Critical Factors Analysis of Severe Traffic Accidents Based on Bayesian Network in China

Chen, Hong; Yang, Zhao; Ma, Xiaotong

doi:10.1155/2020/8878265

Cited by 14 publications

(17 citation statements)

References 42 publications

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“…Terefore, the seminaive Bayes approach is less suitable than the Bayesian networks. A Bayesian network is a probabilistic graphical model that represents a set of variables and their conditional dependencies through a directed acyclic graph (DAG) [42]. It can provide a convenient framework to represent causal relationships, making inference uncertainty more logically evident.…”

Section: I(a B) � H(b) − H(b|a)mentioning

confidence: 99%

Accident Diagnosis and Evaluation System in Parking Lots Using Multisource Data Based on Bayesian Networks

Zhang

Wang

et al. 2023

Journal of Advanced Transportation

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Parking lots have many complex structures, diverse functions, and plentiful elements. The frequent flow of vehicles with narrow and dim spaces increases the probability of various traffic accidents. Due to the low severity and lack of relevant data, there is limited understanding of safety analyses for parking lot accidents. This study integrates multisource data to establish a Bayesian diagnostic model for parking lot accidents. The mutual information method is used to screen the possible influencing factors before modeling to reduce the subjectivity of Bayesian networks. Studying the cause and effect analysis of accidents provides diagnosis and prediction for property damage and event causes. This provides valuable correlation information between factors and accident characteristics, as well as consequences under the influence of multiple factor chains. As the developed model has good accuracy, this study proposes a parking lot safety evaluation system with a library of countermeasures based on the model results to ensure rigorous conclusions. The combination with ITS technology gives the system high scalability and adaptability in multiple scenarios.

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Section: I(a B) � H(b) − H(b|a)mentioning

confidence: 99%

Accident Diagnosis and Evaluation System in Parking Lots Using Multisource Data Based on Bayesian Networks

Zhang

Wang

et al. 2023

Journal of Advanced Transportation

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“…model could reflect the relations among accident factors for the severe traffic accidents in China. In addition, three-factor combination sequences for the number of injuries and five-factor combination sequences for the number of deaths based on BN's junction tree engine were ranked according to the degree of severity to discover the critical reasons and reduce the massive damage of traffic accidents [50].…”

Section: International Journal Of Research In Science and Technologymentioning

confidence: 99%

Domain-Driven Actionable Knowledge Discovery for Traffic Accidents Using Rules Induction

Yousif¹,

Agarwal²,

Pareek³

2022

IJRST

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Due to the limitation of the methodologies of traditional data mining to satisfy business expectations, the shift from mining data-centered hidden patterns to domain-driven actionable knowledge discovery has become a significant direction of KDD research [22]. Traditional data mining algorithms and tools face major obstacles and challenges to solve real-life business problems and issues as they fail to provide actions that can be taken by people in business based on generated rules [22]. A small set of rules are generated by standard classification algorithms to form a classifier, but these classification algorithms use domain independent biases and heuristics [2]. This research aimed to propose a new approach to find actionable rules from sets of discovered rules. It focused on how a combination of traditional classification data mining and domain-driven data mining approach could be applied in solving real-life problems related to the field of traffic accidents in UAE. Real-life data were collected and pre-processed using the user’s existing knowledge and needs. Classification using Rules Induction was applied on the domain-driven dataset. The discovered rules from this technique were then summarized, combined, and analyzed. The final set of actionable rules from Classification technique for each class was then generated using a proposed interestingness method. To support such a process, the domain driven in-depth pattern discovery (DDID-PK) framework was followed [9]. Based on experimental results, the extracted domain-driven rules were more interesting and actionable than those produced by the traditional classification technique of data mining. In addition, the integration of data-centered classification technique of data mining to domain-driven approach of data mining and actionable knowledge discovery could help the Dubai police authority to reduce traffic accident severity by formulating new policies and traffic rules based on the domain-driven knowledge extracted from some hidden patterns from real data.

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“…It is widely used in the field of traffic safety for crash analysis and prevention by combining qualitative and quantitative methods [48][49][50][51][52][53]. Chen et al [54] analyzed the complex coupling relations among accident factors contributing to the single-vehicle and multivehicle traffic accidents with the Bayesian network (BN) crash severity model. Ye et al [55] analyzed the factors affecting the LOS (level of service) of non-motorized vehicles crossing the signalized intersection and aimed to construct an appropriate method to evaluate the LOS.…”

Section: Introductionmentioning

confidence: 99%

Unsafe Behaviors Analysis of Sideswipe Collision on Urban Expressways Based on Bayesian Network

Ning

Bai

2022

Sustainability

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The causes of crashes on urban expressways are mostly related to the unsafe behaviors of drivers before the crash. This study focuses on sideswipe collisions on urban expressways. Through real and visual crash data, 17 unsafe behaviors were identified for the analysis of sideswipe collisions on an urban expressway. The chains of high-risk and unsafe behaviors were then revealed to investigate the relationship between drivers’ unsafe behaviors and sideswipe collisions. A Bayesian network diagram of unsafe behaviors was used to obtain the correlation between unsafe behaviors and their influence. A topology diagram of unsafe behaviors was then constructed, and relational reasoning of typical behavioral chains was conducted. Finally, the unsafe behaviors and behavior chains that were likely to cause sideswipe collisions on the urban expressway were determined. The possibility of each behavior chain was quantified through the reasoning of variable structures constructed by the Bayesian network. The result shows that the significant influential single unsafe behavior leading to sideswipe collision on urban expressways was lane change without checking the rearview mirror or not scanning the road around and queue-jumping; moreover, based on unsafe behavior chains analysis, the most influential chains leading to sideswipe collision were: improper driving behavior in an emergency—failure to turn on signal when changing lanes—distracted and inattentive driving. Some safety precautions and countermeasures aimed at unsafe behaviors could be taken before the crash. The results of the study can be used to reduce the number of sideswipe collisions, thereby improving traffic safety on urban expressways.

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Critical Factors Analysis of Severe Traffic Accidents Based on Bayesian Network in China

Cited by 14 publications

References 42 publications

Accident Diagnosis and Evaluation System in Parking Lots Using Multisource Data Based on Bayesian Networks

Accident Diagnosis and Evaluation System in Parking Lots Using Multisource Data Based on Bayesian Networks

Domain-Driven Actionable Knowledge Discovery for Traffic Accidents Using Rules Induction

Unsafe Behaviors Analysis of Sideswipe Collision on Urban Expressways Based on Bayesian Network

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