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Article

A Study on Traffic Accident Risk Prediction of Chartered Bus Company Using System Dynamics

시스템 다이내믹스 분석기법을 활용한 전세버스 업체 교통사고위험예측에 관한 연구

Donguk WON1
,
Gyu-ho KIM2
,
Myung Sik DO3
,
Soobeom LEE4*
원 동욱1
,
김 규호2
,
도 명식3
,
이 수범4*
1CEO, Rasan Engineering, Seoul 06575, Korea
2Ph.D. Candidate, Department of Urban Engineering, Hanbat National University, Daejeon 34158, Korea
3Professor, Department of Urban Engineering, Hanbat National University, Daejeon 34158, Korea
4Professor, Department of Transportation Engineering, University of Seoul, Seoul 20504, Korea
1라산엔지니어링 대표
2한밭대학교 도시공학과 박사과정
3한밭대학교 도시공학과 교수
4서울시립대학교 교통공학과 교수
*Corresponding Author
31 August 2022. pp. 517-538
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Abstract

This study aims to develop a traffic accident risk prediction model for a chartered bus using the system dynamics analysis methodology that is based on the system thinking. System dynamics is not only a tool that makes the systematic thought to a model but also a computer program-based analysis methodology that mathematically models the system varying according to time variation. This study analyzed the causal relationship based on the interrelation among variables and structured them by considering various variables that affect traffic accidents from the perspective of traffic safety management. In the study, data for past 10 year ‧ from 2008 to 2017) were utilized, the target year was set to 2028 and the influence according to time variation was identified. Variables were categorized into 12 exogenous variables, 13 endogenous variables and 5 policy variables. Together with these, the prediction model, which has the traffic accident risk index considering accident frequency and accident severity as an objective variable, was developed. In terms of the prediction model development, the results of this study are summarized as follows. First, this study classified 30 factors, which have direct or indirect influence on the traffic accident risk of the chartered bus, into human factor, organizational factor and policy factor, afterwards, developed the system dynamics sub-model that is based on mutual causal relationship and correlation among each variable. Second, it was predicted that the traffic accident risk index of 2028 that is the target year in future will continuously decrease with -3.6% in the average annual increasing rate and will reach a half of that in 2008. Third, as a result of development in the integrated model that connects each sub-model, it was revealed that the traffic accident risk of the chartered bus was significantly influenced by driver fatigues, traffic safety investment, driver driving hour, safety culture index, driver break hour, dangerous driving and smart car. The validity of developed model was analyzed through unit check and a sensitivity analysis by time step change, a model goodness of fit check and a sensitivity analysis for the p-values and prediction model validation of single year. The results of this study are not predictive-oriented model development as in previous studies, but rather an empirical predictive model to respond to changes in the future political, social, and industrial environment. It can be used in various fields such as traffic safety policy.

Keywords
causal loop diagram
stock-flow model
system dynamics
traffic accident

본 연구는 시스템 사고에 기초한 시스템 다이내믹스 분석 방법론을 이용해 전세버스의 교통사고위험을 예측하기 위한 모델을 개발하는데 목적이 있다. 시스템 다이내믹스(System Dynamics)는 시스템적 사고(System Thinking)를 모델화하는 도구이자, 시간의 변화에 따라 변화하는 시스템을 수학적으로 모델링하는 컴퓨터 프로그램 기반의 분석 방법론이다. 본 연구에서는 이러한 기업의 교통안전관리 차원에서 교통사고에 영향을 미치는 다양한 변수들에 대한 고려와 함께 각 변수들간의 상호관련성에 기초한 인과관계를 분석하고 이를 구조화하고자 시스템 다이내믹스 방법론을 활용하였다. 본 연구에서는 과거 10년간(2008-2017년) 데이터를 활용해 목표연도를 2028년으로 설정하고, 12개 외생변수와 13개 내생변수 및 5개 정책변수로 구분하여 교통사고위험지수(사고빈도와 심각도 고려)를 목적변수를 하는 예측모델을 개발하였다. 예측모델 개발 측면에서 본 연구의 결과를 요약하면 다음과 같다. 첫째, 전세버스 교통사고위험에 직접적 그리고 간접적 영향을 미치는 30개의 변수를 인적요인, 조직요인, 정책요인, 차량요인, 도로환경요인 등으로 구분하고 각 변수들간의 상호 인과관계, 상관관계를 기초로 하는 시스템 다이내믹스 예측모델을 개발하였다. 둘째, 각 변수의 예측 서브모델을 개발하고 각각의 서브모델을 연결하는 통합모델을 개발한 결과, 장래 목표연도인 2028년 교통사고위험지수는 연평균증가율 -3.6%로 감소하여, 2008년 대비 절반 수준에 이를 것으로 예측되었다. 셋째, 전세버스 교통사고위험에는 운전자 피로도, 교통안전투자, 운전자 운전시간, 휴게시간, 운전자 안전의식, 운전자 휴게시간, 위험행동, 첨단차량대수 등이 큰 영향을 미치고 있는 것으로 분석되었다. 모델의 검증은 연구모델의 단위점검, 타임스텝 변화에 따른 민감도분석, 모델 적합도 체크(결정계수 검토), 계수값에 대한 민감도 분석, 단일년도 예측모델 검증 등을 통해 모델의 타당성 분석을 실시하고, 예측모델의 객관성을 검증하였다. 본 연구결과는 기존 선행연구에서처럼 예측식 위주의 모델 개발이 아니라, 정책적 ‧ 사회적 ‧ 산업적 미래환경 변화에 대응하기 위한 실증적 예측모델로서, 의사결정자의 결정, 판단에 기여할 것으로 판단되며, 운수업체 뿐만 아니라 교통안전정책 등 다양한 분야에서 활용이 가능하다.

키워드
인과지도
저량-유량 모델
시스템 다이내믹스
교통사고위험
References
1
Bottani E., Monica L., Vignali G. (2009), Safety Management System: Performance Differences between Adopters and Non-Adopters, Safety Science, 47, 155-162. 10.1016/j.ssci.2008.05.001
2
Cho J. R. (2015), The Effect of Organizational Safety Management’s System Factors on Safety Performance - Focusing on Workers’ Safety Perception, Ph.D. Thesis, Inha University.
3
Do C. W., Kim H. S., Kim K. H., Lee S. B., Cho H. J. (2016), Traffic Safety Engineering: Cheongmoonkak.
4
Forrester J. W. (1958), Industrial Dynamics, A Major Breakthrough for Decision Makers, Harvard Bus, Rev. 36, 27-66.
5
Gyotong New (Transportation New), http://www.gyotongn.com/news/articleView.html?idxno=229434 , 2019.5.14.
6
ICAO (2013), Safety Management Manual (SMM).
7
Im D. W. (2018), Analysis of Traffic Crashes Based on Digital Tachograph and Driver’s Personal Traits - Focused on Expressway Bus Drivers, Ph.D. Thesis, Seoul National University.
8
Inchen metropolitan Chartered Bus conveyance business Association (ICBA) (2013), Carriage Cost of Chartered Bus in Incheon.
9
Jo G. H. (2013), The Relation of Commercial Motor Vehicle Driver’s Fatigue and Traffic Accident, Masters Dissertation, Ewha Womans University.
10
Jung K. M. (2017), A Study on the Traffic Safety Management Plan of Transportation Companies Using Statistical Model - Focused on Traffic Safety Diagnosis, Ph.D. Thesis, Korea National University of Transportation.
11
Kim C. O. (2013), Safety Engineering (Disaster Management), Seoul National University of Science and Technology.
12
Kim D. H., Moon T. H., Kim D. H. (2001), System Dynamics, Daeyoung.
13
Kim J. Y. (2015), Development for City Bus Driver’s Accident Occurrence Prediction Model Based on Digital Tachometer Records, Ph.D. Thesis, Myongji University.
14
KISTEP (2008-2017), Evaluation for Technologies Level.
15
Korea Chartered Bus Conveyance Business Association Alliance (KCBA) (2008-2017a), Enterprises of Chartered Bus.
16
Korea Chartered Bus Conveyance Business Association Alliance (KCBA) (2008-2017b), Operating Profit of Chartered Bus.
17
Korea Chartered Bus Conveyance Business Association Alliance (KCBA) (2008-2017c), Registered Car and Vehicle Distance of Chartered Bus.
18
Korea Chartered Bus Conveyance Business Association Alliance (KCBA) (2008-2017d), Worker’s Wage of Chartered Bus.
19
Korea Transportation Safety Authority (TS) (2012), System Introduction for Commercial Vehicle’s Driving Hours Limits.
20
Korea Transportation Safety Authority (TS) (2012-2018), Traffic Culture Index Survey Report.
21
Korea Transportation Safety Authority (TS) (2016), A Study on the Prevention of Traffic Accidents for Business (Second).
22
Korea Transportation Safety Authority (TS) (2016-2018a), Dangerous Driving of Chartered Bus.
23
Korea Transportation Safety Authority (TS) (2016-2018b), Driver Education Data of Chartered Bus.
24
Korea Transportation Safety Authority (TS) (2016-2018c), Traffic Law Violation Data of Chartered Bus.
25
Korea Transportation Safety Authority (TS) (2017), Average Mileage of Automobile.
26
Korean Law Information Center (KLIC) (2018), Traffic Safety Act.
27
KOROAD (2008-2018), Traffic Accident Statistics-Traffic Accident Cost.
28
KOSTAT (2008-2017), Employment and Wage-Work (over 5 people), National Accounts and Local Accounts-GDP, Price and Household Account.
29
KOSTAT (2008-2028), Population and Household-estimated Future Population.
30
KOSTAT (2016-2017), Employment and Wage-Work’s Debt.
31
KOSTAT (2016-2018), Consumer Price Index.
32
KOSTAT (2017-2018), Survey Entrepreneurship (Enterprise sector)-Vision and Strategies.
33
KTO (2008-2017), Foreigner Tourist (entrance) Statistics.
34
Kwak S. M., Yoo J. K. (2016), System Dynamics Modeling and Simulation.
35
Lee G. I. (2013), A Study on Legal Problems of Freelancer Owner Driver System in Charter Bus Business, Masters Dissertation, Chungnam National University.
36
Lim G. H. (2008), The Study on the Establishment of Traffic Safety Management System for the Company with Large Scaled Vehicle, Ph.D. Thesis, Myongji University.
37
Lim Y. G. (2011), The Effects of the Commercial Vehicle’s Service Quality and Dangerous Driving on Customer Satisfaction and Job Satisfaction – Focus on Commercial Bus, Ph.D. Thesis, University of Seoul.
38
MoLIT (2008-2017), Registered Car Statistics.
39
MoLIT (2011), Plan for Functional Vitalization and Safety Management for Chartered Bus.
40
MoLIT (2013a), Countermeasures to Rationalize Regulations for Chartered Bus Transportation Business.
41
MoLIT (2013b), Development of Indicators for Passenger Transportation Industry Trend Analysis and Effective Assistance System.
42
MoLIT (2016), Master Plan for 8th National Traffic Safety.
43
Moon T. H. (2007), Sustainable City from System Thinking, Jipmoondang.
44
O S. U. (2016), The Impact of Construction Companies Qualitative, Quantitative Safety Management Activities and Employee’s Safety Awareness, Organizational Trust, Ph.D. Thesis, Hoseo University.
45
O’Toole M. (2002), The Relationship between Employees’ Perceptions of Safety and Organization Culture, Journal of Safety Research, 33, 231-243. 10.1016/S0022-4375(02)00014-2
46
Park W. I. (2019), Study on Development of Bus Traffic Accident Models Considering Driver’s Working Conditions, Ph.D. Thesis, Ajou University. 10.7470/jkst.2019.37.4.322
47
Rim C. W. (2016), Analysis on Factors of Business Vehicle’s Traffic Accident Using Structural Equation Model by Sectors, Ph.D. Thesis, Hongik University.
48
Rumar, K. (1985), The Role of Perceptual and Cognitive Filters in Observed Behavior, Human Behavior and Traffic Safety, New York, NY: Plenum Press, 151-165. 10.1007/978-1-4613-2173-6_8
49
Seo D. H. (2016), The Effect of the Safety Health Activity on the Casualty Loss Expense of the Corporation, Masters Dissertation, Kyungpook National University.
50
Shire M. I. et al. (2018), The Application of System Dynamics Modeling to System Safety Improvement: Present Use and Future Potential, Safety Science 106, 104-120. 10.1016/j.ssci.2018.03.010
51
The Korea Transport Institute (KOTI) (2012), Improvement of Age Limits for Passenger Car Transport.
52
The Korea Transport Institute (KOTI) (2017), Improvement of Driving and Rest Hours for Commercial Vehicle Drivers - Focusing on Passenger Car Transport.
53
The Korea Transport Institute (KOTI) (2018), Performance Analysis about Supply-Demand of Chartered Bus.
54
UN (2011), Global Plan for the Decade of Action for Road Safety.
55
Vredenburgh A. G. (2002), Organizational Safety: Which Management Practices are Most Effective in Reducing Employee Injury Rate?, Journal of Safety Research, 33, 259-276. 10.1016/S0022-4375(02)00016-6
Information
  • Publisher :Korean Society of Transportation
  • Publisher(Ko) :대한교통학회
  • Journal Title :Journal of Korean Society of Transportation
  • Journal Title(Ko) :대한교통학회지
  • Volume : 40
  • No :4
  • Pages :517-538
  • Received Date : 2022-01-28
  • Revised Date : 2022-02-23
  • Accepted Date : 2022-06-30
  • DOI :https://doi.org/10.7470/jkst.2022.40.4.517
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