Modeling Highway Traffic Safety for Developing Countries Using Delphi Technique and Bayesian Network： Case Study for Nigeria

Highway traffic safety is an issue confronting developing countries and those of industrialized nations. Nigeria, as a developing country, has been experiencing unusually high traffic related injury and fatality rates. In order to commence combating highway safety problems in Nigeria, the first task is to identify the major contributing factors; however, Nigeria has no reliable and comprehensive database of traffic accidents and casualties. Consequently, the Delphi technique was utilized in generating the required data such as number of registered automobiles, number of licensed drivers, and annual fatality count for modeling and forecasting accident rates in Nigeria. A Bayesian network model was developed and used, with the data obtained from Delphi process, to demonstrate possible traffic safety responses to different scenarios of changes in the Nigerian socio-political culture. Although the Delphi technique and the Bayesian network model only estimate the accident and safety data, those methods can be a realistic option when those data are not available, especially for the developing countries. As a result, the major accident contributors have been identified and the top three contributors-road condition, DUI （driving under the influence） and reckless driving-are policy related. The Nigerian traffic safety outlook would improve significantly if the existing laws and policies can be enforced, even at a very moderate level.

Big Data Interprets US Opioid Crisis

Since 2010,there has been a new round of drug crises in the United States.The abuse of opioids has led to a sharp increase in the number of people involved in drug crimes in the United States.There is an urgent need to explore solutions to the drug crisis in the United States.In this paper,the model of in-depth analysis is established under the condition of obtaining the opioid data and the influence factor data of the large sample of five state[1].In the first part,we use the Highway Safety Research Institute model based on the differential equation model to predict the initial value,find the initial position of the drug transfer,and obtain the curve of the number of different groups over time by fitting the data,so that the curves can be predicted the changing trends of the groups in the future.It was found that in Kentucky State,the county's most likely to start using opioids were Pike and Bale.In Ohio,the county's most likely to start using opioids are Jackson and Scioto.In Pennsylvania State,Mercer and Lackawanna are the counties most likely to start using opioids.Martinsville and Galax are the counties where Virginia State is most likely to start using opioids.Logan and Mingo are the counties where West Virginia State is most likely to start using opioids.In the second part,the gray prediction model is used to further analyze the time series of each factor,the maximum likelihood estimation method is used to obtain the weight of each factor,and the weight coefficient matrix is used to simulate the multivariate regression equation,and the factors that have the greatest influence on opioid abuse are educational background and family composition.In the third part,the hypothesis test model of two groups(the data type is proportional)is used to verify the difference between the influence factors(including the predicted values)in the first two parts of the states,thus verifying the feasibility between them.At the same time,we put forward a few suggestions to combine the current situation in the United States with the CDC data.We believe that in order to address the opium crisis,the U.S.government needs to strengthen not only oversight of doctors'prescriptions,but also make joint efforts of all sectors of society to fundamentally reduce the barriers to the use of opioids.

Pavement markings:identification of relevant covariates and controllable factors of retroreflectivity performance as a road safety measureáã

In this paper we present relevant contributions and important features related to the study of the retroreflectivity performance of pavement markings.The contribution of this paper is threefold.First,we propose an artificial scheme to allow some randomization of the treatments owing to several restrictions imposed on the choice of the experimental units.It is an experiment involving one fixed factor(three types of materials)in a randomized block design executed on a high-traffic-volume highway.Under this condition,the traffic volume works as a stress factor and the degradation of the retroreflectivity of pavement markings is faster than the degradation on rural roads or streets.This is related to the second contribution:the possibility of a reduction of experimental time.The current experiment spent 20 weeks to collect the data.And finally a mixed linear model considering three random effects and several fixed effects is fitted and the most relevant effects pointed out.This study can help highway managers to improve road safety by scheduling the maintenance of pavement marks at the appropriate time,choosing adequate material for the pavement markings and applying the proposed artificial scheme in future studies.

An investigation of influential factors of downgrade truck crashes:A logistic regression approach

Truck crash occurrence causes extensive damage to lives and property. Truck crashes on downgrades exacerbate these costs due to the likelihood of a runaway being involved.Highway agencies have continuously sought engineering measures to reduce the incidence of such crashes. However, most past studies on truck crashes have focused on level roadway sections of highways without considering the effects of downgrades. The difference in geometric characteristics of downgrades and the mechanics of truck operations on such sections mean different factors may be at play in contrast to level roadway sections.This paper investigated the factors influencing truck crashes on downgrades; an attempt to fill in some of the research gaps. An empirical analysis of factors affecting truck crashes on two-lane downgrade roadways in Wyoming was carried out using a binary logistic regression technique. After calibrating the model, the effect of each significant variable was determined using theoretical concepts established in previous studies and engineering intuition. Crash factors including driver gender and age, weather, lighting and road conditions, number of crest curves, crash type, number of driveways, day of week and posted speed limit were found to be significant. The results of the study offer new understandings into how the identified factors influence truck crashes on downgrades.