scholarly journals A Novel Framework for Road Traffic Risk Assessment with HMM-Based Prediction Model

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4313 ◽  
Author(s):  
Xunjia Zheng ◽  
Di Zhang ◽  
Hongbo Gao ◽  
Zhiguo Zhao ◽  
Heye Huang ◽  
...  

Over the past decades, there has been significant research effort dedicated to the development of intelligent vehicles and V2X systems. This paper proposes a road traffic risk assessment method for road traffic accident prevention of intelligent vehicles. This method is based on HMM (Hidden Markov Model) and is applied to the prediction of steering angle status to (1) evaluate the probabilities of the steering angle in each independent interval and (2) calculate the road traffic risk in different analysis regions. According to the model, the road traffic risk is quantified and presented directly in a visual form by the time-varying risk map, to ensure the accuracy of assessment and prediction. Experiment results are presented, and the results show the effectiveness of the assessment strategies.

2020 ◽  
Vol 34 (5) ◽  
pp. 627-640 ◽  
Author(s):  
Shi Xianwu ◽  
Qiu Jufei ◽  
Chen Bingrui ◽  
Zhang Xiaojie ◽  
Guo Haoshuang ◽  
...  

Author(s):  
Zuzhen Ji ◽  
Dirk Pons ◽  
John Pearse

Successful implementation of Health and Safety (H&S) systems requires an effective mechanism to assess risk. Existing methods focus primarily on measuring the safety aspect; the risk of an accident is determined based on the product of severity of consequence and likelihood of the incident arising. The health component, i.e., chronic harm, is more difficult to assess. Partially, this is due to both consequences and the likelihood of health issues, which may be indeterminate. There is a need to develop a quantitative risk measurement for H&S risk management and with better representation for chronic health issues. The present paper has approached this from a different direction, by adopting a public health perspective of quality of life. We have then changed the risk assessment process to accommodate this. This was then applied to a case study. The case study showed that merely including the chronic harm scales appeared to be sufficient to elicit a more detailed consideration of hazards for chronic harm. This suggests that people are not insensitive to chronic harm hazards, but benefit from having a framework in which to communicate them. A method has been devised to harmonize safety and harm risk assessments. The result was a comprehensive risk assessment method with consideration of safety accidents and chronic health issues. This has the potential to benefit industry by making chronic harm more visible and hence more preventable.


2021 ◽  
Vol 420 ◽  
pp. 129893
Author(s):  
Zijian Liu ◽  
Wende Tian ◽  
Zhe Cui ◽  
Honglong Wei ◽  
Chuankun Li

2021 ◽  
Vol 102 ◽  
pp. 102134
Author(s):  
Junjiang He ◽  
Tao Li ◽  
Beibei Li ◽  
Xiaolong Lan ◽  
Zhiyong Li ◽  
...  

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hamid Reza Marateb ◽  
Maja von Cube ◽  
Ramin Sami ◽  
Shaghayegh Haghjooy Javanmard ◽  
Marjan Mansourian ◽  
...  

Abstract Background Already at hospital admission, clinicians require simple tools to identify hospitalized COVID-19 patients at high risk of mortality. Such tools can significantly improve resource allocation and patient management within hospitals. From the statistical point of view, extended time-to-event models are required to account for competing risks (discharge from hospital) and censoring so that active cases can also contribute to the analysis. Methods We used the hospital-based open Khorshid COVID Cohort (KCC) study with 630 COVID-19 patients from Isfahan, Iran. Competing risk methods are used to develop a death risk chart based on the following variables, which can simply be measured at hospital admission: sex, age, hypertension, oxygen saturation, and Charlson Comorbidity Index. The area under the receiver operator curve was used to assess accuracy concerning discrimination between patients discharged alive and dead. Results Cause-specific hazard regression models show that these baseline variables are associated with both death, and discharge hazards. The risk chart reflects the combined results of the two cause-specific hazard regression models. The proposed risk assessment method had a very good accuracy (AUC = 0.872 [CI 95%: 0.835–0.910]). Conclusions This study aims to improve and validate a personalized mortality risk calculator based on hospitalized COVID-19 patients. The risk assessment of patient mortality provides physicians with additional guidance for making tough decisions.


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