Effect of Using Mobile Phones on Driver’s Control Behavior Based on Naturalistic Driving Data

Zhang, Lanfang; Cui, Boyu; Yang, Minhao; Guo, Feng; Wang, Junhua · 2019 · openalex

DOI: 10.3390/ijerph16081464

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Summary

This study investigates the impact of mobile phone use on drivers’ control behaviors using naturalistic driving data, addressing the limitations of previous simulator-based research which often failed to capture real-world complexity. While mobile phone distraction is a leading cause of accidents, existing regulations and studies frequently overlook cognitive interference, focusing instead on visual or manual distractions. The authors argue that simulator studies lack ecological validity because participants cannot choose when to engage in distractions based on driving conditions. To address this, the study utilizes data from the Shanghai Natural Driving Study (SH-NDS), which provides unobtrusive, high-precision data on actual driving processes. The researchers extracted 134 cases of mobile phone use from the SH-NDS dataset, which involved 60 drivers and over 750,000 km of driving data. To minimize environmental interference, samples were screened for specific conditions: driving on expressways in continuous traffic flow, with no concurrent events or surrounding vehicle interference. Mobile phone use was categorized into five distinct operations: answering, dialing, talking and listening, hanging up, and viewing information. The study employed a "moving time window" method to analyze dynamic changes in driving control. Specifically, it calculated the standard deviation of kinematic parameters within one-second windows to measure three metrics: the intensity of control activity, the sensitivity of control operations, and the stability of the control state. This approach allowed for the detection of subtle fluctuations in driving behavior that might be masked by mean values. The empirical results demonstrated a strong correlation between distracted operations and changes in driving control behavior. By distinguishing between different sub-tasks, the study revealed that various operations affect driving performance differently, challenging the view that mobile phone use impacts driving uniformly. The analysis showed that cognitive distraction, particularly during talking and listening, significantly interferes with the driver’s ability to process visual information and maintain stable control, even when manual and visual distractions are minimal. The moving time window method proved effective in capturing these dynamic variations, providing a more nuanced understanding of how attention reallocation during phone use compromises driving stability and sensitivity. The findings contribute to a deeper understanding of natural driver behavior changes during mobile phone use, highlighting the significant role of cognitive distraction in impairing control behavior. This research provides valuable insights for transport safety management, suggesting that regulations and safety interventions should account for the varying risks associated with different types of phone interactions, not just handheld calls. By leveraging naturalistic data, the study offers a more realistic assessment of distraction risks, supporting the development of more effective strategies to mitigate distracted driving accidents.

Key finding

There is a strong correlation between specific mobile phone operations during driving and significant changes in drivers' control behavior intensity, sensitivity, and stability.

Methodology

naturalistic

Sample size: 134

Provenance

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• mobile phones
• manual
• distraction laws
• visual manual
• visual
• distraction detection algorithms

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

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