Rainy weather conditions can have significant impact on the severity and frequency of traffic crashes. This study investigated factors that influence the severity of vehicle crashes during rainy weather in California. Data from 23,242 rain-related crashes in California were taken from the Highway Safety Information System (HSIS) database. The data was divided into 12 groups based on driver gender (male and female) and crash type (six categories: rear-end, hit object, sideswipe, overturned, head-on, and broadside). Each group was assigned a logistic regression model for crash severity (Property Damage Only (PDO) vs. injuries or fatalities (NotPDO)) yielding 12 models for various combinations of driver gender and crash types. Results indicate that factors such as the number of vehicles involved, vehicle manufacturing year, annual average daily traffic (AADT), road topography, season of crash, number of lanes, and driver age group all significantly influenced crash severity across various scenarios. These findings provide detailed insights into how various factors contribute to crash severity in different scenarios, allowing policymakers to develop targeted interventions. Policymakers can utilize the findings of this study to implement targeted measures in areas with high frequencies of specific crash types, particularly during adverse environmental conditions.

As autonomous vehicles (AVs) become more prevalent in traffic systems and human drivers are beginning to interact with them, understanding how the presence of AVs affects traffic dynamics and safety is becoming more important. This study investigates the impact of AVs on the behavior of human-driven vehicles (HDVs) in mixed traffic environments. One key question is whether the conspicuity of an AV influences HDV car-following behavior. Using data from a naturalistic driving study conducted in Ohio, U.S., for different road types, this research compares interactions with two types of advanced driver assistance system (ADAS)-equipped vehicle: readily identifiable (RI) with visible sensor arrays, and discreet (DI) with concealed sensors. Using statistical tests such as the Kolmogorov-Smirnov test and the Mann-Whitney U test, the analysis of key metrics—including following distances, gaps, speed differences, acceleration, braking patterns, and lateral distances during overtaking events—revealed notable differences in following behavior when trailing an RI vehicle. Specifically, drivers tended to maintain greater distances and adjust their speed and acceleration patterns more when following RI vehicles than when following DI vehicles. The results also indicated that drivers braked more aggressively on freeways and non-divided roads when following RI vehicles, and they maintained greater distances when overtaking on freeways. Assessing surrogate measures of safety, such as time-to-collision and deceleration required to avoid crash, further confirmed what appears to be safer driving behavior when following RI vehicles. This suggests that human drivers interact differently around AVs depending on their visibility. These findings underscore the need to consider the visibility of AV features in traffic management and vehicle design to optimize traffic flow and enhance road safety.