Sofia Nardoianni

Pedestrian safety represents a critical priority for the development of sustainable urban mobility systems. This study proposes an innovative methodological framework integrating supervised and unsupervised learning techniques with econometric modeling to identify and interpret risk scenarios. Using the Italian national dataset from 2010 to 2023, an XGBoost model has been initially trained and tested. Then, SHapley Additive exPlanations (SHAPs) have been applied to highlight contributing factors. Using the resulting SHAP values, a K-Means clustering algorithm was finally employed to segment crashes into homogeneous clusters. For each cluster, a Generalized Linear Mixed Model incorporating geographic random intercepts and temporal random slopes was calibrated. Through this hybrid-stratified approach, three risk scenarios have been identified, primarily driven by demographic vulnerability. For elderly pedestrians, the involvement of heavy vehicles nearly doubles the odds of a fatal outcome. Crash dynamics varied significantly: heavy vehicles and speeding nearly double the fatality risk for elderly pedestrians; nighttime represents a severe hazard for adults (OR = 3.87) and youths (OR = 7.99), with the latter also highly penalized by unsafe road behaviors (OR = 3.12). From a spatio-temporal perspective, random effects revealed that the Islands (Sicily and Sardinia) are the most critical macro-areas (+55.2% baseline risk for adults) and the North-West the safest. Furthermore, the COVID-19 pandemic mitigated fatal risk for young pedestrians nationwide, had a neutral impact on the elderly, and for adults was protective in Southern regions but corresponded to higher odds of mortality in the North, reflecting altered traffic dynamics.

The implementation of Raised Pedestrian Crossings (RPCs) is one of the most common strategies used to reduce vehicle speeds in urban areas, thereby enhancing road safety levels. The understanding of driver speed profile variability is critical for designing effective traffic-calming policies (such as Zone 30) that account for the full range of driver responses. This study analyzed 19,840 discrete speed measurements collected from 2480 unique drivers at eight longitudinal points approaching and departing an RPC, located in the city of Cassino, Italy. The model takes into account two distinct directional approaches due to the different slopes that characterize the case study road segments that converge at the RPC. To overcome the statistical bias of simple OLS models, a Linear Mixed-Effects Model (LMEM) for each direction has been implemented. This hierarchical approach correctly models the nested data structure (measurements within drivers) and quantifies the variance related to individual driver behavior. Within this LMEM approach, the aim is to evaluate how drivers decelerate and accelerate before and after the RPC. This step is crucial because understanding vehicle and driver behavior on the RPC provides the basis for implementing effective speed reduction strategies, such as the 30 km/h Zone.

Following the fostering of sustainable mobility policies in urban areas, accidents involving vulnerable road users have increased recently, prompting more detailed analyses of effective countermeasures. Solutions based on traffic calming may represent a valuable answer to this problem. However, the inner principles governing their performance have not been sufficiently studied. This work focuses on vertical traffic calming devices, with the aim of developing a new performance-based modeling framework to ensure proper and refined speed control by vehicles near the device, ensuring the safe passage of pedestrians. A key design variable is the vehicle fleet traveling on the road where the traffic calming device is installed. This aspect was investigated by analyzing vertical dynamic interaction by means of a mathematical model carried out for different vehicle types, highlighting similar vertical acceleration (RMS) experienced by different vehicles when passing at different speeds over a defined raised pedestrian crossing. The model was calibrated through speed profiles extracted from video-based measurements. Future developments will involve implementing a driving simulator scenario to generalize and further validate the current findings.

Improving cyclist safety conditions in the urban context is a key strategy to promote sustainable transport modes and reduce noise and environmental pollution. Recent plans have also addressed this point. In September 2020, the UN General Assembly declared the Decade of Action for Road Safety 2021–2030, aiming to reduce the number of road deaths by at least half. To achieve this task and highlight the risk factor, after collecting and pre-processing cyclist crash data in the city of Rome between 2013 and 2020, Random Forest and Ordered Logistic Regression models are proposed. The crash dataset is also enriched with vehicular speed and flows, and geographical information. A DBSCAN Clustering Analysis is also proposed to identify anomalous areas in the city. The findings show that the presence of cycle paths, the presence of anthropic activities, such as shops, schools, and universities, play a risk mitigation role. Conversely, vehicular speed and heavy vehicles emerge as the main detected risk factors. Finally, spatial analysis indicates that commercial activities reduce cycle path safety due to complex interactions with other road users. Furthermore, historic areas present unique risks driven by pedestrian flows and poor road surfaces, despite low vehicular traffic.