Mobile Phone Sensor-based Nigerian Driving Dataset to Detect Alcohol-influenced Behaviours

• URL: http://arxiv.org/abs/2509.05358v1
• Date: Wed, 03 Sep 2025 15:21:03 GMT
• Title: Mobile Phone Sensor-based Nigerian Driving Dataset to Detect Alcohol-influenced Behaviours
• Abstract summary: This paper presents a unique driving dataset collected in Nigeria via mobile phone sensors to support a machine learning model for detecting alcohol-influenced driving behaviours.<n>The proposed model leverages smartphone sensors such as accelerometers, gyroscopes, and GPS to provide a non-invasive, continuous solution for detecting impaired driving patterns in real time.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: This paper presents a unique driving dataset collected in Nigeria via mobile phone sensors to support a machine learning model for detecting alcohol-influenced driving behaviours, with the long-term aim of integrating this model into a mobile application that encourages safer driving behaviours. Driving under the influence of alcohol is a major public safety concern, particularly in low-income countries like Nigeria, where traditional enforcement mechanisms may be limited. The proposed model leverages smartphone sensors such as accelerometers, gyroscopes, and GPS to provide a non-invasive, continuous solution for detecting impaired driving patterns in real time. This study adapts existing data processing and pattern matching methodologies to label real-world driving data collected from Nigerian drivers, which are then used to train the model. A decision tree classifier is developed to detect alcohol influence, based on behavioural and temporal features, achieving a recall of 100%, a precision of 60%, and an F1 score of 75%. The model's overall accuracy was 90.91%, ensuring that no alcohol influenced trips were missed. Key predictive features included speed variability, course deviation, and time of day, which align with established patterns of alcohol consumption. This study contributes to the field by demonstrating how machine learning can be applied in low-resource environments to improve road safety. The findings suggest that the model can significantly enhance the detection and prevention of risky driving behaviours, with the potential for future integration into mobile applications to provide real-time feedback and encourage safer driving practices. This scalable and accessible solution offers a new approach to addressing road safety challenges in regions where traditional interventions are inadequate.

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