22,660 research outputs found
Analysis of Traffic Collisions in Dublin Using GIS Based System
This study aims to analyse traffic collisions in the Greater Dublin Region between the period 2006-2012, using GIS to identify hotspots and examine the relationship between collisions and a range of contributory factors including vehicular speeds, traffic volume, road curvature, road category and distance from intersection that could enable prediction of traffic collisions. To this end, Road Safety Authority (RSA) collision data for Dublin Region geocoded as point events, road profiles, traffic flow characteristics on which these occur are spatially merged using ArcGIS and FME software to establish if a significant relationship exists between collision frequencies on road links and the specific link characteristics and traffic flow characteristics. The road network has been divided into uniform segments and the collision frequencies on each of these noted. Traffic collisions are rare and random events and often a major proportion of segments would have no instance of collisions, thus following a Negative Binomial distribution. The outputs from GIS exercise are tested through SPSS software using Negative Binomial distribution for modelling the relationship between different variables. This paper comes at a significant time where efforts are being made to improve the safety of roads within the European Union [1]. Every year, road collisions cause human fatalities together with huge financial loss which can be significantly reduced by improving road safety through the enforcement of traffic laws and road user compliance. By identifying the cause effect relationship and the spatial locations most prone to collisions, prioritized regulatory and safety interventions can be put in place to reduce the collisions on the roads
Analysis of Traffic Collisions in Dublin Using GIS Based System
This study aims to analyse traffic collisions in the Greater Dublin Region between the period 2006-2012, using GIS to identify hotspots and examine the relationship between collisions and a range of contributory factors including vehicular speeds, traffic volume, road curvature, road category and distance from intersection that could enable prediction of traffic collisions. To this end, Road Safety Authority (RSA) collision data for Dublin Region geocoded as point events, road profiles, traffic flow characteristics on which these occur are spatially merged using ArcGIS and FME software to establish if a significant relationship exists between collision frequencies on road links and the specific link characteristics and traffic flow characteristics. The road network has been divided into uniform segments and the collision frequencies on each of these noted. Traffic collisions are rare and random events and often a major proportion of segments would have no instance of collisions, thus following a Negative Binomial distribution. The outputs from GIS exercise are tested through SPSS software using Negative Binomial distribution for modelling the relationship between different variables. This paper comes at a significant time where efforts are being made to improve the safety of roads within the European Union [1]. Every year, road collisions cause human fatalities together with huge financial loss which can be significantly reduced by improving road safety through the enforcement of traffic laws and road user compliance. By identifying the cause effect relationship and the spatial locations most prone to collisions, prioritized regulatory and safety interventions can be put in place to reduce the collisions on the roads
Estimation of safety performance functions for urban intersections using various functional forms of the negative binomial regression model and a generalized Poisson regression model
Intersections are established dangerous entities of a highway system due to the challenging and unsafe roadway environment they are characterized for drivers and other road users. In efforts to improve safety, an enormous interest has been shown in developing statistical models for intersection crash prediction and explanation. The selection of an adequate form of the statistical model is of great importance for the accurate estimation of crash frequency and the correct identification of crash contributing factors. Using a six-year crash data, road infrastructure and geometric design data, and traffic flow data of urban intersections, we applied three different functional forms of negative binomial models (i.e., NB-1, NB-2, NB-P) and a generalized Poisson (GP) model to develop safety performance functions (SPF) by crash severity for signalized and unsignalized intersections. This paper presents the relationships found between the explanatory variables and the expected crash frequency. It reports the comparison of different models for total, injury & fatal, and property damage only crashes in order to obtain ones with the maximum estimation accuracy. The comparison of models was based on the goodness of fit and the prediction performance measures.
The fitted models showed that the traffic flow and several variables related to road infrastructure and geometric design significantly influence the intersection crash frequency. Further, the goodness of fit and the prediction performance measures revealed that the NB-P model outperformed other models in most crash severity levels for signalized intersections. For the unsignalized intersections, the GP model was the best performing model. When only the NB models were compared, the functional form NB-P performed better than the traditional NB-1 and, more specifically, the NB-2 models. In conclusion, our findings suggest a potential improvement in the estimation accuracy of the SPFs for urban intersections by applying the NB-P and GP models
Methodology for development of drought Severity-Duration-Frequency (SDF) Curves
Drought monitoring and early warning are essential elements impacting drought
sensitive sectors such as primary production, industrial and consumptive water users. A
quantitative estimate of the probability of occurrence and the anticipated severity of drought
is crucial for the development of mitigating strategies. The overall aim of this study is to
develop a methodology to assess drought frequency and severity and to advance the
understanding of monitoring and predicting droughts in the future. Seventy (70)
meteorological stations across Victoria, Australia were selected for analysis. To achieve the
above objective, the analysis was initially carried out to select the most applicable
meteorological drought index for Victoria. This is important because to date, no drought
indices are applied across Australia by any Commonwealth agency quantifying drought
impacts. An evaluation of existing meteorological drought indices namely, the Standardised
Precipitation Index (SPI), the Reconnaissance Drought Index (RDI) and Deciles was first
conducted to assess their suitability for the determination of drought conditions. The use of
the Standardised Precipitation Index (SPI) was shown to be satisfactory for assessing and
monitoring meteorological droughts in Australia. When applied to data, SPI was also
successful in detecting the onset and the end of historical droughts.
Temporal changes in historic rainfall variability and the trend of SPI were investigated
using non-parametric trend techniques to detect wet and dry periods across Victoria,
Australia. The first part of the analysis was carried out to determine annual rainfall trends
using Mann Kendall (MK) and Sen’s slope tests at five selected meteorological stations with
long historical records (more than 100 years), as well as a short sub-set period (1949-2011) of
the same data set. It was found that different trend results were obtained for the sub-set. For
SPI trend analysis, it was observed that, although different results were obtained showing
significant trends, SPI gave a trend direction similar to annual precipitation (downward and
upward trends). In addition, temporal trends in the rate of occurrence of drought events (i.e.
inter-arrival times) were examined. The fact that most of the stations showed negative slopes
indicated that the intervals between events were becoming shorter and the frequency of
events was temporally increasing. Based on the results obtained from the preliminary
analysis, the trend analyses were then carried out for the remaining 65 stations. The main
conclusions from these analyses are summarized as follows; 1) the trend analysis was
observed to be highly dependent on the start and end dates of analysis. It is recommended
that in the selection of time period for the drought, trend analysis should consider the length
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of available data sets. Longer data series would give more meaningful results, thus improving
the understanding of droughts impacted by climate change. 2) From the SPI and inter-arrival
drought trends, it was observed that some of the study areas in Victoria will face more
frequent dry period leading to increased drought occurrence. Information similar to this
would be very important to develop suitable strategies to mitigate the impacts of future
droughts.
The main objective of this study was the development of a methodology to assess
drought risk for each region based on a frequency analysis of the drought severity series
using the SPI index calculated over a 12-month duration. A novel concept centric on drought
severity-duration-frequency (SDF) curves was successfully derived for all the 70 stations
using an innovative threshold approach. The methodology derived using extreme value
analysis will assist in the characterization of droughts and provide useful information to
policy makers and agencies developing drought response plans. Using regionalisation
techniques such as Cluster analysis and modified Andrews curve, the study area was
separated into homogenous groups based on rainfall characteristics. In the current Victorian
application the study area was separated into six homogeneous clusters with unique
signatures. A set of mean SDF curves was developed for each cluster to identify the
frequency and severity of the risk of drought events for various return periods in each cluster.
The advantage of developing a mean SDF curve (as a signature) for each cluster is that it
assists the understanding of drought conditions for an ungauged or unknown station, the
characteristics of which fit existing cluster groups. Non-homogeneous Markov Chain
modelling was used to estimate the probability of different drought severity classes and
drought severity class predictions 1, 2 and 3 months ahead. The non-homogeneous
formulation, which considers the seasonality of precipitation, is useful for understanding the
evolution of drought events and for short-term planning. Overall, this model predicted
drought situations 1 month ahead well. However, predictions 2 and 3 months ahead should be
used with caution.
Many parts of Australia including Victoria have experienced their worst droughts on
record over the last decade. With the threat of climate change potentially further exacerbating
droughts in the years ahead, a clear understanding of the impact of droughts is vital. The
information on the probability of occurrence and the anticipated severity of drought will be
helpful for water resources managers, infrastructure planners and government policy-makers
with future infrastructure planning and with the design and building of more resilient
communities
Crash risk estimation and assessment tool
Currently in Australia, there are no decision support tools for traffic and transport engineers to assess the crash risk potential of proposed road projects at design level. A selection of equivalent tools already exists for traffic performance assessment, e.g. aaSIDRA or VISSIM. The Urban Crash Risk Assessment Tool (UCRAT) was developed for VicRoads by ARRB Group to promote methodical identification of future crash risks arising from proposed road infrastructure, where safety cannot be evaluated based on past crash history. The tool will assist practitioners with key design decisions to arrive at the safest and the most cost -optimal design options. This paper details the development and application of UCRAT software. This professional tool may be used to calculate an expected mean number of casualty crashes for an intersection, a road link or defined road network consisting of a number of such elements. The mean number of crashes provides a measure of risk associated with the proposed functional design and allows evaluation of alternative options. The tool is based on historical data for existing road infrastructure in metropolitan Melbourne and takes into account the influence of key design features, traffic volumes, road function and the speed environment. Crash prediction modelling and risk assessment approaches were combined to develop its unique algorithms. The tool has application in such projects as road access proposals associated with land use developments, public transport integration projects and new road corridor upgrade proposals
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