A Bayesian Network Approach to Causation Analysis of Road Accidents Using Netica

Zou, Xin; Yue, Wen Long

doi:10.1155/2017/2525481

Cited by 64 publications

(41 citation statements)

References 55 publications

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“…The deployment of the Bayesian methodology in recent decades has been developed for various subject areas for learning, modeling, forecasting and decision-making [ 32 , 33 ]. As regards the regulatory alignment and traffic safety research, Bayesian networks have been used to assess the safety impact of red-light cameras on the reduction of traffic signal violations [ 34 ], predict road safety hotspots [ 35 ], analyze the causation of road accidents [ 36 , 37 ], measure the influence the drivers’ behaviors and psychophysical factors on injury severity and distractions [ 38 ], measure the influence of the seat-belt use on the traffic accidents severity [ 39 ] and analyze the role of the journey purpose in road traffic injuries [ 40 ].…”

Section: Methodsmentioning

confidence: 99%

An Investigation into Unsafe Behaviors and Traffic Accidents Involving Unlicensed Drivers: A Perspective for Alignment Measurement

Boulagouas

Herrero

Chaib

et al. 2020

IJERPH

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Road traffic plays a vital role in countries’ economic growth and future development. However, traffic accidents are considered a major public health issue affecting humankind. Despite efforts by governments to improve traffic safety, the misalignment between the policy efforts and on-ground infringements, distractions and breaches reflect the regulatory failure. This paper uses the Bayesian network method to investigate unsafe behaviors and traffic accidents involving unlicensed drivers as a perspective for the regulatory alignment assessment. The findings suggest that: (1) unlicensed drivers are more likely to have unsafe driving behaviors; (2) the probability of being involved in a severe traffic accident increases when the drivers are unlicensed and decreases in the case of licensed drivers; (3) young drivers are noticeably more likely to engage in unsafe behaviors, usually leading to serious injuries and deaths, when their driving licenses are invalid; (4) women are more likely to engage in right-of-way violations and to have collisions with no serious injuries, contrary to unlicensed men drivers, who are involved in other types of traffic accidents resulting in serious injuries.

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Section: Methodsmentioning

confidence: 99%

An Investigation into Unsafe Behaviors and Traffic Accidents Involving Unlicensed Drivers: A Perspective for Alignment Measurement

Boulagouas

Herrero

Chaib

et al. 2020

IJERPH

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show abstract

“…And in order to improve the efficiency of emergency rescue of Hazmat transportation road accidents, a study was conducted to evaluate the time of accidents dealing based on the Bayesian network [20]. In addition, the Bayesian network model could also be used to describe the probability and risk of accidents [21][22][23][24].…”

Section: Literature Reviewmentioning

confidence: 99%

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

Xing

Lü

2018

Journal of Advanced Transportation

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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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“…In order to characterize the dependences between the di erent factors and the target variable, the probabilistic graphical models (PGMs) have been considered. Several studies have previously employed Bayesian network in their analysis of tra c accidents to express certain relationships between the di erent factors [36][37][38][39]. ese models are based on a graph in which each node represents a variable or factor and each link between variables represents a dependence between them.…”

Section: Bayesian Networkmentioning

confidence: 99%

The Role of Journey Purpose in Road Traffic Injuries: A Bayesian Network Approach

Febres

Mohamadi

Saldaña

et al. 2019

Journal of Advanced Transportation

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Introduction. Road traffic injuries are now regarded as the eighth leading cause of death globally. For example, in 2016, 102,362 traffic injuries took place in Spain in which 174,679 drivers suffered injuries. These findings necessitated the development of the current study which focuses on the prime factors that cause this type of injuries. The aim of this study, therefore, is to explore the behavioral factors that entail a higher risk of suffering either a serious or a fatal injury for drivers. Methods. The findings are based on information and data provided by “Dirección General de Tráfico” (DGT) in Spain on traffic injuries that occurred in the year 2016. Reviewing a wide range of the literature, the authors identified the most influential variables and created a model using the Bayesian networks. The variables that define the model are grouped into four factors: vehicle factor, road factor, circumstantial factor and human factor. Results. The results suggest that the principal variables that determine a higher probability of serious or fatal injuries in traffic injuries are: lack of using appropriate safety accessories, high-speed violations, distractions as well as errors. Finally, the research shows the severity probability based on reason of displacement (“in itinere,” on business, or in leisure).

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A Bayesian Network Approach to Causation Analysis of Road Accidents Using Netica

Cited by 64 publications

References 55 publications

An Investigation into Unsafe Behaviors and Traffic Accidents Involving Unlicensed Drivers: A Perspective for Alignment Measurement

An Investigation into Unsafe Behaviors and Traffic Accidents Involving Unlicensed Drivers: A Perspective for Alignment Measurement

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

The Role of Journey Purpose in Road Traffic Injuries: A Bayesian Network Approach

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