Insights for Improving Road Safety：Focusing on Vehicle Accidents in Daegu Metropolitan City
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
Road accidents not only caused loss of human lives but also costed 3% of gross domestic product in most of the countries. The road accidents pose significant challenges to public safety and urban transportation management. There is a need to identify the high-risk area of accidents along with the critical day of week and vulnerable time period in order to implement effective preventive measures and optimizing the resource allocation. We collected 5,012 accident data from 대구교통종합정보. This study identified the high-risk locations, days of week, and time periods for accidents in Daegu and estimated the conditional probabilities of accidents occurring based on combinations of location, day of the week, and time period. The result is visualized in the form of dashboard in Tableau. This study holds substantial practical significance for urban planners, transportation authorities, and policymakers in Daegu to strategically allocate resources for traffic management, law enforcement, and targeted safety campaigns.
Abstract
Road accidents not only caused loss of human lives but also costed 3% of gross domestic product in most of the countries. The road accidents pose significant challenges to public safety and urban transportation management. There is a need to identify the high-risk area of accidents along with the critical day of week and vulnerable time period in order to implement effective preventive measures and optimizing the resource allocation. We collected 5,012 accident data from 대구교통종합정보. This study identified the high-risk locations, days of week, and time periods for accidents in Daegu and estimated the conditional probabilities of accidents occurring based on combinations of location, day of the week, and time period. The result is visualized in the form of dashboard in Tableau. This study holds substantial practical significance for urban planners, transportation authorities, and policymakers in Daegu to strategically allocate resources for traffic management, law enforcement, and targeted safety campaigns.
참고문헌 (28)
- "Road traffic injuries" (2022)[URL]
- "Leading Causes of Death" (2023)[URL]
- "2022 Transportation Census Report" (2023)[URL]
- "Accidents According To Metropolitan Cities" (2023)[URL]
- S.M. Hosseinian, "Analysis of factors affecting urban road accidents in rasht metropolis", Eng Transactions, 1, pp.1~4 (2020)
- K. Bucsuházy, "Human factors contributing to the road traffic accident occurrence", Transportation Research Procedia, 45, pp.555~561 (2020)
- J.J. Rolison, "What are the factors that contribute to road accidents? An assessment of law enforcement views, ordinary drivers' opinions, and road accident records", Accident Analysis & Prevention, 115, pp.11~24 (2018)
- A.S. Al-Ghamdi, "Using logistic regression to estimate the influence of accident factors on accident severity", Accident Analysis & Prevention, 34(6), pp.729~741 (2002)
- L. Eboli, "Factors influencing accident severity : an analysis by road accident type", Transportation Research Procedia, 47, pp.449~456 (2020)
- F. Rezaie Moghaddam, "Prediction of accident severity using artificial neural networks", International Journal of Civil Engineering, 9(1), pp.41~48 (2011)
- V. Shankar, "Statistical analysis of accident severity on rural freeways", Accident Analysis & Prevention, 28(3), pp.391~401 (1996)
- 황영은, "대구광역시 교통약자 보행자 교통사고 공간 군집 분석", 디지털융복합연구, 20(3), pp.75~83 (2022), 한국디지털정책학회[DOI]
- S.H. Park, "Exploring the determinants of the severity of pedestrian injuries by pedestrian age: a case study of Daegu Metropolitan City, South Korea", International journal of environmental research and public health, 17(7), pp.2358~ (2020)
- 김윤용, "대구지역 교통사고 유형별 위험요인 분석", 한국데이터정보과학회지, 31(3), pp.503~510 (2020), 한국데이터정보과학회[DOI]
- K. Goniewicz, "Road accident rates : strategies and programmes for improving road traffic safety", European Journal of Trauma and Emergency Surgery, (42), pp.433~438 (2016)
- C. Cabrera-Arnau, "Uncovering the behaviour of road accidents in urban areas", Royal Society Open Science, 7(4), pp.191739~ (2020)
- S. Kumar, "A data mining approach to characterize road accident locations", Journal of Modern Transportation, 24, pp.62~72 (2016)
- "Accident Information" (2022)[URL]
- B. Zhao, "Encyclopedia of big data", pp.1~3 (2017), Springer Cham
- "Geopy" (2023)[URL]
- R. Warner, "Optimizing the Display and Interpretation of Data", pp.117~134 (2016), Elsevier
- J. M. Joyce, "Handbook of the History of Logic, vol. 10", pp.415~475 (2011)
- "Naïve Bayes Classifier" (2018)[URL]
- A. Oikawa, "Application of conditional probability analysis to distant metastases from lung cancer", Oncology Letters, 3(3), pp.629~634 (2012)
- K. Orfanogiannaki, "Conditional probability approach of the assessment of tsunami potential : application in three tsunamigenic regions of the Pacific Ocean", Pure and Applied Geophysics, 164, pp.593~603 (2007)
- L. Lin, "Identification of corporate distress in UK industrials : a conditional probability analysis approach", Applied Financial Economics, 14(2), pp.73~82 (2004)
- "What are the benefits and drawbacks of different normalization techniques?" (2023)[URL]
- H. Henderi, "Comparison of Min-Max normalization and Z-Score Normalization in the K-nearest neighbor(kNN)Algorithm to Test the Accuracy of Types of Breast Cancer", International Journal of Informatics and Information Systems, 4(1), pp.13~20 (2021)