GIS-based assessment of pedestrian-vehicle accidents in terms of safety with four different ML models
Küçük Resim Yok
Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Taylor & Francis Inc
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this study, both micro and macro level evaluation of pedestrian-vehicle crashes were conducted. Macro-level findings were obtained with GIS-based density analyzes, and critical road segments were determined. The data on road characteristics, traffic characteristics, built environment and land use were collected in 70 critical urban road segments. While conducting micro-level research, commonly used multilayer perceptron and C4.5 decision tree, as well as innovative converted fuzzy-decision model and revised fuzzy-decision model, which significantly reduces the expert judgements on fuzzy models, were used. Significant rules were extracted, and were evaluated from safety perspective. Information gain ratio was used to deal with the black-box structure of machine learning models and to examine independent factors in-depth. The best performance was achieved in revised fuzzy decision model with 68.57% accuracy. The results revealed that land use, parking and peak hour volume have high effect, as well as public transport, speed and road type factors have the greatest effect on pedestrian safety. In the light of the results, various managerial implications such as controlling the density of public transport on main arterials, preventing stop-and-go effects, and monitoring vehicle speeds especially during peak hours were suggested to improve pedestrian safety.
Açıklama
Anahtar Kelimeler
Pedestrian safety, geographical information system, machine learning, fuzzy logic, accident analysis, Road Traffic Accidents, Crash Injury Severity, Built Environment, Spatial-Analysis, Decision Rules, Risk-Factors, New-York, Membership Functions, Density-Estimation, Logit Model
Kaynak
Journal Of Transportation Safety & Security
WoS Q Değeri
Q4
Scopus Q Değeri
Q1
Cilt
14
Sayı
9
