Causality-Network-Based Critical Hazard Identification for Railway Accident Prevention: Complex Network-Based Model Development and Comparison

Li, Qian; Zhang, Zhe; Peng, Fei

doi:10.3390/e23070864

Cited by 11 publications

(6 citation statements)

References 34 publications

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“…In this method, the feature vectors of the original dataset are used as the nodes of the complex network, and the relationships among the feature vectors are used as the edges of the complex network to construct the complex network [32,33]. The construction process is shown as follows:…”

Section: The Construction Of Complex Networkmentioning

confidence: 99%

Cause Analysis and Accident Classification of Road Traffic Accidents Based on Complex Networks

Wang,

Zhai,

Cao

et al. 2023

Applied Sciences

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The number of motor vehicles on the road is constantly increasing, leading to a rise in the number of traffic accidents. Accurately identifying the factors contributing to these accidents is a crucial topic in the field of traffic accident research. Most current research focuses on analyzing the causes of traffic accidents rather than investigating the underlying factors. This study creates a complex network for road traffic accident cause analysis using the topology method for complex networks. The network metrics are analyzed using the network parameters to obtain reduced dimensionality feature factors, and four machine learning techniques are applied to accurately classify the accidents’ severity based on the analysis results. The study divides real traffic accident data into three main categories based on the factors that influences them: time, environment, and traffic management. The results show that traffic management factors have the most significant impact on road accidents. The study also finds that Extreme Gradient Boosting (XGBoost) outperforms Logistic Regression (LR), Random Forest (RF) and Decision Tree (DT) in accurately categorizing the severity of traffic accidents.

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Section: The Construction Of Complex Networkmentioning

confidence: 99%

Cause Analysis and Accident Classification of Road Traffic Accidents Based on Complex Networks

Wang,

Zhai,

Cao

et al. 2023

Applied Sciences

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“…Li et al [7] investigates a critical hazard identification method for railway accident prevention. A new accident causation network is proposed to model the interaction between hazards and accidents.…”

Section: The Expanded Si Publication Listmentioning

confidence: 99%

Special Issue “Complex Dynamic System Modelling, Identification and Control”

Zhu

Fusco

et al. 2022

Entropy

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“…However, Kampung Rambutan Terminal has been operating without significant progress, giving rise to a number of problems. There are several variables, such as unclean terminals and inefficient vehicle circulation, such as the existence of bay terminals, as well as the irregularity of public transportation vehicles in carrying out their activities [12,13]. Reported from the media that there was a friction at the terminal caused by a conflict over passengers between ticket sellers of bus Muhammad Isradi, ETJ Volume 08 Issue 07 July 2023 companies, and this conflict led to a fight and caused injuries to the conflicting parties (detik.com, 2020).…”

Section: Introductionmentioning

confidence: 99%

Analysis of User Community Satisfaction Kampung Rambutan Terminal

Firdaus,

Isradi,

Prasetijo

et al. 2023

ETJ

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The terminal is a manifestation of a road transportation node that serves the community as a transportation location, such as intra- and intermodal locations for passengers and loading and unloading of goods, as well as regulating the smooth flow of arrivals and departures of public vehicles. The purpose of this study is to determine the level of satisfaction and the level of service performance using objective measures to highlight changes that should be made to the Kampung Rambutan terminal service. The method used is to calculate based on the results of a questionnaire based on the standardization of PM No. 40 of 2015 and then process the results using the Customer Satisfaction Index (CSI) method to determine the public's perspective on services at the Kampung Rambutan terminal and Importance Performance Analysis (IPA) to map attributes that require priority improvement at the Kampung Rambutan terminal. The results of the analysis through CSI obtained a satisfaction index of 54.40%, meaning that service users are quite satisfied with the services provided. Meanwhile, IPA obtained 10 service attributes that require priority improvement. There are several important aspects that should be considered, such as environmental cleanliness, facilities for users with disabilities.

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Causality-Network-Based Critical Hazard Identification for Railway Accident Prevention: Complex Network-Based Model Development and Comparison

Cited by 11 publications

References 34 publications

Cause Analysis and Accident Classification of Road Traffic Accidents Based on Complex Networks

Cause Analysis and Accident Classification of Road Traffic Accidents Based on Complex Networks

Special Issue “Complex Dynamic System Modelling, Identification and Control”

Analysis of User Community Satisfaction Kampung Rambutan Terminal

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