Road Rage and Aggressive Driving Behaviour Detection in Usage-Based Insurance Using Machine Learning

Road Rage and Aggressive Driving Behaviour Detection in Usage-Based Insurance Using Machine Learning

Subramanian Arumugam, R. Bhargavi
Copyright: © 2023 |Pages: 29
DOI: 10.4018/IJSI.319314
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Driving behaviour is a critical issue in modern transportation systems due to the increasing concerns about the safety of drivers, passengers, and road users. Machine learning models are capable of learning driving patterns from sensor data and recognizing individuals by their driving behaviours. This paper presents a novel framework for aggressive driving detection and driver classification based on driving events identified from GPS data collected with smartphones and heart rate of the driver captured with a wearable device. The proposed system for road rage and aggressive driving detection (RAD) is realized with an integral framework with components for data acquisition, event detection, driver classification, and model interpretability. The system is implemented by generating a prediction model by training machine learning classifiers with a dataset collected in a cohort to classify drivers into good, unhealthy, road rage, and always bad. The proposed system is to improve road safety and to customize insurance premiums in the best interest of policy holders and insurance companies.
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Driving behaviours are the main cause of road accidents and one of the main sources of insurance claims. To improve road safety and reduce the number of insurance claims, it is important to identify driving behaviours in order to adapt the insurance contract accordingly. Identifying abnormal driving behaviour is an important task for Usage-Based Insurance (UBI) companies as it can help them to assess a customer’s risk and price their policies accordingly. Driving behaviours such as speeding and aggressive and careless driving are of particular interest to UBI companies as they are directly related to an individual’s risk of an accident. Car driving behaviours can be measured by data such as speed, acceleration/deceleration, lane position and headway. Accident proneness refers to a general disposition or personality trait that increases the likelihood of an individual being involved in an accident (Shinar, 2017). Differences in accident proneness are generally caused by a number of factors, such as gender, age, personality traits, driving experience, attitude towards driving, road conditions and environmental stimuli. Figure 1 illustrates the factors influencing driver behaviour.

Driving style is defined as a habitual driving behaviour, characterizing a driver’s tendencies to behave in specific ways on a regular basis (Sagberg et al., 2015). It also describes how a driver’s driving style affects the safety of the individual and others on the road. The identification of such habits has become increasingly important for the development of insurance companies as it can help them to identify high-risk drivers, estimate risk and set an insurance premium accordingly.

Abnormal driving is generally characterized by atypical or risky behaviour that is not in line with the norms for a particular group of drivers (Hu et al., 2017). There are a number of different types of abnormal driving, but the most relevant for UBI are those that are associated with an increased risk of an accident, such as speeding, aggressive driving and careless driving. Road rage is a brief, intense reaction to perceived provocation in a situation of conflict between two or more persons on the road, characterized by verbal abuse, shoving, hitting, threatening and possibly minor or major physical aggression (Shinar, 1998). Aggressive driving is characterized by hostile, impatient and risky behaviour such as speeding, tailgating, weaving in and out of traffic and running red lights.

Figure 1.

Factors Influencing Driving Behaviour


The World Health Organization (WHO) report on road traffic injuries reveals that around 1.3 million people die in road crashes every year (WHO, 2022). These crashes are identified as causing around 3% loss of Gross Domestic Product (GDP) in most countries. Further, 20 to 50 million people are susceptible to injuries, resulting in disabilities and long-term health conditions. Figure 2 depicts the fatalities in road accidents over the past few years, which are increasing every year. Along with the Bloomberg Initiative for Global Road Safety (BIGRS), the WHO strives to reduce fatalities and help governments develop a long-term sustainable plan for safety and road traffic injury prevention, and define guidelines and principles of a comprehensive road safety approach. Technological interventions in road rage and aggressive driving behaviour are crucial for this objective.

Figure 2.

Yearly Fatalities in Road Accidents


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