Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

항만 도시 교통물류 안전 증진을 위한 실시간 기상 변화 및 항만 영향권 특성 별 사고 영향요인 분석에 관한 연구open accessAdvanced computer learning-based accident severity analysis considering weather changes and port influence areas: Focused on South Korea Cases

Other Titles
Advanced computer learning-based accident severity analysis considering weather changes and port influence areas: Focused on South Korea Cases
Authors
박누리박준영
Issue Date
Mar-2024
Publisher
한국물류과학기술학회
Keywords
항만 안전; 사고 심각도 모형; 기상 데이터; 교통안전; 머신러닝; Port safety; Crash severity model; Weather data; Traffic safety; Machine learning
Citation
물류과학기술연구, v.5, no.1, pp 23 - 43
Pages
21
Indexed
KCICANDI
Journal Title
물류과학기술연구
Volume
5
Number
1
Start Page
23
End Page
43
URI
https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/118983
DOI
10.23178/jlst.5.1.202403.002
ISSN
2765-2351
Abstract
항만과 항만 인근 항만의 영향을 받는 도로는 대형 사고를 초래할 수 있는 화물차의 이동이 많기 때문에 교통안전에 각별한 주의가 필요하다. 따라서 사고 심각도를 낮출 수 있는 안전 관리 대책을 마련하기 위해 항만 도시의 도로 구간을 항만의 영향을 받는 정도에 따라 구분하고, 각 항만 영향권에서 사고 심각도에 영향을 미치는 요인을 도출해 안전 관리 전략을 수립할 필요가 있다. 본 연구에서는 항만 영향권에서 사고 심각도에 영향을 미치는 요인을 네 가지 머신러닝 기법을 통해 도출하고자 하였다. 모형 개발 후에는 가장 예측성능이 뛰어난 모형에 대하여 설명가능한 인공지능 기법을 통해 높은 사고 심각도에 영향을 미치는 요인을 도출하였다. 본 연구에서 도출된 결과를 활용하여 항만 지역의 사고 심각도 감소를 위한 정책 수립의 기초자료로 활용될 수 있을 것으로 기대된다.
Port safety management should consider a variety of cargo shifting within trucks and containers, occurring at and near port areas. In particular, it is crucial for port safety management to consider not only incidents directly 'at-port' but also those in the surrounding 'near-port' areas, including the port influence area. This is significant because of the potential for high crash severity at near port areas, given the substantial truck traffic that could lead to large-scale crashes. Therefore, developing management strategies for port city safety requires identifying key risk factors that influence crash severity in each port area. During this process, because the key factors influencing crash severity may vary as one gets closer to the port center, it is essential to take into account the size of the port influence area. This study collected and matched both crash and weather data to consider various variables. Additionally, this study developed four machine learning-based crash severity models, including Naive Bayes Classification, Support Vector Machine, Extreme Gradient Boosting, and Light Gradient-Boosting Machine. Furthermore, the identification of key factors influencing high crash severity is determined through the application of an eXplainable Artificial Intelligence technique. It is expected that findings derived from this study can contribute to policy-making efforts aimed at enhancing traffic safety in the port area.
Files in This Item
Go to Link
Appears in
Collections
COLLEGE OF ENGINEERING SCIENCES > DEPARTMENT OF TRANSPORTATION AND LOGISTICS ENGINEERING > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Park, June young photo

Park, June young
ERICA 공학대학 (DEPARTMENT OF TRANSPORTATION AND LOGISTICS ENGINEERING)
Read more

Altmetrics

Total Views & Downloads

BROWSE