Older pedestrians are vulnerable on the streets and at significant risk of injury or death when involved in crashes. Pedestrians’ safety is critical for roadway agencies to consider and improve, especially older pedestrians aged greater than 65 years old. To better protect the older pedestrian group, the factors that contribute to the older crashes need to be analyzed deeply. Traditional modeling approaches such as Logistic models for data analysis may lead to modeling distortions due to the independence assumptions. In this study, Extreme Gradient Boosting (XGBoost), is used to model the classification problem of three different levels of severity of older pedestrian traffic crashes from crash data in Colorado, US. Further, Shapley Additive explanations (SHAP) are implemented to interpret the XGBoost model result and analyze each feature’s importance related to the levels of older pedestrian crashes. The interpretation results show that the driver characteristic, older pedestrian characteristics, and vehicle movement are the most important factors influencing the probability of the three different severity levels. Those results investigate each severity level’s correlation factors, which can inform the department of traffic management and the department of road infrastructure to protect older pedestrians by controlling or managing some of those significant features.

Older pedestrians are more likely to have severe or fatal consequences when involved in traffic crashes. Identifying the factors contributing to the severity and possible interdependencies between ...

Different original equipment manufacturers (OEMs) provide different connected and autonomous vehicle (CAV) solutions. However, previous studies that estimated the impact of CAV on driving behaviors assumed CAVs are identical, with an integrated form of technologies. In this study, a simulation was conducted to investigate how different fundamental elements of CAV technology (i.e., communication range, number of vehicular interactions, and car-following distance) affect right-turn gap acceptance behavior at unsignalized intersections. The results of the statistical analysis, critical gaps comparison, platooning analysis, and logistic regression revealed that (1) all elements increase the lag acceptance rate compared with all human-driven vehicles; (2) a longer communication range and more vehicular interactions make CAVs turn conservatively from a minor road, causing the critical gap to increase; (3) a shorter car-following distance causes CAVs to generate longer platoons and shorter gaps on a major road, which decreases acceptance probability; and (4) CAVs with a shorter car-following distance on a minor road are more aggressive in accepting gap/lag, causing the critical gap to decrease and acceptance probability to increase. The study conducted an impact analysis of CAV technologies from a novel perspective, and the results can be used as a technical report of CAV’s performance to help build transportation policies.