An Analysis of Factors Affecting the Severity of Cycling Crashes Using Binary Regression Model

Integrating visual factors in crash rate analysis at Intersections: An AutoML and SHAP approach towards cycling safety

Cycling crashes constitute a significant and rising share of traffic accidents. Consequently, exploring factors affecting cycling safety has become a priority for both governmental bodies and scholars. However, most existing studies have neglected the vision factors capable of quantitatively describing the city-level cycling environment. Moreover, they have relied on limited models that lack interpretability and fail to capture the spatial variations in the contribution of factors. To address these gaps, this research proposed a framework that used origin-destination-based cycling flow and vision factors generated from Google Street View images to identify the leading factors. It also employed the comparative Automatic Machine Learning and interpretable SHAP value-based geospatial analysis to explain each factor's contribution to the cycling crash risk, with a particular focus on the spatial variations in the influence of vision factors. The effectiveness of this framework was validated by a case study in Manhattan, which examined the leading risk factors of cycling crash rates at intersections. The results showed that the LightGBM model, with selected subsets of factors, outperformed other models. Through SHAP explanations of global feature importance, the study identified the proportion of road barriers, the proportion of open sky, and the number of visible trucks as the leading visual risk factors. Additionally, using SHAP-based geospatial analysis, the study revealed the local variations in the effects of these three factors and identified eight areas with higher cycling crash rates. Based on these findings, the study provided practical measures for a safer cycling environment in Manhattan.

Traffic Accident Severity Prediction Based on Random Forest

The prediction of traffic accident severity is essential for traffic safety management and control. To achieve high prediction accuracy and model interpretability, we propose a hybrid model that integrates random forest (RF) and Bayesian optimization (BO). In the proposed model, BO-RF, RF is adopted as a basic predictive model and BO is used to tune the parameters of RF. Experimental results show that BO-RF achieves higher accuracy than conventional algorithms. Moreover, BO-RF provides interpretable results by relative importance and a partial dependence plot. We can identify important influential factors for traffic accident severity by relative importance. Further, we can investigate how the influential factors affect traffic accident severity by the partial dependence plot. These results provide insights to mitigate the severity of traffic accident consequences and contribute to the sustainable development of transportation.

Risk Factors Analysis of Car Door Crashes Based on Logistic Regression

Unlike door crash accidents predominantly involving bicycles in Australia, the UK, and other Western countries, cases in Taiwan are far more fatal as they usually involve motorcycles. This is due to the unique anthropogeography and transportation patterns of Taiwan, particularly the numbers of motorcycles being twice that of cars. Both path analysis and multivariate logistic regression methods were adopted in this study. The multivariate logistic regression analysis results have shown that the main risk factors causing serious injuries in door crashes include winter, morning, male motorcyclists, heavy motorcycles, and the left sides of cars. Regarding the gender differences in motorcyclists, it appears that female motorcyclists have higher door crash accident rates, while the odds of severe injury and fatality in male motorcyclists are 1.658 times greater than that of female motorcyclists. The risk factors derived from the multivariate logistic regression analysis were further discussed and analysed. It was found that the causes of serious injuries and deaths stemming from door crashes were related to the risk perception ability, reaction ability, visibility, and riding speed of the motorcyclists. Therefore, suggestions on risk management and accident prevention were proposed using advocacy through the 3E strategies of human factors engineering design.