15,114 research outputs found
Multiobjective Tactical Planning under Uncertainty for Air Traffic Flow and Capacity Management
We investigate a method to deal with congestion of sectors and delays in the
tactical phase of air traffic flow and capacity management. It relies on
temporal objectives given for every point of the flight plans and shared among
the controllers in order to create a collaborative environment. This would
enhance the transition from the network view of the flow management to the
local view of air traffic control. Uncertainty is modeled at the trajectory
level with temporal information on the boundary points of the crossed sectors
and then, we infer the probabilistic occupancy count. Therefore, we can model
the accuracy of the trajectory prediction in the optimization process in order
to fix some safety margins. On the one hand, more accurate is our prediction;
more efficient will be the proposed solutions, because of the tighter safety
margins. On the other hand, when uncertainty is not negligible, the proposed
solutions will be more robust to disruptions. Furthermore, a multiobjective
algorithm is used to find the tradeoff between the delays and congestion, which
are antagonist in airspace with high traffic density. The flow management
position can choose manually, or automatically with a preference-based
algorithm, the adequate solution. This method is tested against two instances,
one with 10 flights and 5 sectors and one with 300 flights and 16 sectors.Comment: IEEE Congress on Evolutionary Computation (2013). arXiv admin note:
substantial text overlap with arXiv:1309.391
Computational Methods for Probabilistic Inference of Sector Congestion in Air Traffic Management
This article addresses the issue of computing the expected cost functions
from a probabilistic model of the air traffic flow and capacity management. The
Clenshaw-Curtis quadrature is compared to Monte-Carlo algorithms defined
specifically for this problem. By tailoring the algorithms to this model, we
reduce the computational burden in order to simulate real instances. The study
shows that the Monte-Carlo algorithm is more sensible to the amount of
uncertainty in the system, but has the advantage to return a result with the
associated accuracy on demand. The performances for both approaches are
comparable for the computation of the expected cost of delay and the expected
cost of congestion. Finally, this study shows some evidences that the
simulation of the proposed probabilistic model is tractable for realistic
instances.Comment: Interdisciplinary Science for Innovative Air Traffic Management
(2013
A Holistic Approach to Forecasting Wholesale Energy Market Prices
Electricity market price predictions enable energy market participants to
shape their consumption or supply while meeting their economic and
environmental objectives. By utilizing the basic properties of the
supply-demand matching process performed by grid operators, known as Optimal
Power Flow (OPF), we develop a methodology to recover energy market's structure
and predict the resulting nodal prices by using only publicly available data,
specifically grid-wide generation type mix, system load, and historical prices.
Our methodology uses the latest advancements in statistical learning to cope
with high dimensional and sparse real power grid topologies, as well as scarce,
public market data, while exploiting structural characteristics of the
underlying OPF mechanism. Rigorous validations using the Southwest Power Pool
(SPP) market data reveal a strong correlation between the grid level mix and
corresponding market prices, resulting in accurate day-ahead predictions of
real time prices. The proposed approach demonstrates remarkable proximity to
the state-of-the-art industry benchmark while assuming a fully decentralized,
market-participant perspective. Finally, we recognize the limitations of the
proposed and other evaluated methodologies in predicting large price spike
values.Comment: 14 pages, 14 figures. Accepted for publication in IEEE Transactions
on Power System
Implementation and Evaluation of a Cooperative Vehicle-to-Pedestrian Safety Application
While the development of Vehicle-to-Vehicle (V2V) safety applications based
on Dedicated Short-Range Communications (DSRC) has been extensively undergoing
standardization for more than a decade, such applications are extremely missing
for Vulnerable Road Users (VRUs). Nonexistence of collaborative systems between
VRUs and vehicles was the main reason for this lack of attention. Recent
developments in Wi-Fi Direct and DSRC-enabled smartphones are changing this
perspective. Leveraging the existing V2V platforms, we propose a new framework
using a DSRC-enabled smartphone to extend safety benefits to VRUs. The
interoperability of applications between vehicles and portable DSRC enabled
devices is achieved through the SAE J2735 Personal Safety Message (PSM).
However, considering the fact that VRU movement dynamics, response times, and
crash scenarios are fundamentally different from vehicles, a specific framework
should be designed for VRU safety applications to study their performance. In
this article, we first propose an end-to-end Vehicle-to-Pedestrian (V2P)
framework to provide situational awareness and hazard detection based on the
most common and injury-prone crash scenarios. The details of our VRU safety
module, including target classification and collision detection algorithms, are
explained next. Furthermore, we propose and evaluate a mitigating solution for
congestion and power consumption issues in such systems. Finally, the whole
system is implemented and analyzed for realistic crash scenarios
Fusing Loop and GPS Probe Measurements to Estimate Freeway Density
In an age of ever-increasing penetration of GPS-enabled mobile devices, the
potential of real-time "probe" location information for estimating the state of
transportation networks is receiving increasing attention. Much work has been
done on using probe data to estimate the current speed of vehicle traffic (or
equivalently, trip travel time). While travel times are useful to individual
drivers, the state variable for a large class of traffic models and control
algorithms is vehicle density. Our goal is to use probe data to supplement
traditional, fixed-location loop detector data for density estimation. To this
end, we derive a method based on Rao-Blackwellized particle filters, a
sequential Monte Carlo scheme. We present a simulation where we obtain a 30\%
reduction in density mean absolute percentage error from fusing loop and probe
data, vs. using loop data alone. We also present results using real data from a
19-mile freeway section in Los Angeles, California, where we obtain a 31\%
reduction. In addition, our method's estimate when using only the real-world
probe data, and no loop data, outperformed the estimate produced when only loop
data were used (an 18\% reduction). These results demonstrate that probe data
can be used for traffic density estimation
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An Assessment of PIER Electric Grid Research 2003-2014 White Paper
This white paper describes the circumstances in California around the turn of the 21st century that led the California Energy Commission (CEC) to direct additional Public Interest Energy Research funds to address critical electric grid issues, especially those arising from integrating high penetrations of variable renewable generation with the electric grid. It contains an assessment of the beneficial science and technology advances of the resultant portfolio of electric grid research projects administered under the direction of the CEC by a competitively selected contractor, the University of California’s California Institute for Energy and the Environment, from 2003-2014
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