Applying the Accumulator model to predict driver's reaction time based on looming in approaching and braking conditions

INTRODUCTION

The prediction of when the driver will react to a change in the lead vehicle motion is critical for assessing rear-end crash risk using car-following models. Past studies have assumed constant reaction time and driver's continuous reaction. However, these assumptions are not valid as the driver's reaction time can vary in different car-following situations and the driver does not continuously react to the lead vehicle motion. Thus, this study predicted the driver's reaction time using the Wiedemann car-following model and the Accumulator model. The Accumulator model assumes the driver's start of reaction based on the accumulation of looming and thereby reflects the driver's intermittent reaction.

METHOD

Fifty drivers' behavior was observed using a driving simulator in two scenarios: (1) approach and follow a moving lead vehicle and (2) approach a stopped lead vehicle. The Accumulator model predicted the reaction times based on different looming variables (angular velocity and tau-inverse), lead vehicle type (car and truck), and lead vehicle brake lights (on or off).

RESULTS

The Accumulator model showed lower prediction errors of the reaction time than the Wiedemann model, which assumes reaction based on the fixed looming threshold. The Accumulator model predicted the reaction times more accurately when it was calibrated with the angular velocity due to width and height of lead vehicles. Moreover, the Accumulator model with tau-inverse produced the smallest prediction error of reaction times among different Accumulator models and the Wiedemann model when lead vehicle brake lights were on.

CONCLUSIONS

This study demonstrates that the Accumulator model is a promising method of predicting the driver's reaction time in car-following situations, which affects rear-end crash risk.

PRACTICAL APPLICATIONS

The Accumulator model can be incorporated into a car-following model for the prediction of reaction times and can estimate the rear-end collision risk of vehicles more accurately.

In-depth analysis of drivers' merging behavior and rear-end crash risks in work zone merging areas

This study investigates the drivers' merging behavior and the rear-end crash risk in work zone merging areas during the entire merging implementation period from the time of starting a merging maneuver to that of completing the maneuver. With the merging traffic data from a work zone site in Singapore, a mixed probit model is developed to describe the merging behavior, and two surrogate safety measures including the time to collision (TTC) and deceleration rate to avoid the crash (DRAC) are adopted to compute the rear-end crash risk between the merging vehicle and its neighboring vehicles. Results show that the merging vehicle has a bigger probability of completing a merging maneuver quickly under one of the following situations: (i) the merging vehicle moves relatively fast; (ii) the merging lead vehicle is a heavy vehicle; and (iii) there is a sizable gap in the adjacent through lane. Results indicate that the rear-end crash risk does not monotonically increase as the merging vehicle speed increases. The merging vehicle's rear-end crash risk is also affected by the vehicle type. There is a biggest increment of rear-end crash risk if the merging lead vehicle belongs to a heavy vehicle. Although the reduced remaining distance to work zone could urge the merging vehicle to complete a merging maneuver quickly, it might lead to an increased rear-end crash risk. Interestingly, it is found that the rear-end crash risk could be generally increased over the elapsed time after the merging maneuver being triggered.