SHERP

Implementing Surrogate Safety Measures to Driving Simulator and its Applicability
Driving Simulator내 적용가능한 Surrogate Safety Measures의 선정

DC Field Value Language
dc.contributor.advisorChungwon Lee-
dc.contributor.author홍준의-
dc.date.accessioned2018-05-29T03:07:49Z-
dc.date.available2018-05-29T03:07:49Z-
dc.date.issued2018-02-
dc.identifier.other000000151110-
dc.identifier.urihttp://hdl.handle.net/10371/141325-
dc.description학위논문 (석사)-- 서울대학교 대학원 : 공과대학 건설환경공학부, 2018. 2. Chungwon Lee.-
dc.description.abstractSince 93% of all traffic accidents are related to human factors, the prevention of human errors is important for reducing the number of traffic accidents, and safe-driving education can contribute to this goal. Thus, a many safe-driving education programs using driving simulators (DSs) have been developed to reduce crashes that occur due to human errors. However, DSs are not highly utilized because DS-based education has several limitations, such as risk-free decisionmaking, simulator sickness, and the lack of reality. Of special concern is that DS-based education generally is conducted without considering the interaction between the vehicle represented by the DS and surrounding vehicles. Thus, the goal of this study was to determine whether a driver's aggressive driving can be evaluated by considering her or his interaction with surrounding vehicles in comparison to the surrogate safety measures (SSMs) currently used in driver's education using DSs. Thus we used a traffic flow model to realistically represent the movement of vehicles surrounding the DS. After reviewing the literature, we used various SSMs as indicators to identify aggressive driving, and the usefulness and applicability of the the SSMs were reviewed through an experimental study. The results showed that 20 of the SSMs were significant measures that could be used in DS-based education. An additional study is needed to evaluate the effects of driving education based on the SSMs proposed in this research and to develop a scoring system that integrates several SSMs into a comprehensive index for evaluating the safety effects of drivers’ education.-
dc.description.tableofcontentsChapter 1. Introduction 1

Chapter 2. Methodology 20

Chapter 3. Results of the Analyses 20

Chapter 4. Conclusions 20

References 30

요약(국문 초록) 35
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dc.formatapplication/pdf-
dc.format.extent1006589 bytes-
dc.format.mediumapplication/pdf-
dc.language.isoen-
dc.publisher서울대학교 대학원-
dc.subjectSurrogate Safety Measure-
dc.subjectDriving Simulator-
dc.subjectdriving education-
dc.subject.ddc624-
dc.titleImplementing Surrogate Safety Measures to Driving Simulator and its Applicability-
dc.title.alternativeDriving Simulator내 적용가능한 Surrogate Safety Measures의 선정-
dc.typeThesis-
dc.description.degreeMaster-
dc.contributor.affiliation공과대학 건설환경공학부-
dc.date.awarded2018-02-
Appears in Collections:
College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Civil & Environmental Engineering (건설환경공학부)Theses (Master's Degree_건설환경공학부)
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