompassing unique combinations of adverse road-weather and traffic conditions are modelled as normal distributions characterized by their means and standard deviations formulated based on two principal statistical theorems and techniques i.e., Central Limit Theorem and Minimum Variance Unbiased Estimation. Combination of the precipitation conditions, road surface conditions, time of the day, temperature, traffic flow and the heavy vehicle percentage at the time of travel were considered in defining the combinations of road-weather and traffic conditions. The findings reveal that simultaneous occurrence of particular precipitation and pavement conditions significantly affect the characteristics of the desired speed distribution and potentially expose drivers to elevated safety risks. Jurisdictions experiencing extreme road-weather conditions may adapt the proposed methodology to assess speed behaviour under different road-weather conditions to establishing and deploying weather-responsive traffic management strategies such as variable speed limit to regulate speeding and improve traffic safety in winter.

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Understanding the impacts of different driving conditions on truck speed is critical to the development and maintenance of resilient highway freight transportation systems. This study attempts to evaluate the combined impact of road weather, travel lane, vehicle type, and truck payload conditions on drivers’ speed choice by modelling speed distributions as normal distributions. Two data analytics, a regression-based approach (RBA) and a central limit theorem (CLT)-based approach (CBA), are adapted to model context-specific speed distributions. The regression-based approach models population-level speed distributions by considering samples of individual speed data, whereas the CBA uses sampling distributions produced according to the CLT. A holistic approach is proposed to identify overall vehicle-specific collision risks imposed by different road-weather conditions, based on the speed distribution parameters estimated. Implications of the study results pertaining to the trucking industry are threefold. First, adapting different data analytics leads to different study results; yet, the CBA is recommended to model speed distributions. Second, truck speeds are significantly affected by the presence of adverse road-weather conditions, yet marginally varied under different loading conditions. Third, overall, tractor–trailer combinations (TTCs) entail high collision risks, particularly when transporting a freight load under adverse road-weather conditions. The study results would be useful to policymakers, particularly for effective speed management in extremely cold regions. Trucking companies may use the study results to identify the least risk-posing road-weather conditions to deploy safe freight transport operations. The resulting speed distribution models are also useful as input to calibrate traffic micro-simulation models.

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