Using contextual data to predict risky driving events: A novel methodology from explainable artificial intelligence. (May 2023)

• Record Type: Journal Article
• Authors: Masello, Leandro; Castignani, German; Sheehan, Barry; Guillen, Montserrat; Murphy, Finbarr
• Is Part Of: Accident analysis and prevention. Volume 184(2023)
• Publication Date: 2023-05
• Subjects: Driving context -- Explainable AI -- Machine learning -- Risk assessment -- Usage-based insurance; ADAS Advanced Driver Assistance -- Systems GLM Generalized Linear Models -- GNSS Global Navigation Satellite -- System IRI International Roughness Index -- MPD Mean Poisson Deviance -- PAYD Pay-as-you-drive -- PHYD Pay-how-you-drive -- PWYD Pay-where-you-drive -- RMSE Root Mean Squared Error -- SHAP Shapley Additive -- Explanations UBI Usage-based Insurance; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2023.106997
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: This study analyses the link between the driving context (where people drive) and risk. The impacts of the contextual factors on the predictions are ranked and interpreted using SHAP. It is found that the driving context has significant power in predicting driving risk. High-speed roads in warm temperatures increase the likelihood of harsh acceleration and braking. Speed limit, temperature, traffic conditions, and road slope are the leading contexts for most risk events. Abstract: Usage-based insurance has allowed insurers to dynamically tailor insurance premiums by understanding when and how safe policyholders drive. However, telematics information can also be used to understand the driving contexts experienced by the driver within each trip (e.g., road types, weather, traffic). Since different combinations of these conditions affect exposure to accidents, this understanding introduces predictive opportunities in driving risk assessment. This paper investigates the relationships between driving context combinations and risk using a naturalistic driving dataset of 77, 859 km. In particular, XGBoost and Random Forests are used to determine the predictive significance of driving contexts for near-misses, speeding and distraction events. Moreover, the most important contextual factors in predicting these risky events are identified and ranked through Shapley Additive Explanations. The results show that the driving context has significant power in predicting driving risk. Speed limit, weather temperature, wind speed, traffic conditions and road slope appear in the top ten most relevant features for most risky events. Analysing contextual feature variations and their influence on risky events showed that low-speed limits increase the predicted frequency of speeding and phone unlocking events, whereas high-speed limits decrease harsh accelerations. Low temperatures decrease the expected frequency of harsh manoeuvres, and precipitations increase harsh acceleration, harsh braking, and distraction events. Furthermore, road slope, intersections and pavement quality are the most critical factors among road layout attributes. The methodology presented in this study aims to support road safety stakeholders and insurers by providing insights to study the contextual risk factors that influence road accident frequency and driving risk.