ΟΑ2042. Leveraging smartphone telematics for urban traffic safety: A data-driven analysis of unsafe driving

Abstract Background The surge in smartphone-based telematics has opened new avenues in traffic safety research, enabling granular, real-time monitoring of driving behavior. Objectives This study investigates the relationship between unsafe driving events—particularly harsh braking and acceleration—and crash occurrences in urban environments. By integrating data from smartphone applications, traffic control systems, and digital maps, the research aims to identify high-risk urban junctions, understand key behavioral and infrastructural risk factors, and inform targeted safety interventions. Methods Data were collected via the OSeven smartphone application from over 300 drivers operating across Mesogeion and Vouliagmenis Avenues in Athens, capturing more than 10,000 harsh acceleration and braking events. This behavioral data was enriched with traffic metrics (e.g., speed, occupancy, flow) from 26 sensor loops, and road infrastructure characteristics (e.g., lane counts, entrance/exit presence) from Google Maps. The study employed a multi-method framework, including: • Clustering Analysis: K-Means, DBSCAN, Hierarchical Clustering, and Gaussian Mixture Models (GMM) to identify patterns of unsafe driving. • Machine Learning: A Random Forest Regressor and SHAP values for feature importance and crash risk prediction. • Statistical Analysis: Generalized Linear Models (GLM) and descriptive statistics to understand correlations. • Dimensionality Reduction: Principal Component Analysis (PCA) for model simplification and insight clarity. • Spatial Analysis: Local Moran’s I and Geary’s C to identify high-risk clusters and outlier locations. To address class imbalance in crash prediction, SMOTE (Synthetic Minority Over-Sampling Technique) was applied. Results Clustering revealed three dominant junction profiles, with one cluster (Cluster 1) showing high frequencies of harsh braking and high crash incidence, indicating urgent need for intervention. Random Forest models identified mean speed difference, braking probability (Prob_Brk), and braking frequency (Mod_Freq_Brk) as the strongest predictors of crash likelihood—accounting for over 85% of model importance collectively. Spatial analysis using Moran’s I and Geary’s C highlighted specific junctions (e.g., JM1, JM14, JM17) as hotspots of unsafe events, while also revealing spatial outliers with abnormal patterns. Multicollinearity analysis flagged high VIF scores for braking-related features (>75), suggesting strong internal correlation and justifying the use of PCA to ensure robust modeling. Conclusions This research demonstrates the value of smartphone-derived telematics in enhancing urban traffic safety. By combining advanced statistical and machine learning techniques, it presents a scalable, real-time solution for identifying high-risk locations and understanding the behavioral underpinnings of road crashes. Key messages • Driver behavior—especially harsh braking—is a more significant crash predictor than road design or traffic volume alone. • Clustering and spatial analysis enable a proactive approach to safety interventions, rather than waiting for crash reports. • The methodologies used can guide the design of smarter infrastructure, driver feedback systems, and targeted enforcement strategies. Future work should incorporate additional data sources (e.g., weather, lighting conditions), expand spatial scope, and experiment with deep learning methods for more nuanced behavioral insights. Topic Telematics Data, Crash Prediction, Urban Traffic Safety.