Quantifying visual road environment to establish a speeding prediction model: An examination using naturalistic driving data.

Quantifying visual road environment to establish a speeding prediction model: An examination using naturalistic driving data. Accid Anal Prev. 2019 Jun 06;129:289-298 Authors: Yu B, Chen Y, Bao S Abstract Speeding is one of the major contributors to traffic crashes. To solve this problem, speeding prediction is recognized as a critical step in a pre-warning system. While previous studies have shown that speeding is affected by road environmental design, research in predicting speeding behavior through road environment features has not yet been conducted. Furthermore, there is a large discrepancy between actual and perceived road environmental information given that a driver's visual perception plays a crucial role as the dominant source of information in determining driver's behavior. Thus, this paper aims to establish a speeding prediction model based on quantifying the visual road environment to improve the design of pre-waring systems, which can predict whether drivers are going to speed and provide them with visual or/and audio warnings about their current driving speed and the speed limit prior to the occurrence of speeding behavior. Twenty input variables derived from three categories including visual road environment parameters, vehicle kinematic features, and driver characteristics were considered in the proposed speeding prediction model. Especially, the road environmental design factors consisting of the visual road geometr...
Source: Accident; Analysis and Prevention. - Category: Accident Prevention Authors: Tags: Accid Anal Prev Source Type: research