scholarly journals Urban Road Accident Black Spot Identification and Classification Approach: A Novel Grey Verhuls–Empirical Bayesian Combination Method

2021 ◽  
Vol 13 (20) ◽  
pp. 11198
Author(s):  
Yan Wan ◽  
Wenqiang He ◽  
Jibiao Zhou
Keyword(s):  
Bayesian Method ◽  
Black Spot ◽  
Road Accident ◽  
Combination Method ◽  
Risk Level ◽  
Empirical Bayesian ◽  
Safety Research ◽  
Black Spots ◽  
Empirical Bayesian Method

The identification and classification of accident black spots on urban roads is a key element of road safety research. To solve the problems caused by the randomness of accident occurrences and the unclear classification of accident black spots by the traditional model, we propose a method that can quickly identify and classify accident black spots on urban roads: a combined grey Verhuls–Empirical Bayesian method. The grey Verhuls model is used to obtain the predicted/expected numbers of accidents at accident hazard locations, and the empirical Bayesian approach is used to derive two accident black spot discriminators, a safety improvement space and a safety index (SI), and to classify the black spots into two, three, four and five levels according to the range of the SI. Finally, we validate this combined method on examples. High-quality and high-accuracy data are obtained from the accident collection records of the Ningbo Jiangbei District from March to December 2020, accounting for 90.55% of the actual police incidents during this period. The results show that the combined grey Verhuls–Empirical Bayesian method can identify accident black spots quickly and accurately due to the consideration of accident information from the same types of accident locations. The accident black point classification results show that the five-level rating of accident black points is most reasonable. Our study provides a new idea for accident black spot identification and a feasible method for accident black spot risk level classification.

scholarly journals The Bühlmann–Straub Estimation of Claim Means in Random B-F Reserve Model

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Zhang Yi ◽  
Wen Limin ◽  
Li Zhilong
Keyword(s):  
Prior Distribution ◽  
Personal Experience ◽  
Bayesian Method ◽  
Historical Data ◽  
Credibility Theory ◽  
Empirical Bayesian ◽  
Sample Distribution ◽  
Empirical Bayesian Method ◽  
Chain Ladder ◽  
Bayesian Estimators

In the B-F reserve model, it is a very critical step to estimate the claim means of the accident year. However, the traditional method uses the prior estimators of the claim means based on the personal experience of actuaries or historical data. This method inevitably carries the subjectivity of the actuary himself. In this paper, a stochastic B-F model is established, and a prior distribution is constructed for the claim means in the accident year. The idea of the credibility theory is used to derive the linear Bayesian estimators of claim means. Finally, the empirical Bayesian method is used to estimate the first two moments of the prior distribution, and the empirical Bayesian estimators of the claim means and the corresponding reserves are derived. The estimators obtained in this paper do not depend on the specific forms of the sample distribution and the prior distribution and can be used directly in practice. In the numerical simulation, our estimates are compared with the traditional B-F estimates and the chain ladder estimates. It is verified that the estimates given in this paper have small mean square error.

scholarly journals Bayesian Analysis of Inverted Kumaraswamy Mixture Model with Application to Burning Velocity of Chemicals

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Farzana Noor ◽  
Saadia Masood ◽  
Mehwish Zaman ◽  
Maryam Siddiqa ◽  
Raja Asif Wagan ◽  
...  
Keyword(s):  
Bayesian Analysis ◽  
Mixture Model ◽  
Bayesian Method ◽  
Burning Velocity ◽  
Likelihood Estimation ◽  
Empirical Bayesian ◽  
Squared Error ◽  
Linex Loss ◽  
Empirical Bayesian Method ◽  
Better Than

Burning velocity of different chemicals is estimated using a model from mixed population considering inverted Kumaraswamy (IKum) distribution for component parts. Two estimation techniques maximum likelihood estimation (MLE) and Bayesian analysis are applied for estimation purposes. BEs of a mixture model are obtained using gamma, inverse beta prior, and uniform prior distribution with two loss functions. Hyperparameters are determined through the empirical Bayesian method. An extensive simulation study is also a part of the study which is used to foresee the characteristics of the presented model. Application of the IKum mixture model is presented through a real dataset. We observed from the results that Linex loss performed better than squared error loss as it resulted in lower risks. And similarly gamma prior is preferred over other priors.

scholarly journals An approximate empirical Bayesian method for large-scale linear-Gaussian inverse problems

2018 ◽  
Vol 34 (9) ◽  
pp. 095001 ◽  
Author(s):  
Qingping Zhou ◽  
Wenqing Liu ◽  
Jinglai Li ◽  
Youssef M Marzouk
Keyword(s):  
Inverse Problems ◽  
Large Scale ◽  
Bayesian Method ◽  
Empirical Bayesian ◽  
Empirical Bayesian Method

scholarly journals Prioritization of Black Spots in Hilly Road Segment using Road Engineering Data

2020 ◽  
Vol 9 (3) ◽  
pp. 4418-4422
Keyword(s):  
Black Spot ◽  
Road Transport ◽  
Road Accident ◽  
Industrialized Countries ◽  
Road Accidents ◽  
Road Engineering ◽  
The Road ◽  
Black Spots ◽  
Accident Data ◽  
On The Road

Road accidents are one of the causes of disability, injury and death. As per the latest road accident data released by the Ministry of Road Transport & Highways (MoRTH), the total number of accidents increased by 2.5 percent from 4,89,400 in 2014 to 5,01,423 in 2015. The analysis reveals that about 1,374 accidents and 400 deaths take place every day. Every single year, it has been estimated that over three lakh persons die and 10-15 million persons are injured in road accidents throughout the world. According to the analyses, statistics of global accident indicate that in developing countries, the rate of fatality per licensed vehicle is very high as compared to that of industrialized countries. A road stretch of about 500 metres in length in which either ten fatalities or five road accidents (involving grievous injuries/fatalities) took place during last three calendar years, on National Highways is considered as a road accident black spot according to MoRTH, Government of India. In the present study the identified black spots of Haridwar and Dehradun city were included comprising of a total of 81 black spots out of which there were 49 black spots which were identified in Dehradun followed by 32 black spots in Haridwar. The present study was an attempt to carry out the prioritization of these identified blackspots with respect to the factors that were considered to evaluate accident prone locations on the road. The identified black spots were then prioritized using the classification scheme (ranking from low to high).The study reveals that the advantage of using this approach for prioritizing accident black spots on roads is that it requires very less additional data other than the road network maps.

scholarly journals Identification of Road Accident by using Black Spot Method Between Panthaghati to Dhalli Road

2020 ◽  
Vol 9 (2) ◽  
pp. 723-728
Keyword(s):  
Road Safety ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Black Spot ◽  
Research Paper ◽  
Road Accident ◽  
Road Traffic Accidents ◽  
Road Accidents ◽  
The Road ◽  
Black Spots

In India road accidents are very serious problem because of large population and high traffic density of vehicles. Most of the road accidents occur mainly due to the negligence of driver and poor infrastructure only a few accidents occur due to the technical error of vehicles. The main purpose of this research paper is prevention of road traffic accidents and improvement of road safety in Shimla. Road safety is very important aspect of today’s life, so it is important that everybody should aware about road safety. To do this study a section of 12km length is chosen between Panthaghati to Dhalli in district Shimla on NH 5 where accidents black spots are identified for the section by analyzing secondary data used to prevent road accidents. In this study primary data is used for observing the road conditions and secondary data is used to find accidents black spot. Black Spot is a point or a place on the road where road accident occurs repeatedly one after another which is known as accident black spot. To identify these black spots we use weighted severity index (WSI) method. It is one the most reliable and effective method for determining the most proven accidents black spots. Shimla is a hilly area and it has narrow roads, blind curve and black spots which increase the chances of road traffic accidents. In past recent years road traffic accidents are increasing in Shimla and this study deals with identification of major issues causing road traffic accidents. This research paper helps to improve the road safety in Shimla because in this study the analysis has been done to identify the major problems responsible for gradually increasing road accidents. This research paper is also used in future research paper as reference purpose and it will also provide an overview to other researchers who want do their research on similar kind of topics.

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