Predicting Traffic Safety Risk Factors Using an Ensemble Classifier

2018 ◽  
pp. 201-216
Author(s):  
Nasim Arbabzadeh ◽  
Mohammad Jalayer ◽  
Mohsen Jafari
Keyword(s):  
Risk Factors ◽  
Traffic Safety ◽  
Ensemble Classifier ◽  
Safety Risk

scholarly journals Detecting Change between Urban Road Environments along a Route Based on Static Road Object Occurrences

2021 ◽  
Vol 11 (8) ◽  
pp. 3666
Author(s):  
Zoltán Fazekas ◽  
László Gerencsér ◽  
Péter Gáspár
Keyword(s):  
Change Detection ◽  
Traffic Safety ◽  
Recognition System ◽  
Driver Assistance Systems ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Safety Risk ◽  
Urban Road ◽  
Environment Type ◽  
Cusum Method

For over a decade, urban road environment detection has been a target of intensive research. The topic is relevant for the design and implementation of advanced driver assistance systems. Typically, embedded systems are deployed in these for the operation. The environments can be categorized into road environment-types. Abrupt transitions between these pose a traffic safety risk. Road environment-type transitions along a route manifest themselves also in changes in the distribution of traffic signs and other road objects. Can the placement and the detection of traffic signs be modelled jointly with an easy-to-handle stochastic point process, e.g., an inhomogeneous marked Poisson process? Does this model lend itself for real-time application, e.g., via analysis of a log generated by a traffic sign detection and recognition system? How can the chosen change detector help in mitigating the traffic safety risk? A change detection method frequently used for Poisson processes is the cumulative sum (CUSUM) method. Herein, this method is tailored to the specific stochastic model and tested on realistic logs. The use of several change detectors is also considered. Results indicate that a traffic sign-based road environment-type change detection is feasible, though it is not suitable for an immediate intervention.

An improved text mining approach to extract safety risk factors from construction accident reports

2021 ◽  
Vol 138 ◽  
pp. 105216
Author(s):  
Na XU ◽  
Ling MA ◽  
Qing Liu ◽  
Li WANG ◽  
Yongliang Deng
Keyword(s):  
Risk Factors ◽  
Text Mining ◽  
Safety Risk

scholarly journals Analysis on Relationships of Safety Risk Factors in Metro Construction

2016 ◽  
Vol 9 (5) ◽  
pp. 150-157 ◽  
Author(s):  
Xu Na ◽  
◽  
Wang Jianping ◽  
Li Jie ◽  
Ni Guodong ◽  
...  
Keyword(s):  
Risk Factors ◽  
Safety Risk

scholarly journals MEVA - a new method of occupational health and safety risk assessment

2019 ◽  
Vol 290 ◽  
pp. 12008
Author(s):  
Doru-Costin Darabont ◽  
Eduard Smîdu ◽  
Alina Trifu ◽  
Vicențiu Ciocîrlea ◽  
Iulian Ivan ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Occupational Health ◽  
Deductive Reasoning ◽  
Health And Safety ◽  
Occupational Health And Safety ◽  
New Method ◽  
Risk Level ◽  
Simple Method ◽  
Safety Risk

The paper describes a new method of occupational health and safety risk assessment. This method, called MEVA, unlike the old ones, focuses more on reduce or eliminate subjective issues in determining the probability of manifestation of risk factors and is based on a deductive reasoning, with the help of which is studied the chain between two or more events. The novelty of the method consists in combining risk assessment techniques with evaluation of compliance with legal and other requirements, aiming to provide a more objective results of the risk assessment. In the MEVA method, the risk matrix is defined by 5 classes of severity and 5 probability classes, resulting in 5 levels of risk. After quantifying the risk factors, prevention measures are proposed for all the identified risk factors and each partial risk level is recalculated as a result of the proposed measures. The five levels of risk were grouped into three categories: acceptable, tolerable and unacceptable. The MEVA method is a simple method and it can be used for assessing various workplaces, with different characteristics of complexity, activity domain or occupational health and safety recordings.

scholarly journals Toward a Stakeholder Perspective on Safety Risk Factors of Metro Construction: A Social Network Analysis

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Ying Lu ◽  
Yu Zhang
Keyword(s):  
Risk Factors ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Government Regulation ◽  
Rapid Development ◽  
Construction Site ◽  
Safety Risk ◽  
Stakeholder Perspective ◽  
Bad Weather

The rapid development of the metro has greatly relieved the traffic pressure on the urban ground system, but the frequency of metro construction accidents is also increasing year by year. Due to the complex construction process of the metro, once an accident occurs, casualties and property damage are extremely serious. The safety risk factors triggered by different stakeholders were the primary cause of accidents during the metro construction phase. This paper builts a social analysis network of safety risk factors in metro construction from a stakeholder’s perspective. Based on 42 accident cases and related literature, 6 stakeholders and 25 safety risk factors were identified and the relationships between stakeholders and safety risk factors were also determined. Through the application of social network analysis, a social network of safety risk factors in metro construction was constructed, and quantitative analysis was carried out based on density, degree centrality, betweenness centrality, and cohesive subgroup. The results showed that the key safety risk factors in the construction phase of the metro were in action of the contractor’s construction site managers, lack of safety protection at the construction site, insufficient detailed survey and design information provided by the designer, unfavorable government regulation, and bad weather. Moreover, the results of 20 cohesive subgroups illustrated the interrelationship between safety risk factors. S1H2 (“violations by operatives” related to contractor) and S1H4 (“lack of safety precautions” related to contractor) and S5H5 (“ineffective supervision” related to supervisor) both belonged to subgroup G1, which means that there is a high probability that these three safety risk factors would occur simultaneously. This paper provided a basis to improve the level of safety risk management and control from the stakeholder’s perspective.

scholarly journals MODELLING THE ASSESSMENT OF TRAFFIC RISK AT LEVEL CROSSINGS OF LITHUANIAN RAILWAYS

Transport ◽  
2016 ◽  
Vol 32 (3) ◽  
pp. 282-290 ◽  
Author(s):  
Gintautas Bureika ◽  
Eduardas Gaidamauskas ◽  
Jonas Kupinas ◽  
Marijonas Bogdevičius ◽  
Stasys Steišūnas
Keyword(s):  
Traffic Safety ◽  
Risk Evaluation ◽  
Safety Management ◽  
Road Traffic ◽  
Scientific Publications ◽  
Level Crossings ◽  
Safety Risk ◽  
Railway Traffic ◽  
Safety Risks ◽  
Railway Infrastructure

The article discusses the problem of railway safety management and the criteria for evaluating the traffic risk described in various scientific publications. The research aims to assess the risk posed to railway infrastructure objects and to create a model for safety risk evaluation. The research on the traffic safety risk with respect to the infrastructure of the rail transport system was divided into three stages. The Plan–Do–Check–Act (PDCA) model can be used for investigating the problem of managing railway traffic safety risks in the Lithuanian railways. The main threats to railway infrastructure objects are defined, and the catalogue of technical parameters of level crossings of the Lithuanian railways is presented. The maximum allowable train speed, the intensity of railway and road traffic, as well as the size (width) and visibility of level crossings from the train driver’s cab are chosen as the main criteria for evaluating the vulnerability of a level crossing. The logistic regression method has been applied to rank and assess the safety of level crossings of the Lithuanian railways. The validity of the model developed in the present work for assessing traffic safety risks at level crossings of Lithuanian railways is about 86%. The suggested risk evaluation model is flexible and can be easily adapted for the evaluation and monitoring of safety risk of other elements pertaining to railway infrastructure. Finally, the basic conclusions and recommendations are provided.

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