8 research outputs found
Network based data oriented methods for application driven problems
Networks are amazing. If you think about it, some of them can be found in almost every single aspect of our life from sociological, financial and biological processes to the human body. Even considering entities that are not necessarily connected to each other in a natural sense, can be connected based on real life properties, creating a whole new aspect to express knowledge. A network as a structure implies not only interesting and complex mathematical questions, but the possibility to extract hidden and additional information from real life data. The data that is one of the most valuable resources of this century. The different activities of the society and the underlying processes produces a huge amount of data, which can be available for us due to the technological knowledge and tools we have nowadays. Nevertheless, the data without the contained knowledge does not represent value, thus the main focus in the last decade is to generate or extract information and knowledge from the data. Consequently, data analytics and science, as well as data-driven methodologies have become leading research fields both in scientific and industrial areas.
In this dissertation, the author introduces efficient algorithms to solve application oriented optimization and data analysis tasks built on network science based models. The main idea is to connect these problems along graph based approaches, from virus modelling on an existing system through understanding the spreading mechanism of an infection/influence and maximize or minimize the effect, to financial applications, such as fraud detection or cost optimization in a case of employee rostering
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Modeling and optimizing network infrastructure for autonomous vehicles
Autonomous vehicle (AV) technology has matured sufficiently to be in testing on public roads. However, traffic models of AVs are still in development. Most previous work has studied AV technologies in micro-simulation. The purpose of this dissertation is to model and optimize AV technologies for large city networks to predict how AVs might affect city traffic patterns and travel behaviors. To accomplish these goals, we construct a dynamic network loading model for AVs, consisting of link and node models of AV technologies, which is used to calculate time-dependent travel times in dynamic traffic assignment. We then study several applications of the dynamic network loading to predict how AVs might affect travel demand and traffic congestion. AVs admit reduced perception-reaction times through technologies such as (cooperative) adaptive cruise control, which can reduce following headways and increase capacity. Previous work has studied these in micro-simulation, but we construct a mesoscopic simulation model for analyses on large networks. To study scenarios with both autonomous and conventional vehicles, we modify the kinematic wave theory to include multiple classes of flow. The flow-density relationship also changes in space and time with the class proportions. We present multiclass cell transmission model and prove that it is a Godunov approximation to the multiclass kinematic wave theory. We also develop a car-following model to predict the fundamental diagram at arbitrary proportions of AVs. Complete market penetration scenarios admit dynamic lane reversal -- changing lane direction at high frequencies to more optimally allocate road capacity. We develop a kinematic wave theory in which the number of lanes changes in space and time, and approximately solve it with a cell transmission model. We study two methods of determining lane direction. First, we present a mixed integer linear program for system optimal dynamic traffic assignment. Since this program is computationally difficult to solve, we also study dynamic lane reversal on a single link with deterministic and stochastic demands. The resulting policy is shown to significantly reduce travel times on a city network. AVs also admit reservation-based intersection control, which can make greater use of intersection capacity than traffic signals. AVs communicate with the intersection manager to reserve space-time paths through the intersection. We create a mesoscopic node model by starting with the conflict point variant of reservations and aggregating conflict points into capacity-constrained conflict regions. This model yields an integer program that can be adapted to arbitrary objective functions. To motivate optimization, we present several examples on theoretical and realistic networks demonstrating that naïve reservation policies can perform worse than traffic signals. These occur due to asymmetric intersections affecting optimal capacity allocation and/or user equilibrium route choice behavior. To improve reservations, we adapt the decentralized backpressure wireless packet routing and P0 traffic signal policies for reservations. Results show significant reductions in travel times on a city network. Having developed link and node models, we explore how AVs might affect travel demand and congestion. First, we study how capacity increases and reservations might affect freeway, arterial, and city networks. Capacity increases consistently reduced congestion on all networks, but reservations were not always beneficial. Then, we use dynamic traffic assignment within a four-step planning model, adding the mode choice of empty repositioning trips to avoid parking costs. Results show that allowing empty repositioning to encourage adoption of AVs could reduce congestion. Also, once all vehicles are AVs, congestion will still be significantly reduced. Finally, we present a framework to use the dynamic network loading model to study shared AVs. Results show that shared AVs could reduce congestion if used in certain ways, such as with dynamic ride-sharing. However, shared AVs also cause significant congestion. To summarize, this dissertation presents a complete mesoscopic simulation model of AVs that could be used for a variety of studies of AVs by planners and practitioners. This mesoscopic model includes new node and link technologies that significantly improve travel times over existing infrastructure. In addition, we motivate and present more optimal policies for these AV technologies. Finally, we study several travel behavior scenarios to provide insights about how AV technologies might affect future traffic congestion. The models in this dissertation will provide a basis for future network analyses of AV technologies.Civil, Architectural, and Environmental Engineerin
Applications of Game Theory to Multi-Agent Coordination Problems in Communication Networks
Recent years there has been a growing interest in the study of distributed control mechanisms for use in communication networks. A fundamental assumption in these models is that the participants in the network are willing to cooperate with the system. However, there are many instances where the incentives to cooperate is missing. Then, the agents may seek to achieve their own private interests by behaving strategically. Often, such selfish choices lead to inefficient equilibrium state of the system, commonly known as the tragedy of commons in Economics terminology. Now, one may ask the following question: how can the system be led to the socially optimal state in spite of selfish behaviors of its participants? The traditional control design framework fails to provide an answer as it does not take into account of selfish and strategic behavior of the agents. The use of game theoretical methods to achieve coordination in such network systems is appealing, as it naturally captures the idea of rational agents taking locally optimal decisions.
In this thesis, we explore several instances of coordination problems in communication networks that can be analyzed using game theoretical methods. We study one coordination problem each, from each layer of TCP/IP reference model - the network model used in the current Internet architecture. First, we consider societal agents taking decisions on whether to obtain content legally or illegally, and tie their behavior to questions of performance of content distribution networks. We show that revenue sharing with peers promote performance and revenue extraction from content distribution networks. Next, we consider a transport layer problem where applications compete against each other to meet their performance objectives by selfishly picking congestion controllers. We establish that tolling schemes that incentivize applications to choose one of several different virtual networks catering to particular needs yields higher system value. Hence, we propose the adoption of such virtual networks. We address a network layer question in third problem. How do the sources in a wireless network split their traffic over the available set of paths to attain the lowest possible number of transmissions per unit time? We develop a two level distributed controller that attains the optimal traffic split. Finally, we study mobile applications competing for channel access in a cellular network. We show that the mechanism where base station conducting sequence of second price auctions and providing channel access to the winner achieves the benefits of the state of art solution, Largest Queue First policy
Efficient path search in intermodal transportation optimization
As the economies of the world become more interrelated and Supply Chains are globalizing, the need arises to create efficient transportation network. This reality in conjunction with conservation of fuel and environmental friendliness gives rise to the research of Efficient Intermodal Transportation System. In particular, the underutilization of railroads in the United States motivates us to research the development of optimal procedures in the transportation of containers in a rail network. With this thesis we search for a cost, time and capacity effective algorithm for solving transportation problem in a graph of intermodal centers (IMC\u27s). We consider discrete model of the real time dynamic situation when all the arcs of the input graph can be affected by changes in their costs, the transportation means have limited and different container capacities at each IMC, and all the nodes (IMC\u27s) can be visited more than once either by different transport means or at different time. This is more general and real situation than the ones considered in the literature so far. The resulting optimization problem is computational intractable (NP-hard), which creates the necessity to develop, implement and test efficient heuristic optimization techniques. We will use Shortest Path Problem (SPP) as the basis for the development of three heuristics. Because of the nature of the problem and application, shortest path procedures provide a very flexible and computationally efficient technique for our model. We will compare the three heuristics with the optimal solution for small size problems for which we could find optimality. Furthermore, we will demonstrate that one of the heuristics perform very well when the fixed costs of running transportation modes is the dominant aspect of the cost structure
Auction algorithms for shortest hyperpath problems
The auction-reduction algorithm is a strongly polynomial version of the auction method for the shortest path problem. In this paper we extend the auction-reduction algorithm to different types of shortest hyperpath problems in directed hypergraphs. The results of preliminary computational experiences show that the auction-reduction method is comparable to other known methods for specific classes of hypergraphs