Antonella Scarano

Growing concerns about emissions, urban traffic congestion, and the promotion of an active lifestyle are inducing more people to choose bike for their daily commute. The increase in bike usage underscores the need for improving the cyclist’s safety. Our study examined the 72 363 cyclist crashes that occurred in Great Britain in the period 2016-2019 with the objective of (1) examining how various factors influence cyclist crash severity, (2) identifying complex interactions among these crash patterns, and (3) proposing countermeasures aimed at solving the identified risk factors. To achieve these goals, a Classification Tree (CT) model was used as an exploratory tool to detect patterns and interactions that may not have been hypothesized a priori and an econometric approach, such as Mixed Logit Model (MLM), was used to quantify global effects and test the interactions identified by the CT and all the explanatory variables within a statistically rigorous framework. Specifically, six interaction variables were identified from the CT terminal nodes with the highest probability of fatal crashes by tracing back their pathways to the root node. These interactions were then included as additional explanatory variables in the MLM to guarantee that all risk factors were tested within a unified statistical framework. Interestingly, all the interactions were statistically significant. Thus, the CT model is explicitly used as a supporting tool to identify potential interactions, while conclusions are extracted from the MLM results. Based on the identified risk factors, a set of targeted safety countermeasures has been proposed to minimize cyclist crash severity and improve overall road safety.

Walking plays an important role in overcoming many challenges nowadays, and governments and local authorities are encouraging healthy and environmentally sustainable lifestyles. Nevertheless, pedestrians are the most vulnerable road users and crashes with pedestrian involvement are a serious concern. Thus, the identification of pedestrian crash patterns is crucial to identify appropriate safety countermeasures. The aims of the study are (1) to identify the road infrastructure, environmental, vehicle, and driver-related patterns that are associated with an overrepresentation of pedestrian crashes, and (2) to identify safety countermeasures to mitigate the detected pedestrian crash patterns. The analysis carried out an econometric model, namely the mixed logit model, and the association rules and the classification tree algorithm, as machine learning tools, to analyse the patterns contributing to the overrepresentation of pedestrian crashes in Italy. The dataset consists of 874,847 crashes—including 101,032 pedestrian crashes—that occurred in Italy from 2014 to 2018. The methodological approach adopted in the study was effective in uncovering relations among road infrastructure, environmental, vehicle, and driver-related patterns, and the overrepresentation of pedestrian crashes. The mixed logit provided a clue on the impact of each pattern on the pedestrian crash occurrence, whereas the association rules and the classification tree detected the associations among the patterns with insights on how the co-occurrence of more factors could be detrimental to pedestrian safety. Drivers’ behaviour and psychophysical state turned out to be crucial patterns related to pedestrian crashes’ overrepresentation. Based on the identified crash patterns, safety countermeasures have been proposed.

Pedestrians are the most vulnerable road users and pedestrian crashes are a major concern both for their number and their severity. In Italy, pedestrians account for 34% of the road fatalities in urban area. To improve pedestrian safety, this study is aimed at analysing the roadway, environmental, vehicle, driver and pedestrian-related factors that are associated with fatal pedestrian crashes in Italy and providing insights for the development of effective countermeasures. This study used an econometric model, the mixed logit model, and a machine learning algorithm, the association rules, to analyse 101,032 pedestrian crashes that occurred in Italy. Study results identified several factors associated with fatal pedestrian crashes. The mixed logit identified 46 significant indicator variables (1 with random parameter), and the association rules provided 119 valid rules. F-measure and G-mean showed higher prediction performance of the mixed logit over the association rules. Study results recommend using both models as complementary approaches since their combination is effective in providing meaningful insights about pedestrian crash contributory factors and their interdependencies. To address the contributory factors identified by the study, behavioural/engineering pedestrian safety countermeasures are recommended. The findings provided new insights for transportation agencies to develop effective countermeasures for pedestrian safety improvement.

The study aims to investigate the factors that are associated with fatal and severe vehicle–pedestrian crashes in Great Britain by developing four parametric models and five non-parametric tools to predict the crash severity. Even though the models have already been applied to model the pedestrian injury severity, a comparative analysis to assess the predictive power of such modeling techniques is limited. Hence, this study contributes to the road safety literature by comparing the models by their capabilities of identifying the significant explanatory variables, and by their performances in terms of the F-measure, the G-mean, and the area under curve. The analyses were carried out using data that refer to the vehicle–pedestrian crashes that occurred in the period of 2016–2018. The parametric models confirm their advantages in offering easy-to-interpret outputs and understandable relations between the dependent and independent variables, whereas the non-parametric tools exhibited higher classification accuracies, identified more explanatory variables, and provided insights into the interdependencies among the factors. The study results suggest that the combined use of parametric and non-parametric methods may effectively overcome the limits of each group of methods, with satisfactory prediction accuracies and the interpretation of the factors contributing to fatal and serious crashes. In the conclusion, several engineering, social, and management pedestrian safety countermeasures are recommended.