scholarly journals Analysis Factors That Influence Escalator-Related Injuries in Metro Stations Based on Bayesian Networks: A Case Study in China

2020 ◽  
Vol 17 (2) ◽  
pp. 481 ◽  
Author(s):  
Yingying Xing ◽  
Shengdi Chen ◽  
Shengxue Zhu ◽  
Jian Lu
Keyword(s):  
Bayesian Network ◽  
Expectation Maximization ◽  
Expert Knowledge ◽  
Evidence Theory ◽  
Influential Factor ◽  
Prevention Measures ◽  
Independence Test ◽  
Severe Injuries ◽  
Bayesian Network Structure

Escalator-related injuries have become an important issue in daily metro operation. To reduce the probability and severity of escalator-related injuries, this study conducted a probability and severity analysis of escalator-related injuries by using a Bayesian network to identify the risk factors that affect the escalator safety in metro stations. The Bayesian network structure was constructed based on expert knowledge and Dempster–Shafer evidence theory, and further modified based on conditional-independence test. Then, 950 escalator-related injuries were used to estimate the posterior probabilities of the Bayesian network with expectation–maximization (EM) algorithm. The results of probability analysis indicate that the most influential factor in four passenger behaviors is failing to stand firm (p = 0.48), followed by carrying out other tasks (p = 0.32), not holding the handrail (p = 0.23), and another passenger’s movement (p = 0.20). Women (p = 0.64) and elderly people (aged 66 years and above, p = 0.48) are more likely to be involved in escalator-related injuries. Riding an escalator with company (p = 0.63) has a relatively high likelihood of resulting in escalator-related injuries. The results from the severity analysis show that head and neck injuries seem to be more serious and are more likely to require an ambulance for treatment. Passengers who suffer from entrapment injury tend to claim for compensation. Severe injuries, as expected, significantly increase the probability of a claim for compensation. These findings could provide valuable references for metro operation corporations to understand the characteristics of escalator-related injuries and develop effective injury prevention measures.

Incorporating expert knowledge when learning Bayesian network structure: A medical case study

2011 ◽  
Vol 53 (3) ◽  
pp. 181-204 ◽  
Author(s):  
M. Julia Flores ◽  
Ann E. Nicholson ◽  
Andrew Brunskill ◽  
Kevin B. Korb ◽  
Steven Mascaro
Keyword(s):  
Bayesian Network ◽  
Network Structure ◽  
Expert Knowledge ◽  
Medical Case ◽  
Bayesian Network Structure

Efficient Risk Profiling Using Bayesian Networks and Particle Swarm Optimization Algorithm

2016 ◽  
pp. 1580-1612
Author(s):  
Goran Klepac ◽  
Leo Mrsic ◽  
Robert Kopal
Keyword(s):  
Particle Swarm Optimization ◽  
Bayesian Network ◽  
Network Structure ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Expert Knowledge ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Bayesian Network Structure ◽  
Risk Profiling

Chapter introduce usage of particle swarm optimization algorithm and explained methodology, as a tool for discovering customer profiles based on previously developed Bayesian network (BN). Bayesian network usage is common known method for risk modelling although BN's are not pure statistical predictive models (like neural networks or logistic regression, for example) because their structure could also depend on expert knowledge. Bayesian network structure could be trained using algorithm but, from perspective of businesses requirements model efficiency and overall performance, it is recommended that domain expert modify Bayesian network structure using expert knowledge and experience. Chapter will also explain methodology of using particle swarm optimization algorithm as a tool for finding most riskiness profiles based on previously developed Bayesian network. Presented methodology has significant practical value in all phases of decision support in business environment (especially for complex environments).

scholarly journals Hybrid Optimization Algorithm for Bayesian Network Structure Learning

Information ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 294 ◽  
Author(s):  
Xingping Sun ◽  
Chang Chen ◽  
Lu Wang ◽  
Hongwei Kang ◽  
Yong Shen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Network Structure ◽  
Structure Learning ◽  
Expert Knowledge ◽  
Hybrid Optimization ◽  
Experimental Simulation ◽  
Bayesian Network Structure ◽  
Bayesian Network Structure Learning

Since the beginning of the 21st century, research on artificial intelligence has made great progress. Bayesian networks have gradually become one of the hotspots and important achievements in artificial intelligence research. Establishing an effective Bayesian network structure is the foundation and core of the learning and application of Bayesian networks. In Bayesian network structure learning, the traditional method of utilizing expert knowledge to construct the network structure is gradually replaced by the data learning structure method. However, as a result of the large amount of possible network structures, the search space is too large. The method of Bayesian network learning through training data usually has the problems of low precision or high complexity, which make the structure of learning differ greatly from that of reality, which has a great influence on the reasoning and practical application of Bayesian networks. In order to solve this problem, a hybrid optimization artificial bee colony algorithm is discretized and applied to structure learning. A hybrid optimization technique for the Bayesian network structure learning method is proposed. Experimental simulation results show that the proposed hybrid optimization structure learning algorithm has better structure and better convergence.

scholarly journals MODELING THE SAILING RISK OF ROPAX SHIPS WITH BAYESIAN NETWORK

Transport ◽  
2014 ◽  
Vol 32 (4) ◽  
pp. 340-347 ◽  
Author(s):  
Qingcheng Zeng ◽  
Luyan Yang ◽  
Qian Zhang
Keyword(s):  
Sensitivity Analysis ◽  
Decision Support ◽  
Risk Analysis ◽  
Bayesian Network ◽  
Expectation Maximization ◽  
Development Process ◽  
Risk Mitigation ◽  
Parameter Learning ◽  
Provide Decision Support

In this paper, a methodology based on Bayesian Network (BN) was proposed to deal with the difficulty of risk analysis in RoPax transport. Based on data collection and expert survey, BN model for RoPax sailing risk analysis was constructed first. Then the Expectation Maximization (EM) algorithm for parameter learning and Evidence Prepropagation Importance Sampling (EPIS) algorithm for reasoning were designed. Finally, a sensitivity analysis was conducted. To validate the model algorithms, a case study on the RoPax system of Bohai gulf in China was provided. Results indicate that the BN model can effectively address the problem of data deficiency and mutual dependency of incidents in risk analysis. It can also model the development process of unexpected hazards and provide decision support for risk mitigation.

scholarly journals Generic Bayesian network models for making maintenance decisions from available data and expert knowledge

2018 ◽  
Vol 232 (5) ◽  
pp. 505-523 ◽  
Author(s):  
Haoyuan Zhang ◽  
D William R Marsh
Keyword(s):  
Bayesian Network ◽  
Expert Knowledge ◽  
Network Models ◽  
Rail Network ◽  
Periodic Inspection ◽  
Failure Data ◽  
Typical Data ◽  
Maintenance Decisions ◽  
Maintenance Decision

To maximise asset reliability cost-effectively, maintenance should be scheduled based on the likely deterioration of an asset. Various statistical models have been proposed for predicting this, but they have important practical limitations. We present a Bayesian network model that can be used for maintenance decision support to overcome these limitations. The model extends an existing statistical model of asset deterioration, but shows how (1) data on the condition of assets available from their periodic inspection can be used, (2) failure data from related groups of asset can be combined using judgement from experts and (3) expert knowledge of the deterioration’s causes can be combined with statistical data to adjust predictions. A case study of bridges on the rail network in Great Britain (GB) is presented, showing how the model could be used for the maintenance decision problem, given typical data likely to be available in practice.

scholarly journals Dynamic Knowledge Inference Based on Bayesian Network Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Deyan Wang ◽  
Adam AmrilJaharadak ◽  
Ying Xiao
Keyword(s):  
Bayesian Network ◽  
Message Passing ◽  
Expert Knowledge ◽  
Posteriori Probability ◽  
Inference Process ◽  
Network Nodes ◽  
A Posteriori Probability ◽  
Network Learning ◽  
Bayesian Network Structure ◽  
Message Passing Algorithm

On the basis of studying datasets of students' course scores, we constructed a Bayesian network and undertook probabilistic inference analysis. We selected six requisite courses in computer science as Bayesian network nodes. We determined the order of the nodes based on expert knowledge. Using 356 datasets, the K2 algorithm learned the Bayesian network structure. Then, we used maximum a posteriori probability estimation to learn the parameters. After constructing the Bayesian network, we used the message-passing algorithm to predict and infer the results. Finally, the results of dynamic knowledge inference were presented through a detailed inference process. In the absence of any evidence node information, the probability of passing other courses was calculated. A mathematics course (a basic professional course) was chosen as the evidence node to dynamically infer the probability of passing other courses. Over time, the probability of passing other courses greatly improved, and the inference results were consistent with the actual values and can thus be visualized and applied to an actual school management system.

Efficient Risk Profiling Using Bayesian Networks and Particle Swarm Optimization Algorithm

2016 ◽  
pp. 91-124 ◽  
Author(s):  
Goran Klepac ◽  
Leo Mrsic ◽  
Robert Kopal
Keyword(s):  
Particle Swarm Optimization ◽  
Bayesian Network ◽  
Network Structure ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Expert Knowledge ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Bayesian Network Structure ◽  
Risk Profiling

Chapter introduce usage of particle swarm optimization algorithm and explained methodology, as a tool for discovering customer profiles based on previously developed Bayesian network (BN). Bayesian network usage is common known method for risk modelling although BN's are not pure statistical predictive models (like neural networks or logistic regression, for example) because their structure could also depend on expert knowledge. Bayesian network structure could be trained using algorithm but, from perspective of businesses requirements model efficiency and overall performance, it is recommended that domain expert modify Bayesian network structure using expert knowledge and experience. Chapter will also explain methodology of using particle swarm optimization algorithm as a tool for finding most riskiness profiles based on previously developed Bayesian network. Presented methodology has significant practical value in all phases of decision support in business environment (especially for complex environments).

scholarly journals Causation Analysis of Hazardous Material Road Transportation Accidents by Bayesian Network Using Genie

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaoli Ma ◽  
Yingying Xing ◽  
Jian Lu
Keyword(s):  
Risk Factors ◽  
Bayesian Network ◽  
Evidence Theory ◽  
Severe Accident ◽  
Specific Factor ◽  
Road Transportation ◽  
Bayesian Network Structure ◽  
Theoretical Support ◽  
Hazmat Transportation ◽  
Causation Analysis

With the increase of hazardous materials (Hazmat) demand and transportation, frequent Hazmat road transportation accidents had arisen the widespread concern in the community. Thus, it is necessary to analyze the risk factors’ implications, which would make the safety of Hazmat transportation evolve from “passive type” to “active type”. In order to explore the influence of risk factors resulting in accidents and predict the occurrence of accidents under the combination of risk factors, 839 accidents that have occurred for the period 2015–2016 were collected and examined. The Bayesian network structure was established by experts’ knowledge using Dempster-Shafer evidence theory. Parameter learning was conducted by the Expectation-Maximization (EM) algorithm in Genie 2.0. The two main results could be likely to obtain the following. (1) The Bayesian network model can explore the most probable factor or combination leading to the accident, which calculated the posterior probability of each risk factor. For example, the importance of three or more vehicles in an accident leading to the severe accident is higher than less vehicles, and in the absence of other evidences, the most probable reasons for “explosion accident” are vehicles carrying flammable liquids, larger quantity Hazmat, vehicle failure, and transporting in autumn. (2) The model can predict the occurrence of accident by setting the influence degrees of specific factor. Such that the probability of rear-end accidents caused by “speeding” is 0.42, and the probability could reach up to 0.97 when the driver is speeding at the low-class roads. Moreover, the complex logical relationship in Hazmat road transportation accidents could be obtained, and the uncertain relation among various risk factors could be expressed. These findings could provide theoretical support for transportation corporations and government department on taking effective measures to reduce the risk of Hazmat road transportation.

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