3,429 research outputs found
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
Automatic Estimation of the Exposure to Lateral Collision in Signalized Intersections using Video Sensors
Intersections constitute one of the most dangerous elements in road systems.
Traffic signals remain the most common way to control traffic at high-volume
intersections and offer many opportunities to apply intelligent transportation
systems to make traffic more efficient and safe. This paper describes an
automated method to estimate the temporal exposure of road users crossing the
conflict zone to lateral collision with road users originating from a different
approach. This component is part of a larger system relying on video sensors to
provide queue lengths and spatial occupancy that are used for real time traffic
control and monitoring. The method is evaluated on data collected during a real
world experiment
Intersection SPaT Estimation by means of Single-Source Connected Vehicle Data
The file attached to this record is the author's final peer reviewed version.Current traffic management systems in urban networks require real-time estimation of the traffic states. With the development of in-vehicle and communication technologies, connected vehicle data has emerged as a new data source for traffic measurement and estimation. In this work, a machine learning-based methodology for signal phase and timing information (SPaT) which is highly valuable for many applications such as green light optimal advisory systems and real-time vehicle navigation is proposed. The proposed methodology utilizes data from connected vehicles travelling within urban signalized links to estimate the queue tail location, vehicle accumulation, and subsequently, link outflow. Based on the produced high-resolution outflow estimates and data from crossing connected vehicles, SPaT information is estimated via correlation analysis and a machine learning approach. The main contribution is that the single-source proposed approach relies merely on connected vehicle data and requires neither prior information such as intersection cycle time nor data from other sources such as conventional traffic measuring tools. A sample four-leg intersection where each link comprises different number of lanes and experiences different traffic condition is considered as a testbed. The validation of the developed approach has been undertaken by comparing the produced estimates with realistic micro-simulation results as ground truth, and the achieved simulation results are promising even at low penetration rates of connected vehicles
17-11 Evaluation of Transit Priority Treatments in Tennessee
Many big cities are progressively implementing transit friendly corridors especially in urban areas where traffic may be increasing at an alarming rate. Over the years, Transit Signal Priority (TSP) has proven to be very effective in creating transit friendly corridors with its ability to improve transit vehicle travel time, serviceability and reliability. TSP as part of Transit Oriented Development (TOD) is associated with great benefits to community liveability including less environmental impacts, reduced traffic congestions, fewer vehicular accidents and shorter travel times among others.This research have therefore analysed the impact of TSP on bus travel times, late bus recovery at bus stop level, delay (on mainline and side street) and Level of Service (LOS) at intersection level on selected corridors and intersections in Nashville Tennessee; to solve the problem of transit vehicle delay as a result of high traffic congestion in Nashville metropolitan areas. This study also developed a flow-delay model to predict delay per vehicle for a lane group under interrupted flow conditions and compared some measure of effectiveness (MOE) before and after TSP. Unconditional green extension and red truncation active priority strategies were developed via Vehicle Actuated Programming (VAP) language which was tied to VISSIM signal controller to execute priority for transit vehicles approaching the traffic signal at 75m away from the stop line. The findings from this study indicated that TSP will recover bus lateness at bus stops 25.21% to 43.1% on the average, improve bus travel time by 5.1% to 10%, increase side street delay by 15.9%, and favour other vehicles using the priority approach by 5.8% and 11.6% in travel time and delay reduction respectively. Findings also indicated that TSP may not affect LOS under low to medium traffic condition but LOS may increase under high traffic condition
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