Examining the Impact of Driver Distraction on Speeding Through the Exploitation of Smartphone Sensor Data

Papantoniou, Panagiotis; Tripsas, Andrew; Pavlou, Dimosthenis · 2026 · Crossref

DOI: 10.1007/978-3-031-88974-5_18

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Summary

This study investigates the impact of driver distraction, specifically mobile phone use, on speeding behavior by leveraging smartphone sensor data. Motivated by the complex nature of road safety and the established link between distraction and crash risk, the research aims to quantify how distraction influences speed violations across different road environments. The authors seek to determine if mobile phone use correlates with increased speeding and to identify other contributing factors such as road type, driving dynamics, and demographic characteristics. The methodology involved a naturalistic driving experiment conducted in Greece over six months, involving 100 drivers aged 18–65. Data were collected using the OSeven smartphone application, which utilizes machine learning algorithms to detect speeding events, mobile phone usage, harsh accelerations, and braking patterns while ensuring GDPR compliance. Participants also completed a questionnaire covering socio-demographic traits, driving habits, and attitudes toward road safety. The researchers developed four multiple lognormal regression models to predict the percentage of speeding: one overall model and three specific to highway, rural, and urban road types. Independent variables included mobile phone use, harsh accelerations, average deceleration, distance traveled, risky hours driving (22:00–05:00), gender, and age. The results demonstrated that driver distraction significantly affects speeding, though the correlation varies by road type. In overall, highway, and rural models, mobile phone use was positively associated with increased speeding, likely due to distracted drivers focusing on their devices and inadvertently accelerating. Conversely, in urban environments, mobile phone use showed a negative correlation with speeding, attributed to the presence of intersections and signaling that force speed reduction. Across all models, harsh accelerations, distance traveled, and driving during risky hours were positively correlated with speeding, while average deceleration was negatively correlated. Demographic analysis revealed that male drivers and younger, less experienced drivers exhibited higher speeding percentages compared to female and older drivers. In the overall model, distance traveled had the greatest influence on speeding, followed by risky hours driving and mobile phone use. The findings highlight that distracted driving leads to aggressive behaviors, particularly on highways and rural roads, while urban infrastructure mitigates this risk. The study confirms that longer distances and night driving exacerbate speeding tendencies. These results provide critical insights for policymakers and industry stakeholders, identifying specific risk factors such as gender, age, and context-dependent distraction effects. The authors suggest that future research should employ factor analysis and microscopic econometric techniques to further refine the understanding of driving behavior and safety interventions.

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-09; verification: verified.

Topics

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• visual
• speed choice
• distraction detection algorithms
• mobile phones

Information type

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• Empirical Findings: observational prevalence, behavioral performance data
• Methodological Resource: dataset resource