scholarly journals Bayesian Network-Based Knowledge Graph Inference for Highway Transportation Safety Risks

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Luo Wenhui ◽  
Cai Fengtian ◽  
Wu Chuna ◽  
Meng Xingkai
Keyword(s):  
Bayesian Network ◽  
Road Traffic ◽  
Expert Knowledge ◽  
Search Algorithm ◽  
Causal Chain ◽  
Transportation Safety ◽  
Knowledge Graph ◽  
Network Parameter ◽  
Safety Risks ◽  
Highway Transportation

Accurate inference of knowledge about highway transportation safety risks forms a crucial aspect of building a knowledge graph. Based on the data related to highway transportation accidents, this study has developed a Bayesian network model. The initial identification of the network nodes is through expert scoring. The network structure is then constructed by utilizing the prior expert knowledge and K2 greedy search algorithm. Later, the network parameters are trained via the expectation-maximization (EM) algorithm. Finally, knowledge about highway transportation safety risks is inferred using the junction tree algorithm. A comparison is made between the trained conditional and actual probabilities during the network parameter training to verify the validity of the proposed model that accords with expert experience, thereby proving the model validity. Further, its main “causal chain” is inferred to be an improper emergency response-human failure-accident occurrence, where the probability of driver failure is 82%, and the probability of accident occurrence is 68% by taking “a certain road traffic accident” as an example. There is consistency between the inference results and the actual accident sequence that suggests the effectiveness of the proposed knowledge inference method.

Traffic Incident Duration Estimation Based on a Dual-Learning Bayesian Network Model

2018 ◽  
Vol 2672 (45) ◽  
pp. 196-209 ◽  
Author(s):  
Haozhe Cong ◽  
Cong Chen ◽  
Pei-Sung Lin ◽  
Guohui Zhang ◽  
John Milton ◽  
...  
Keyword(s):  
Bayesian Network ◽  
Expert Knowledge ◽  
Transportation Systems ◽  
Practical Significance ◽  
Incident Management ◽  
Highway Traffic ◽  
Traffic Incidents ◽  
Traffic Incident ◽  
Incident Duration ◽  
Traffic Incident Management

Highway traffic incidents induce a significant loss of life, economy, and productivity through injuries and fatalities, extended travel time and delay, and excessive energy consumption and air pollution. Traffic emergency management during incident conditions is the core element of active traffic management, and it is of practical significance to accurately understand the duration time distribution for typical traffic incident types and the factors that influence incident duration. This study proposes a dual-learning Bayesian network (BN) model to estimate traffic incident duration and to examine the influence of heterogeneous factors on the length of duration based on expert knowledge of traffic incident management and highway incident data collected in Zhejiang Province, China. Fifteen variables related to three aspects of traffic incidents, including incident information, incident consequences, and rescue resources, were included in the analysis. The trained BN model achieves favorable performance in several areas, including classification accuracy, the receiver operating characteristic (ROC) curve, and the area under curve (AUC) value. A classification matrix, and significant variables and their heterogeneous influences are identified accordingly. The research findings from this study provide beneficial reference to the understanding of decision-making in traffic incident response and process, active traffic incident management, and intelligent transportation systems.

scholarly journals Bayesian Network Models with Decision Tree Analysis for Management of Childhood Malaria in Malawi

2020 ◽  
Author(s):  
Sanya B. Taneja ◽  
Gerald P. Douglas ◽  
Gregory F. Cooper ◽  
Marian G. Michaels ◽  
Marek J. Druzdzel ◽  
...  
Keyword(s):  
Decision Tree ◽  
Bayesian Network ◽  
Clinical Features ◽  
Predictive Models ◽  
Clinical Decision Making ◽  
Performance Metrics ◽  
Expert Knowledge ◽  
Clinical Decision ◽  
Automated Method ◽  
Childhood Malaria

Abstract Background: Malaria is a major cause of death in children under five years old in low- and middle-income countries such as Malawi. Accurate diagnosis and management of malaria can help reduce the global burden of childhood morbidity and mortality. Trained healthcare workers in rural health centers manage malaria with limited supplies of malarial diagnostic tests and drugs for treatment. A clinical decision support system that integrates predictive models to provide an accurate prediction of malaria based on clinical features could aid healthcare worker in judicious use of testing and treatment. We developed Bayesian network (BN) models to predict the probability of malaria from clinical features and an illustrative decision tree to model the decision to use or not use a malaria rapid diagnostic test (mRDT).Methods: We developed two BN models from data that were collected in a national survey of outpatient encounters of children in Malawi. The target diagnosis is taken as the result of mRDT. The first BN model was created manually with expert knowledge, and the second model was derived using an automated method followed by modifications guided by expert knowledge. The performance of the BN models was compared to other statistical models on a range of performance metrics. We developed a decision tree that integrates predictions from these predictive models with the costs of mRDT and a course of recommended treatment. Results: Compared to the logistic regression and random forest models, the BN models had similar accuracy of 64% but had higher sensitivity at the cost of lower specificity at the default threshold. Sensitivity analysis of the decision tree showed that at low (below 0.04) and high (above 0.4) probabilities of malaria in a child, the preferred decision that minimizes expected costs is not to perform mRDT.Conclusion: In resource-constrained settings, judicious use of mRDT is important. Predictive models in combination with decision analysis can provide personalized guidance on when to use mRDT in the management of childhood malaria. BN models can be efficiently derived from data to support such clinical decision making.

An intuitive risk factors search algorithm: usage of the Bayesian network technique in personalized medicine

2014 ◽  
Vol 42 (1) ◽  
pp. 71-87 ◽  
Author(s):  
Evgeny D. Maslennikov ◽  
Alexey V. Sulimov ◽  
Igor A. Savkin ◽  
Marina A. Evdokimova ◽  
Dmitry A. Zateyshchikov ◽  
...  
Keyword(s):  
Risk Factors ◽  
Personalized Medicine ◽  
Bayesian Network ◽  
Search Algorithm

scholarly journals Effects of Improved Traffic Management on Sustainable Distributed Road Transportation Safety Based on Asian Experiences

2010 ◽  
Vol 14-15 (1) ◽  
pp. 321-332
Author(s):  
Irfan Ullah ◽  
Janusz Szpytko
Keyword(s):  
Traffic Safety ◽  
Traffic Engineering ◽  
Road Safety ◽  
Traffic Management ◽  
Road Traffic ◽  
Action Plan ◽  
Monitoring And Evaluation ◽  
Transportation Safety ◽  
Data Systems ◽  
Road Transportation

Effects of Improved Traffic Management on Sustainable Distributed Road Transportation Safety Based on Asian Experiences Knowledge of the improved traffic management and its effect on road safety is an important component in the process of sustainable road network development. Having in mind that so many road traffic casualties occur, a key priority in transport policy is making highways as safe as possible. The mentioned objective is attained by means of sustainable safety. The paper focuses on sustainable road safety and his principles, based on selected Asian countries experiences. The knowledge base covers institutional responsibility of road safety, the development of a road safety action plan, raising awareness and understanding of road safety problems, road crash data systems, road safety education and training, traffic safety legislation, enforcement of traffic laws and monitoring and evaluation of the effectiveness of road safety activities. In order to improve road transportation safety an organized sustainable approach is needed via concentrated action of all of participants in the process of realizing the transport service such as: governments at all levels, those who are professionally engaged in transport and traffic engineering and road users themselves.

Evaluation of Bayesian Network Structure Learning Using Elephant Swarm Water Search Algorithm

2020 ◽  
pp. 139-159
Author(s):  
Shahab Wahhab Kareem ◽  
Mehmet Cudi Okur
Keyword(s):  
Simulated Annealing ◽  
Bayesian Network ◽  
Network Structure ◽  
Structure Learning ◽  
Search Algorithm ◽  
Confusion Matrix ◽  
Data Sets ◽  
Greedy Search ◽  
Bayesian Network Structure ◽  
Bayesian Network Structure Learning

Bayesian networks are useful analytical models for designing the structure of knowledge in machine learning which can represent probabilistic dependency relationships among the variables. The authors present the Elephant Swarm Water Search Algorithm (ESWSA) for Bayesian network structure learning. In the algorithm; Deleting, Reversing, Inserting, and Moving are used to make the ESWSA for reaching the optimal structure solution. Mainly, water search strategy of elephants during drought periods is used in the ESWSA algorithm. The proposed method is compared with Pigeon Inspired Optimization, Simulated Annealing, Greedy Search, Hybrid Bee with Simulated Annealing, and Hybrid Bee with Greedy Search using BDeu score function as a metric for all algorithms. They investigated the confusion matrix performances of these techniques utilizing various benchmark data sets. As presented by the results of evaluations, the proposed algorithm achieves better performance than the other algorithms and produces better scores as well as the better values.

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