Title

Assessment Of Driver Compliance On Roadside Safety Signs With Auditory Warning Sounds Generated From Pavement Surface–A Driving Simulator Study

Keywords

Auditory warning sounds; Driver compliance; Driving simulator; Roadside safety signs

Abstract

A variety of word messages are used in highways in different forms to inform drivers of traffic safety information or to influence positively drivers’ behavior. These include direct word messages for a particular event (such as road work) or general safety messages that warn drivers of risky driving behaviors (such as distracted driving and speeding). However, it is often observed that many drivers even do not recognize the safety messages despite being displayed on roadside signs in a fairly good visibility condition. The present study focused on an engineering method, namely auditory warning sound (AWS), which calls driver's attention on driving tasks and helps them comply with roadside safety signs. A driving simulator experiment was conducted to assess effects of AWS on driver compliance to roadside safety signs. AWS was implemented into driving simulator scenarios as a parameter to generate a certain level of growling warning sounds when subject vehicles are entering within a legibility distance of a roadside safety sign. The present study described laboratory setup and data for the driving simulator experiment, and drew conclusions on driver compliance to roadside safety signs with and without presence of AWS. The experiment results show that drivers are more compliant to roadside safety signs when AWS is used. It is expected that AWS will greatly help drivers comply with roadside safety signs where a specific safety concern is raised, such as a work-zone or a drowsy driving advisory zone.

Publication Date

2-1-2018

Publication Title

Journal of Traffic and Transportation Engineering (English Edition)

Volume

5

Issue

1

Number of Pages

1-13

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.jtte.2017.09.001

Socpus ID

85042198647 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85042198647

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