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The aim of this study was to evaluate the effects of driver-related factors on crash involvement of four different types of commercial vehicles—express buses, local buses, taxis, and trucks—and to compare outcomes across types. Previous studies on commercial vehicle crashes have generally been focused on a single type of commercial vehicle; however, the characteristics of drivers as factors affecting crashes vary widely across types of commercial vehicles as well as across study sites. This underscores the need for comparative analysis between different types of commercial vehicles that operate in similar environments. Toward these ends, we analyzed 627,594 commercial vehicle driver records in South Korea using a mixed logit model able to address unobserved heterogeneity in crash-related data. The estimated outcomes showed that driver-related factors have common effects on crash involvement: greater experience had a positive effect (diminished driver crash involvement), while traffic violations, job change, and previous crash involvement had negative effects. However, the magnitude of the effects and heterogeneity varied across different types of commercial vehicles. The findings support the contention that the safety management policy of commercial drivers needs to be set differently according to the vehicle type. Furthermore, the variables in this study can be used as promising predictors to quantify potential crash involvement of commercial vehicles. Using these variables, it is possible to proactively identify groups of accident-prone commercial vehicle drivers and to implement effective measures to reduce their involvement in crashes.

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This paper proposes a framework to evaluate the network vulnerability of cities to wildfires. Three cities are selected from the California Public Utilities Commission (CPUC), U.S., fire-threat regions: Orinda, Paradise, and Atascadero. For each city, four different network connectivity measures are calculated, and agent-based evacuation simulations are performed by the Monte Carlo method. In the simulations, the number of isolated vehicles and evacuation time estimates are measured for the following scenarios: (i) no wildfire case with original network; and (ii) wildfire cases with randomly damaged networks that are reduced by 1%, 3%, 5%, 7%, and 10% from the original network. A city-to-city comparison is conducted in relation to network connectivity measures and evacuation simulation results. It is shown that Paradise has the worst network connectivity, and the simulation results reveal that Paradise also has the most sensitive network in relation to random roadway closures caused by wildfire propagation. Thus, among the three cities, Paradise has the most vulnerable network to wildfires as determined through the two analysis results concerning the worst network measures and the simulation results. It is expected that the proposed analysis framework can be generally applied to any city located in a fire-threat region.