Modeling the equivalent property damage only crash rate for road segments using the hurdle regression framework

2016 ◽  
Vol 11 ◽  
pp. 48-61 ◽  
Author(s):  
Lu Ma ◽  
Xuedong Yan ◽  
Chong Wei ◽  
Jiangfeng Wang
2018 ◽  
Vol 250 ◽  
pp. 02002 ◽  
Author(s):  
Nordiana Mashros ◽  
SittiAsmah Hassan ◽  
Yaacob Haryati ◽  
Mohd Shahrir Amin Ahmad ◽  
Ismail Samat ◽  
...  

Understanding and prioritising crash contributing factors is important for improving traffic safety on the expressway. This paper aims to identify the possible contributory factors that were based on findings obtained from crash data at Senai-Desaru Expressway (SDE), which is the main connector between the western and eastern parts of Johor, Malaysia. Using reported accident data, the mishaps that had occurred along the 77.2 km road were used to identify crash patterns and their possible related segment conditions. The Average Crash Frequency and Equivalent Property Damage Only Average Crash Frequency Methods had been used to identify and rank accident-prone road segments as well as to propose for appropriate simple and inexpensive countermeasures. The results show that the dominant crash type along the road stretches of SDE had consisted of run-off-road collision and property damage only crashes. All types of accidents were more likely to occur during daytime. Out of the 154 segments, the 4 most accident-prone road segments had been determined and analysed. The results obtained from the analyses suggest that accident types are necessary for identifying the possible causes of accidents and the appropriate strategies for countermeasures. Therefore, this accident analysis could be helpful to relevant authorities in reducing the number of road accidents and the level of accident severity along the SDE.


2014 ◽  
Vol 66 ◽  
pp. 136-146 ◽  
Author(s):  
Simon Washington ◽  
Md. Mazharul Haque ◽  
Jutaek Oh ◽  
Dongmin Lee

2021 ◽  
Vol 13 (14) ◽  
pp. 7656
Author(s):  
Tien-Pen Hsu ◽  
Ku-Lin Wen ◽  
Taiyi Zhang

The mixed traffic environment often has high accident rates. Therefore, many motorcycle-related traffic improvements or control methods are employed in countries with mixed traffic, including slow-traffic lanes, motorcycle two-stage left turn areas, and motorcycle waiting zones. In Taiwan, motorcycles can ride in only the two outermost lanes, including the curb lane and a mixed traffic lane. This study analyzed the new motorcycle-riding space control policy on 27 major arterial roads containing 248 road segments in Taipei by analyzing before-and-after accident data from the years 2012–2018. In this study, the equivalent-property-damage-only (EPDO) method was used to evaluate the severity of crashes before and after the cancelation of the third lane prohibition of motorcycles (TLPM) policy. After EPDO analysis, the random forest analysis method was used to screen the crucial factors in accidents for specific road segments. Finally, a classification and regression tree (CART) was created to predict the accident improvement effects of the road segments with discontinued TLPM in different situations. Furthermore, to provide practical applications, this study integrated the CART results and the needs of traffic authorities to determine four rules for canceling TLPM. In the future, on the accident-prone road segment with TLPM, the inspection of the four rules can provide the authority to decide whether to cancel TLPM to improve the accident or not.


2020 ◽  
Vol 16 (4) ◽  
pp. 2659-2666 ◽  
Author(s):  
Jing Qiu ◽  
Lei Du ◽  
Dongwen Zhang ◽  
Shen Su ◽  
Zhihong Tian

Computing ◽  
2020 ◽  
Vol 102 (11) ◽  
pp. 2333-2360
Author(s):  
Tarique Anwar ◽  
Chengfei Liu ◽  
Hai L. Vu ◽  
Md. Saiful Islam ◽  
Dongjin Yu ◽  
...  

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Marco Molinari ◽  
Maria de Iorio ◽  
Nishi Chaturvedi ◽  
Alun Hughes ◽  
Therese Tillin

AbstractWe analyse data from the Southall And Brent REvisited (SABRE) tri-ethnic study, where measurements of metabolic and anthropometric variables have been recorded. In particular, we focus on modelling the distribution of insulin resistance which is strongly associated with the development of type 2 diabetes. We propose the use of a Bayesian nonparametric prior to model the distribution of Homeostasis Model Assessment insulin resistance, as it allows for data-driven clustering of the observations. Anthropometric variables and metabolites concentrations are included as covariates in a regression framework. This strategy highlights the presence of sub-populations in the data, characterised by different levels of risk of developing type 2 diabetes across ethnicities. Posterior inference is performed through Markov Chains Monte Carlo (MCMC) methods.


Sign in / Sign up

Export Citation Format

Share Document