Quantifying the Effects of Visual Road Information on Drivers’ Speed Choices to Promote Self-Explaining Roads

Abstract

Roads should deliver appropriate information to drivers and thus induce safer driving behavior. This concept is also known as “self-explaining roads” (SERs). Previous studies have demonstrated that understanding how road characteristics affect drivers’ speed choices is the key to SERs. Thus, in order to reduce traffic casualties via engineering methods, this study aimed to establish a speed decision model based on visual road information and to propose an innovative method of SER design. It was assumed that driving speed is determined by road geometry and modified by the environment. Lane fitting and image semantic segmentation techniques were used to extract road features. Field experiments were conducted in Tibet, China, and 1375 typical road scenarios were picked out. By controlling variables, the driving speed stimulated by each piece of information was evaluated. Prediction models for geometry-determined speed and environment-modified speed were built using the random forest algorithm and convolutional neural network. Results showed that the curvature of the right boundary in “near scene” and “middle scene”, and the density of roadside greenery and residences play an important role in regulating driving speed. The findings of this research could provide qualitative and quantitative suggestions for the optimization of road design that would guide drivers to choose more reasonable driving speeds.

Assessing Subjective and Objective Road Environment Perception in the Bangkok Metropolitan Region, Thailand: A Deep Learning Approach Utilizing Street Images

“…However, the comprehensive evaluation based on commuters' perceptions varies depending on the urban context [29]. Qin et al [30] utilized image semantic segmentation techniques to extract road features, enabling the exploration of a speed decision model based on visual road information. In a similar vein, Wang et al [31] applied street view imagery alongside deep learning techniques to investigate neighborhood perception and its relationship with physical activity.…”

Self-Explaining Roads: Effects of road design on speed choice

“…The elements labeled for the OCL roads were number of lanes, type of separation of driving direction, edge marking type, trees next to the road, curve presence, type of background environment, guardrail presence, overhead sign presence and the presence of slip roads. These elements were selected for labeling because they have been shown to affect driving speed or road safety in the past (Aarts et al, 2005;;Davidse, van Driel & Goldenbeld, 2004;Dijkstra, Schermers & Petegem, 2021;Martens, 1997;Van Driel, Davidse & Van Maarseveen, 2004;Qin, Chen & Lin, 2020;) and/or because these elements are good candidate elements for Self-Explaining Roads. For a collection of all 320 images that were used in this study, see the Open Science Framework link of this project (https://osf.io/rcq7s/).…”

Self-explaining roads: What does visual cognition tell us about designing safer roads?

“…Australia launched the "Safe System Infrastructure" initiative (Turner et al 2009 , p7), in which they describe explicitly “ a self-explaining road is a term from the Netherlands which describes a road which is designed in such a way that drivers will automatically understand what is required of them, including speed choice ” (see also Fildes and Lee 1993 ). In China, the principles of SER were applied in a simulation study determining which elements in the road environment would determine driving speed (Qin et al 2020 ). In India, which has a very high fatality rate, the need for better road design and geometric standards is stated including the idea of using SER principles (Tiwari 2015 ).…”