Abstract: Iraq is a middle income country where the road safety issue is considered one of the leading causes of deaths. To control the road risk issue, the Iraqi Ministry of Planning, General Statistical Organization started to organise a collection system of traffic accidents data with details related to their causes and severity. These data are published as an annual report. In this paper, a review of the available crash data in Iraq will be presented. The available data represent the rate of accidents in aggregated level and classified according to their types, road users’ details, and crash severity, type of vehicles, causes and number of causalities. The review is according to the types of models used in road safety studies and research, and according to the required road safety data in the road constructions tasks. The available data are also compared with the road safety dataset published in the United Kingdom as an example of developed country. It is concluded that the data in Iraq are suitable for descriptive and exploratory models, aggregated level comparison analysis, and evaluation and monitoring the progress of the overall traffic safety performance. However, important traffic safety studies require disaggregated level of data and details related to the factors of the likelihood of traffic crashes. Some studies require spatial geographic details such as the location of the accidents which is essential in ranking the roads according to their level of safety, and name the most dangerous roads in Iraq which requires tactic plan to control this issue. Global Road safety agencies interested in solve this problem in low and middle-income countries have designed road safety assessment methodologies which are basing on the road attributes data only. Therefore, in this research it is recommended to use one of these methodologies.
Abstract: Truck-involved crashes have higher crash severity than non-truck-involved crashes. There have been many studies about the frequency of crashes and the development of severity models, but those studies only analyzed the relationship between observed variables. To identify why more people are injured or killed when trucks are involved in the crash, we must examine to quantify the complex causal relationship between severity of the crash and risk factors by adopting the latent factors of crashes. The aim of this study was to develop a structural equation or model based on truck-involved and non-truck-involved crashes, including five latent variables, i.e. a crash factor, environmental factor, road factor, driver’s factor, and severity factor. To clarify the unique characteristics of truck-involved crashes compared to non-truck-involved crashes, a confirmatory analysis method was used. To develop the model, we extracted crash data from 10,083 crashes on Korean freeways from 2008 through 2014. The results showed that the most significant variable affecting the severity of a crash is the crash factor, which can be expressed by the location, cause, and type of the crash. For non-truck-involved crashes, the crash and environment factors increase severity of the crash; conversely, the road and driver factors tend to reduce severity of the crash. For truck-involved crashes, the driver factor has a significant effect on severity of the crash although its effect is slightly less than the crash factor. The multiple group analysis employed to analyze the differences between the heterogeneous groups of drivers.
Abstract: Safety is one of the most important considerations
when buying a new car. While active safety aims at avoiding
accidents, passive safety systems such as airbags and seat belts
protect the occupant in case of an accident. In addition to legal
regulations, organizations like Euro NCAP provide consumers with
an independent assessment of the safety performance of cars and
drive the development of safety systems in automobile industry.
Those ratings are mainly based on injury assessment reference values
derived from physical parameters measured in dummies during a car
crash test.
The components and sub-systems of a safety system are designed
to achieve the required restraint performance. Sled tests and other
types of tests are then carried out by car makers and their suppliers
to confirm the protection level of the safety system. A Knowledge
Discovery in Databases (KDD) process is proposed in order to
minimize the number of tests. The KDD process is based on the
data emerging from sled tests according to Euro NCAP specifications.
About 30 parameters of the passive safety systems from different data
sources (crash data, dummy protocol) are first analysed together with
experts opinions. A procedure is proposed to manage missing data
and validated on real data sets. Finally, a procedure is developed to
estimate a set of rough initial parameters of the passive system before
testing aiming at reducing the number of tests.
Abstract: Road crashes not only claim lives and inflict injuries but also create economic burden to the society due to loss of productivity. The problem of deaths and injuries as a result of road traffic crashes is now acknowledged to be a global phenomenon with authorities in virtually all countries of the world concerned about the growth in the number of people killed and seriously injured on their roads. However, the road crash scenario of a developing country like Bangladesh is much worse comparing with this of developed countries. For developing proper countermeasures it is necessary to identify the factors affecting crash occurrences. The objectives of the study is to examine the effect of district wise road infrastructure, socioeconomic and demographic features on crash occurrence .The unit of analysis will be taken as individual district which has not been explored much in the past. Reported crash data obtained from Bangladesh Road Transport Authority (BRTA) from the year 2004 to 2010 are utilized to develop negative binomial model. The model result will reveal the effect of road length (both paved and unpaved), road infrastructure and several socio economic characteristics on district level crash frequency in Bangladesh.
Abstract: Saudi Arabia in recent years has seen drastic increase
in traffic related crashes. With population of over 29 million, Saudi
Arabia is considered as a fast growing and emerging economy. The
rapid population increase and economic growth has resulted in rapid
expansion of transportation infrastructure, which has led to increase
in road crashes. Saudi Ministry of Interior reported more than 7,000
people killed and 68,000 injured in 2011 ranking Saudi Arabia to be
one of the worst worldwide in traffic safety. The traffic safety issues
in the country also result in distress to road users and cause and
economic loss exceeding 3.7 billion Euros annually. Keeping this in
view, the researchers in Saudi Arabia are investigating ways to
improve traffic safety conditions in the country. This paper presents a
multilevel approach to collect traffic safety related data required to do
traffic safety studies in the region. Two highway corridors including
King Fahd Highway 39 kilometre and Gulf Cooperation Council
Highway 42 kilometre long connecting the cities of Dammam and
Khobar were selected as a study area. Traffic data collected included
traffic counts, crash data, travel time data, and speed data. The
collected data was analysed using geographic information system to
evaluate any correlation. Further research is needed to investigate the
effectiveness of traffic safety related data when collected in a
concerted effort.