Cycling provides numerous benefits to individuals and to society but the burden of road traffic injuries and fatalities is disproportionately sustained by cyclists. Without awareness of the contributory factors of cyclist death and injury, the capability to implement context-specific and appropriate measures is severely limited. In this paper, we investigated the effects of the characteristics related to the road, the environment, the vehicle involved, the driver, and the cyclist on severity of crashes involving cyclists analysing 72,363 crashes that occurred in Great Britain in the period 2016-2018. Both a machine learning method, as the Random Forest (RF), and an econometric model, as the Random Parameters Logit Model (RPLM), were implemented. Three different RF algorithms were performed, namely the traditional RF, the Weighted Subspace RF, and the Random Survival Forest. The latter demonstrated superior predictive performances both in terms of F-measure and G-mean. The main result of the Random Survival Forest is the variable importance that provides a ranked list of the predictors associated with the fatal and severe cyclist crashes. For fatal classification, 19 variables showed a normalized importance higher than 5% with the second involved vehicle manoeuvring and the gender of the driver of the second vehicle having the greatest predictive ability. For serious injury classification, 13 variables showed a normalized importance higher than 5% with the bike leaving the carriageway having the greatest normalized importance. Furthermore, each path from the root node to the leaf nodes has been retraced the way back generating 361 if-then rules with fatal crash as consequent and 349 if-then rules with serious injury crash as consequent. The RPLM showed significant unobserved heterogeneity in the data finding four normal distributed indicator variables with random parameters: cyclist age ≥ 75 (fatal prediction), cyclist gender male (fatal and serious prediction), and driver aged 55-64 (serious prediction). The model's McFadden Pseudo R² is equal to 0.21, indicating a very good fit. Furthermore, to understand the magnitude of the effects and the contribution of each variable to injury severity probabilities the pseudo-elasticity was assessed, gaining valuable insights into the relative importance and influence of the variables. The RF and the RPLM resulted complementary in identifying several roadways, environmental, vehicle, driver, and cyclist-related factors associated with higher crash severity. Based on the identified contributory factors, safety countermeasures useful to develop strategies for making bike a safer and more friendly form of transport were recommended.

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.

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.

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.

Across the globe, policymakers are focusing on boosting sustainable transport options, notably cycling, to foster eco-friendly urban environments. However, the persistent safety challenges cyclists face continues to hinder these efforts. This research explores a novel hybrid methodology to investigate the determinants of cyclist crash severity by combining eXtreme Gradient Boosting (XGBoost) with SHapley Additive exPlanations (SHAP) and a random parameters logit model with heterogeneity in means and variances (RPLHMV). Using crash data from the Department for Transport covering crashes in Great Britain from 2016 to 2019, the research evaluates the methodology's effectiveness. The XGBoost-SHAP model reduced data dimensionality allowing the application of a robust statistical model, while the random parameters logit model with heterogeneity in means and variances captured heterogeneity in both means and variances. The statistical model identified 10 significant variables with fixed parameters for the fatal crashes, 22 significant variables for the serious injuries, and two indicator variables such as cyclist age ≤ 17 and overtaking as a manoeuvre for the second vehicle with statistically significant random parameters associated with serious injury outcomes. The relationships revealed by the logit framework were further examined using the XGBoost-SHAP, which provided deeper insights into the interactions between random and fixed parameters. The use of the hybrid approach allowed to achieve a very good R2 McFadden value of 0.52 for the RPLHMV, demonstrating the model's robustness. The hybrid approach not only provides a deeper understanding of crash severity dynamics but also helps in creating specific safety measures. This research can guide policymakers in identifying key factors and interactions that affect crash severity, leading to targeted safety improvements.