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Investigating Relationships Between Phone Use While Driving Behavior and Drivers’ Socio-demographic Characteristics: An Interpretable Machine Learning Approach

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Abstract

Phone use while driving (PUWD) is the most common distracted driving behavior. Considering that distracted driving is a preventable cause of crashes, researchers and practitioners want to understand this behavior to offer effective interventions. This research utilizes a dataset containing massive phone use events from Android phone users in Texas from 2018 to 2020 to explore the relationships between PUWD behavior and drivers’ socio-demographic factors at the census tract level. EXtreme Gradient Boosting (XGBoost) algorithm is adopted to perform the classification task: high PUWD rate or low PUWD rate. SHapley Additive exPlanations (SHAP) algorithm is established on the classification model to investigate the possible associations between socio-demographic factors and PUWD behavior. Our analysis indicates poverty, education attainment, sex, age groups, and income levels are dominantly associated with PUWD behavior. The census tracts with a higher- or lower-income level are more likely to be classified to have a high PUWD rate, while drivers from census tracts with middle-income levels (between 50 and 120 k) drive more defensively. The impact of income on the PUWD rate is also reflected through the education attainment factor. The results also demonstrate the strong association between a younger male population and a high PUWD rate. The census tracts are less likely to be classified to have a high PUWD rate while the median age of the male population increases. These findings demonstrate the potential to help transportation agencies target regions in greater need of anti-distracted driving interventions.

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Raw data is not available for sharing with the public based on the confidentiality agreement with the data provider.

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Acknowledgements

The authors acknowledge SAFE2SAVE, LLC for providing valuable datasets under their terms and conditions. We would also like to express our greatest appreciation to our four reviewers who have provided many valuable suggestions and insights to make this paper better and more robust.

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Authors and Affiliations

  1. Texas A&M University, 3135 TAMU, College Station, TX, 77843, USA

    Xiaoqiang Kong, Yunlong Zhang, Xiaoyu Guo, Xiao Xiao & Zihao Li

  2. The University of Texas at Austin, 305 E 23rd St, Austin, TX, 78712, USA

    Amy Zhang

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  1. Xiaoqiang Kong
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  2. Amy Zhang
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  3. Yunlong Zhang
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  4. Xiaoyu Guo
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Contributions

Dr. Yunlong Zhang and Dr. Xiaoqiang Kong were responsible for formulating the problem and building the analytical structure. Dr. Xiaoqiang Kong, Amy Zhang, Xiaoyu Guo, and Xiao Xiao collaborated to write the main manuscript. Zihao Li made significant contributions to the coding process.

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Correspondence to Yunlong Zhang.

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Kong, X., Zhang, A., Zhang, Y. et al. Investigating Relationships Between Phone Use While Driving Behavior and Drivers’ Socio-demographic Characteristics: An Interpretable Machine Learning Approach. Data Sci. Transp. 6, 8 (2024). https://doi.org/10.1007/s42421-024-00092-3

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