990 research outputs found
Data-Driven Dynamic Robust Resource Allocation: Application to Efficient Transportation
The transformation to smarter cities brings an array of emerging urbanization challenges. With the development of technologies such as sensor networks, storage devices, and cloud computing, we are able to collect, store, and analyze a large amount of data in real time. Modern cities have brought to life unprecedented opportunities and challenges for allocating limited resources in a data-driven way. Intelligent transportation system is one emerging research area, in which sensing data provides us opportunities for understanding spatial-temporal patterns of demand human and mobility. However, greedy or matching algorithms that only deal with known requests are far from efficient in the long run without considering demand information predicted based on data.
In this dissertation, we develop a data-driven robust resource allocation framework to consider spatial-temporally correlated demand and demand uncertainties, motivated by the problem of efficient dispatching of taxi or autonomous vehicles. We first present a receding horizon control (RHC) framework to dispatch taxis towards predicted demand; this framework incorporates both information from historical record data and real-time GPS location and occupancy status data. It also allows us to allocate resource from a globally optimal perspective in a longer time period, besides the local level greedy or matching algorithm for assigning a passenger pick-up location of each vacant vehicle. The objectives include reducing both current and anticipated future total idle driving distance and matching spatial-temporal ratio between demand and supply for service quality. We then present a robust optimization method to consider spatial-temporally correlated demand model uncertainties that can be expressed in closed convex sets. Uncertainty sets of demand vectors are constructed from data based on theories in hypothesis testing, and the sets provide a desired probabilistic guarantee level for the performance of dispatch solutions. To minimize the average resource allocation cost under demand uncertainties, we develop a general data-driven dynamic distributionally robust resource allocation model. An efficient algorithm for building demand uncertainty sets that compatible with various demand prediction methods is developed. We prove equivalent computationally tractable forms of the robust and distributionally robust resource allocation problems using strong duality. The resource allocation problem aims to balance the demand-supply ratio at different nodes of the network with minimum balancing and re-balancing cost, with decision variables on the denominator that has not been covered by previous work.
Trace-driven analysis with real taxi operational record data of San Francisco shows that the RHC framework reduces the average total idle distance of taxis by 52%, and evaluations with over 100GB of New York City taxi trip data show that robust and distributionally robust dispatch methods reduce the average total idle distance by 10% more compared with non-robust solutions. Besides increasing the service efficiency by reducing total idle driving distance, the resource allocation methods in this dissertation also reduce the demand-supply ratio mismatch error across the city
Data-Driven Distributionally Robust Electric Vehicle Balancing for Autonomous Mobility-on-Demand Systems under Demand and Supply Uncertainties
Electric vehicles (EVs) are being rapidly adopted due to their economic and
societal benefits. Autonomous mobility-on-demand (AMoD) systems also embrace
this trend. However, the long charging time and high recharging frequency of
EVs pose challenges to efficiently managing EV AMoD systems. The complicated
dynamic charging and mobility process of EV AMoD systems makes the demand and
supply uncertainties significant when designing vehicle balancing algorithms.
In this work, we design a data-driven distributionally robust optimization
(DRO) approach to balance EVs for both the mobility service and the charging
process. The optimization goal is to minimize the worst-case expected cost
under both passenger mobility demand uncertainties and EV supply uncertainties.
We then propose a novel distributional uncertainty sets construction algorithm
that guarantees the produced parameters are contained in desired confidence
regions with a given probability. To solve the proposed DRO AMoD EV balancing
problem, we derive an equivalent computationally tractable convex optimization
problem. Based on real-world EV data of a taxi system, we show that with our
solution the average total balancing cost is reduced by 14.49%, and the average
mobility fairness and charging fairness are improved by 15.78% and 34.51%,
respectively, compared to solutions that do not consider uncertainties.Comment: 16 page
Ambulance Emergency Response Optimization in Developing Countries
The lack of emergency medical transportation is viewed as the main barrier to
the access of emergency medical care in low and middle-income countries
(LMICs). In this paper, we present a robust optimization approach to optimize
both the location and routing of emergency response vehicles, accounting for
uncertainty in travel times and spatial demand characteristic of LMICs. We
traveled to Dhaka, Bangladesh, the sixth largest and third most densely
populated city in the world, to conduct field research resulting in the
collection of two unique datasets that inform our approach. This data is
leveraged to develop machine learning methodologies to estimate demand for
emergency medical services in a LMIC setting and to predict the travel time
between any two locations in the road network for different times of day and
days of the week. We combine our robust optimization and machine learning
frameworks with real data to provide an in-depth investigation into three
policy-related questions. First, we demonstrate that outpost locations
optimized for weekday rush hour lead to good performance for all times of day
and days of the week. Second, we find that significant improvements in
emergency response times can be achieved by re-locating a small number of
outposts and that the performance of the current system could be replicated
using only 30% of the resources. Lastly, we show that a fleet of small
motorcycle-based ambulances has the potential to significantly outperform
traditional ambulance vans. In particular, they are able to capture three times
more demand while reducing the median response time by 42% due to increased
routing flexibility offered by nimble vehicles on a larger road network. Our
results provide practical insights for emergency response optimization that can
be leveraged by hospital-based and private ambulance providers in Dhaka and
other urban centers in LMICs
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Urban Air Mobility Market Study
The Booz Allen Team explored market size and potential barriers to Urban Air Mobility (UAM) by focusing on three potential markets – Airport Shuttle, Air Taxi, and Air Ambulance. We found that the Airport Shuttle and Air Taxi markets are viable, with a significant total available market value in the U.S. of 2.5 billion, in the near term. However, we determined that these constraints can be addressed through ongoing intra-governmental partnerships, government and industry collaboration, strong industry commitment, and existing legal and regulatory enablers. We found that the Air Ambulance market is not a viable market if served by electric vertical takeoff and landing (eVTOL) vehicles due to technology constraints but may potentially be viable if a hybrid VTOL aircraft are utilized
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Grid flexibility by electrifying energy systems for sustainable aviation
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonDecarbonisation of aviation goals set by Flightpath 2050 Europe’s Vision for Aviation
requires that the airports become emission-free by 2050. This thesis original contribution to
knowledge is to explore the incorporation of aviation electrification technologies, including
electric aircraft (EA), electrified ground support equipment (GSE), and airport parking electric
vehicles (EVs), into power systems, evaluating their influence on grid infrastructure and
operations, as well as their potential to support the grid operation.
A comprehensive review of aviation electrification technologies revealed a research gap in the
integration of these technologies into the power systems. The thesis contributes to electricity
network infrastructure planning for electrification of aviation and airport-based distributed
energy resources (DER) that provide ancillary services to the power grid.
A multi-objective airport microgrid planning framework is developed, comparing EA charging
strategies and revealing that battery swap performs better. Vehicle-to-grid (V2G) strategy with
parking EVs improves the microgrid's performance. A techno-economic assessment of wireless charging
systems for electric airport shuttle buses shows better economic performance than conventional
buses and other charging options.
A novel Aviation-to-Grid (A2G) flexibility concept provides frequency response services to the GB
power system using EA battery charging systems, with typical A2G service capacity showing
significant variation across eight UK airports. A deep reinforcement learning (DRL)-based A2G
dispatch approach evaluates the impact of EA charger capacity on energy dispatch results, with
higher capacities leading to higher revenue and lower operation costs.
To summarise, this thesis addresses the research gaps in integrating aviation
electrification technologies into power systems, offering valuable insights for airport operators
aiming to decarbonise air transport activities through the adoption of these technologies. The
study also provides an understanding of the impacts on grid operators in terms of infrastructure
planning and operations. This comprehensive approach ensures a cohesive understanding of the
challenges and opportunities presented by aviation
electrification and its integration into power systems
A Robust and Constrained Multi-Agent Reinforcement Learning Framework for Electric Vehicle AMoD Systems
Electric vehicles (EVs) play critical roles in autonomous mobility-on-demand
(AMoD) systems, but their unique charging patterns increase the model
uncertainties in AMoD systems (e.g. state transition probability). Since there
usually exists a mismatch between the training and test (true) environments,
incorporating model uncertainty into system design is of critical importance in
real-world applications. However, model uncertainties have not been considered
explicitly in EV AMoD system rebalancing by existing literature yet and remain
an urgent and challenging task. In this work, we design a robust and
constrained multi-agent reinforcement learning (MARL) framework with transition
kernel uncertainty for the EV rebalancing and charging problem. We then propose
a robust and constrained MARL algorithm (ROCOMA) that trains a robust EV
rebalancing policy to balance the supply-demand ratio and the charging
utilization rate across the whole city under state transition uncertainty.
Experiments show that the ROCOMA can learn an effective and robust rebalancing
policy. It outperforms non-robust MARL methods when there are model
uncertainties. It increases the system fairness by 19.6% and decreases the
rebalancing costs by 75.8%.Comment: 8 page
Probabilistic trajectory generation using uncertainty propagation model
This document establishes the basis for the work to be developed within Work Package 2 of the START project. The objective of this Work Package is to build a methodology that could allow for the obtainment of the probabilistic trajectories that would result from the propagation of the characterized micro-level uncertainties in the aircraft trajectory prediction process. This deliverable will be focused on implementing the models and processes required to capture the influence of the uncertainties that are present in the development of an aircraft trajectory. To this end, we will show how to propagate these uncertainties, using a stochastic trajectory predictor, that will allow us to obtain a set of probabilistic trajectories from an initial deterministic flight plan, which will encapsulate the effect of the inputs’ variability.
First, an introduction to Polynomial Chaos Theory, which is the basis of the stochastic trajectory predictor developed in START, and our solution for introducing weather uncertainty into the trajectory prediction process will be exposed. Then, it will be presented how the integration of the advanced data assimilation models, introduced in the deliverable D2.1 [2], together with the stochastic trajectory predictor will lead to more robust airline operations. Additionally, the framework for the probabilistic trajectory generation will be introduced, showing how all different modules will be employed in START in a two-phase approach (first an off-line fitting phase to obtain the models for uncertainty propagation, and then an online phase where, making use of the fitted model, the probabilistic trajectories can be obtained from a deterministic flight plan). Finally, a study case will be presented, showing the application of the previously defined methodology to a specific scenario.Preprin
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