Review on: Severity estimation unit of automotive accident

Raut, Saroj; Karmore, Swapnili

doi:10.1109/icacea.2015.7164800

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…Scholars' research on accident risk is generally concentrated in the fields of coal mine, transportation, aviation and so on. The commonly used risk research methods include Logit model [4], negative binomial models [5], BP neural network [6], grey system theory [7], fuzzy comprehensive evaluation method [8], analytic hierarchy process [9,10], Bayesian network [11,12], spatial models [13,14], etc., focusing on the evaluation of research risk. The research on risk prediction mainly focuses on the fields of transportation [15], coal mine [16], earthquake [17], medicine [18], economy [19], computer [20], civil engineering [21] and so on.…”

Section: Introductionmentioning

confidence: 99%

Risk prediction and risk factor analysis of urban logistics to public security based on PSO-GRNN algorithm

Zhao

Wei

2020

PLoS ONE

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For the complicated operation process, many risk factors, and long cycle of urban logistics, it is difficult to manage the security of urban logistics and it enhances the risk. Therefore, to study a set of effective management mode for the safe operation of urban logistics and improve the risk prediction mechanism, is the primary research item of urban logistics security management. This paper summarizes the risk factors to public security in the process of urban logistics, including pick up, warehouse storage, transport, and the end distribution. Generalized regression neural network (GRNN) is combined with particle swarm optimization (PSO) to predict accidents, and the Apriori algorithm is used to analyze the combination of high-frequency risk factors. The results show that the method of combining GRNN with PSO is effective in accident prediction and has a powerful generalization ability. It can prevent the occurrence of unnecessary urban logistics public accidents, improve the ability of relevant departments to deal with emergency incidents, and minimize the impact of urban logistics accidents on social and public security.

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Section: Introductionmentioning

confidence: 99%

Risk prediction and risk factor analysis of urban logistics to public security based on PSO-GRNN algorithm

Zhao

Wei

2020

PLoS ONE

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“…At present, risk-based research is generally concentrated in the fields of automobile collision, coal mine, transportation of dangerous goods, and aviation. Commonly used risk research methods include BP neural network [9], fuzzy comprehensive evaluation method [10], analytic hierarchy process (AHP) [11,12], and Bayesian network (BN) [13,14], focusing on the evaluation of risk, but lack of risk identification and control research. Other traditional risk research methods are fault tree analysis (FTA) [15], probability theory [16], Swiss cheese model, human factor analysis, and classification system (HFACS) [17].…”

Section: Introductionmentioning

confidence: 99%

Risk Influencing Factor Analysis of Urban Express Logistics for Public Safety: A Chinese Perspective

Zhao

Qing-hua

et al. 2020

Mathematical Problems in Engineering

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Due to the uncertainty and complexity of multilinks and multifactors in urban express logistics system, it is very difficult to analyze the risk factors and the correlation among them for urban public security. In this paper, a method combining domain knowledge and data learning is proposed to construct Bayesian network, which can effectively deal with this problem. Based on the literature review and the investigation of transportation companies, this paper summarizes the risk factors to public safety caused by pick up, warehouse storage, transport, and the end distribution in the process of urban express logistics, which are divided into 5 dimensions: management, weather, human, transportation tools and facilities, and goods, including 11 risk factors. In this paper, Interpretative Structural Model is used to construct the initial hierarchical model to describe the complex relationship between factors, and then causal mapping method is used to improve the initial model to transform the structure into the final Bayesian network model. Finally, the sensitivity of one node to other nodes is analyzed based on the incident data. The results show that Bayesian network is effective in improving urban express logistics operation ability and avoiding public safety risks and has a strong generalization ability, which is simple and easy in practical application.

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Analysis of Road Accidents Prediction and Interpretation Using KNN Classification Model

Sahu¹,

Maram²,

Gampala³

et al. 2022

Emerging Technologies in Data Mining and Information Security

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Review on: Severity estimation unit of automotive accident

Cited by 5 publications

References 9 publications

Risk prediction and risk factor analysis of urban logistics to public security based on PSO-GRNN algorithm

Risk prediction and risk factor analysis of urban logistics to public security based on PSO-GRNN algorithm

Risk Influencing Factor Analysis of Urban Express Logistics for Public Safety: A Chinese Perspective

Analysis of Road Accidents Prediction and Interpretation Using KNN Classification Model

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