Distracted Car-Following Behavior Analysis and Modeling Based on Large-Scale Naturalistic Driving Experiment

Abstract:

Driving distraction is a common abnormal behavior which seriously disturbs the safety and stability of traffic flow. Because the vehicle has a large degree of freedom in the longitudinal car-following（CF） driving context， most of the distracted driving behaviors exist in the CF driving context. Therefore， it is of great significance to deeply understand the characteristics of the distracted CF， and accurately describe the distracted CF behavior. This paper classified and modeled distracted the CF behavior based on behavioral characteristics and explored the adaptability of CF models in different states using model calibration methods. First， a large number of distracted CF samples were extracted based on the naturalistic driving data of Shanghai which spaned more than 3 years， and the distracted CF events were preliminarily classified into five kinds based on the stimulus-response framework of driving behavior. Then， the adaptabilities of four existing classical CF models （GHR， Gipps， IDM， and Wiedemann） on distracted CF behavior were compared， and the classification of distracted CF was optimized according to the results of the model adaptability using five-fold cross-validation. As a result， three types of distracted CF behavior （numbness reaction， overreaction， and delay reaction） were determined. Finally， two modeling strategies for the distracted CF behavior were discussed （AIDM and TDIDM）. The results show that rational calibration of the IDM model can accurately describe different types of distracted CF behavior.

Key words:traffic flow;distraction driving;car-following(CF) model;naturalistic driving data;driver behavior