Accident factor analysis based on different age groups via AdaBoost algorithm

Abstract

A person’s vision, perception, judgment, and operation of a vehicle decline with age. To analyze the influence of age on traffic accidents, we apply the adaptive boosting algorithm (AdaBoost) to investigate the most significant factors for two age groups (older and young driver groups) based on real-world accident data in California. Accident factors include gender, road type, pavement condition, weather, time of day, vehicle behavior, etc., as well as their corresponding subfactors. We first train some weak learners to find importance and then linearly combine those weak learners into a unified stronger learner. The proposed method has several advantages: (1) ability to handle unbalanced data, (2) no requirement on the assumption of data distribution, and (3) being robust for different datasets. Results show that the major factors regarding road safety for older drivers are weather and time of day, while for young drivers are traffic violations and vehicle behaviors.