Optimization Of Large Traffic Systems
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Optimization Of Large Traffic Systems
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Author :
language : en
Publisher:
Release Date : 1977
Optimization Of Large Traffic Systems written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1977 with Automobiles categories.
Computationally Efficient Simulation Based Optimization Algorithms For Large Scale Urban Transportation Problems
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Author : Linsen Chong
language : en
Publisher:
Release Date : 2017
Computationally Efficient Simulation Based Optimization Algorithms For Large Scale Urban Transportation Problems written by Linsen Chong and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
In this thesis, we propose novel computationally efficient optimization algorithms that derive effective traffic management strategies to reduce congestion and improve the efficiency of urban transportation systems. The proposed algorithms enable the use of high-resolution yet computationally inefficient urban traffic simulators to address large-scale urban transportation optimization problems in a computationally efficient manner. The first and the second part of this thesis focus on large-scale offline transportation optimization problems with stochastic simulation-based objective functions, analytical differentiable constraints and high-dimensional decision variables. We propose two optimization algorithms to solve these problems. In the first part, we propose a simulation-based metamodel algorithm that combines the use of an analytical stationary traffic network model and a dynamic microscopic traffic simulator. In the second part, we propose a metamodel algorithm that combines the use of an analytical transient traffic network model and the microscopic simulator. In the first part, we use the first metamodel algorithm to solve a large-scale fixed-time traffic signal control problem of the Swiss city of Lausanne with limited simulation runs, showing that the proposed algorithm can derive signal plans that outperform traditional simulation-based optimization algorithms and a commercial traffic signal optimization software. In the second part, we use both algorithms to solve a time-dependent traffic signal control problem of Lausanne, showing that the metamodel with the transient analytical traffic model outperforms that with the stationary traffic model. The third part of this thesis focuses on large-scale online transportation problems, which need to be solved with limited computational time. We propose a new optimization framework that combines the use of a problem-specific model-driven method, i.e., the method proposed in the first part, with a generic data-driven supervised machine learning method. We use this framework to address a traffic responsive control problem of Lausanne. We compare the performance of the proposed framework with the performance of an optimization framework with only the model-driven method and an optimization framework with only the data-driven method, showing that the proposed framework is able to derive signal plans that outperform the signal plans derived by the other two frameworks in most cases.
Control Of Traffic Systems In Buildings
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Author : Sandor A. Markon
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-11-22
Control Of Traffic Systems In Buildings written by Sandor A. Markon and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-11-22 with Technology & Engineering categories.
Transportation systems in buildings are part of everyday life: whether ferrying people twenty storeys up to the office or moving luggage at the airport, 21st-century society relies on them. This book presents the latest in analysis and control of transportation systems in buildings focusing primarily on elevator groups. The theory and design of passenger and cargo transport systems are covered, with operational examples and topics of special interest.
Optimized Time Dependent Congestion Pricing System For Large Networks
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Author : Aya Aboudina
language : en
Publisher:
Release Date : 2016
Optimized Time Dependent Congestion Pricing System For Large Networks written by Aya Aboudina and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.
Congestion pricing is one of the most widely contemplated methods to manage traffic congestion. The purpose of congestion pricing is to manage traffic demand generation and supply allocation by charging fees (i.e., tolling) for the use of certain roads in order to distribute traffic demand more evenly over time and space. This study presents a system for large-scale optimal time-varying congestion pricing policy determination and evaluation. The proposed system integrates a theoretical model of dynamic congestion pricing, a distributed optimization algorithm, a departure time choice model, and a dynamic traffic assignment (DTA) simulation platform, creating a unified optimal (location- and time-specific) congestion pricing system. The system determines and evaluates the impact of optimal tolling on road traffic congestion (supply side) and travellers' behavioural choices, including departure time and route choices (demand side). For the system's large-scale nature and the consequent computational challenges, the optimization algorithm is executed concurrently on a parallel cluster. The system is applied to simulation-based case studies of tolling major highways in the Greater Toronto Area (GTA) while capturing the regional effects of tolling. The models are developed and calibrated using regional household travel survey data that reflect travellers' heterogeneity. The DTA model is calibrated using actual traffic counts from the Ontario Ministry of Transportation and the City of Toronto. The main results indicate that: (1) more benefits are attained from variable tolling due to departure time rescheduling as opposed to mostly re-routing only in the case of flat tolling, (2) widespread spatial and temporal re-distributions of traffic are observed across the regional network in response to tolling significant - yet limited - highways in the region, (3) optimal variable pricing mirrors congestion patterns and induces departure time re-scheduling and rerouting patterns, resulting in improved average travel times and schedule delays at all scales, (4) tolled routes have different sensitivities to identical toll changes, (5) the start times of longer trips are more sensitive (elastic) to variable distance-based tolling policies compared to shorter trips, (6) optimal tolls intended to manage traffic demand are significantly lower than those intended to maximize toll revenues, (7) toll payers benefit from tolling even before toll revenues are spent, and (8) the optimal tolling policies determined offer a win-win solution in which travel times are improved while also raising funds to invest in sustainable transportation infrastructure.
Mobility Patterns Big Data And Transport Analytics
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Author : Constantinos Antoniou
language : en
Publisher: Elsevier
Release Date : 2018-11-27
Mobility Patterns Big Data And Transport Analytics written by Constantinos Antoniou and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-27 with Social Science categories.
Mobility Patterns, Big Data and Transport Analytics provides a guide to the new analytical framework and its relation to big data, focusing on capturing, predicting, visualizing and controlling mobility patterns - a key aspect of transportation modeling. The book features prominent international experts who provide overviews on new analytical frameworks, applications and concepts in mobility analysis and transportation systems. Users will find a detailed, mobility ‘structural’ analysis and a look at the extensive behavioral characteristics of transport, observability requirements and limitations for realistic transportation applications and transportation systems analysis that are related to complex processes and phenomena. This book bridges the gap between big data, data science, and transportation systems analysis with a study of big data’s impact on mobility and an introduction to the tools necessary to apply new techniques. The book covers in detail, mobility ‘structural’ analysis (and its dynamics), the extensive behavioral characteristics of transport, observability requirements and limitations for realistic transportation applications, and transportation systems analysis related to complex processes and phenomena. The book bridges the gap between big data, data science, and Transportation Systems Analysis with a study of big data’s impact on mobility, and an introduction to the tools necessary to apply new techniques. Guides readers through the paradigm-shifting opportunities and challenges of handling Big Data in transportation modeling and analytics Covers current analytical innovations focused on capturing, predicting, visualizing, and controlling mobility patterns, while discussing future trends Delivers an introduction to transportation-related information advances, providing a benchmark reference by world-leading experts in the field Captures and manages mobility patterns, covering multiple purposes and alternative transport modes, in a multi-disciplinary approach Companion website features videos showing the analyses performed, as well as test codes and data-sets, allowing readers to recreate the presented analyses and apply the highlighted techniques to their own data
Optimization And Equilibrium In Dynamic Networks And Applications In Traffic Systems
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Author : Maokai Lin
language : en
Publisher:
Release Date : 2015
Optimization And Equilibrium In Dynamic Networks And Applications In Traffic Systems written by Maokai Lin and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
This thesis discusses optimization problems and equilibrium in networks. There are three major parts of the thesis. In the first part, we discuss optimization in dynamic networks. We focus on two fundamental optimization problems in dynamic networks: the quickest flow problem and the quickest transshipment problem. The quickest flow problem is to find a minimum time needed to send a given amount of flow from one origin to one destination in a dynamic network. The quickest transshipment problem is similar to the quickest flow problem except with multiple origins and multiple destinations. We derive optimality conditions for the quickest flow problems and introduce simplified and more efficient algorithms for the quickest flow problems. For the quickest transshipment problem, we develop faster algorithms for several special cases and apply the approach to approximate an optimal solution more efficiently. In the second part, we discuss equilibrium in dynamic networks. We extend equilibrium results in static networks into dynamic networks and show that equilibria exist in a network where players either have the same origin or the same destination. We also introduce algorithms to compute such an equilibrium. Moreover, we analyze the average convergence speed of the best-response dynamics and connect equilibria in discrete network models to equilibria in continuous network models. In the third part, we introduce a new traffic information exchange system. The new system resolves the dilemma that broadcasting traffic predictions might affect drivers' behaviors and make the predictions inaccurate. We build game theoretic models to prove that drivers have incentives to use this system. In order to further test the effectiveness of such system, we run a series of behavioral experiments through an online traffic game. Experimental results show that drivers who use the system have a lower average travel time than the general public, and the system can help improve the average travel time of all drivers as the number of drivers who use this system increases.
Web Artificial Intelligence And Network Applications
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Author : Leonard Barolli
language : en
Publisher: Springer Nature
Release Date : 2020-03-30
Web Artificial Intelligence And Network Applications written by Leonard Barolli and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-30 with Technology & Engineering categories.
This proceedings book presents the latest research findings, and theoretical and practical perspectives on innovative methods and development techniques related to the emerging areas of Web computing, intelligent systems and Internet computing. The Web has become an important source of information, and techniques and methodologies that extract quality information are of paramount importance for many Web and Internet applications. Data mining and knowledge discovery play a key role in many of today's major Web applications, such as e-commerce and computer security. Moreover, Web services provide a new platform for enabling service-oriented systems. The emergence of large-scale distributed computing paradigms, such as cloud computing and mobile computing systems, has opened many opportunities for collaboration services, which are at the core of any information system. Artificial intelligence (AI) is an area of computer science that builds intelligent systems and algorithms that work and react like humans. AI techniques and computational intelligence are powerful tools for learning, adaptation, reasoning and planning, and they have the potential to become enabling technologies for future intelligent networks. Research in the field of intelligent systems, robotics, neuroscience, artificial intelligence and cognitive sciences is vital for the future development and innovation of Web and Internet applications. Chapter "An Event-Driven Multi Agent System for Scalable Traffic Optimization" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Network Design And Optimization For Smart Cities
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Author : Pardalos Panos M
language : en
Publisher: World Scientific
Release Date : 2017-05-03
Network Design And Optimization For Smart Cities written by Pardalos Panos M and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-05-03 with Political Science categories.
This comprehensive reference text is a collection of important research findings on the latest developments in network modeling for optimization of smart cities. Such models can be used from outlining the fundamental concepts of urban development to the description and optimization of physical networks, such as power, water or telecommunications. Networks help us understand city economics and various aspects of human interactions within cities with particular applications in quality of life and the flow of people and goods. Finally, the natural environment and even the climate of cities can be modeled and managed as networks.
Optimization In Large Scale Problems
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Author : Mahdi Fathi
language : en
Publisher: Springer Nature
Release Date : 2019-11-20
Optimization In Large Scale Problems written by Mahdi Fathi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-20 with Mathematics categories.
This volume provides resourceful thinking and insightful management solutions to the many challenges that decision makers face in their predictions, preparations, and implementations of the key elements that our societies and industries need to take as they move toward digitalization and smartness. The discussions within the book aim to uncover the sources of large-scale problems in socio-industrial dilemmas, and the theories that can support these challenges. How theories might also transition to real applications is another question that this book aims to uncover. In answer to the viewpoints expressed by several practitioners and academicians, this book aims to provide both a learning platform which spotlights open questions with related case studies. The relationship between Industry 4.0 and Society 5.0 provides the basis for the expert contributions in this book, highlighting the uses of analytical methods such as mathematical optimization, heuristic methods, decomposition methods, stochastic optimization, and more. The book will prove useful to researchers, students, and engineers in different domains who encounter large scale optimization problems and will encourage them to undertake research in this timely and practical field. The book splits into two parts. The first part covers a general perspective and challenges in a smart society and in industry. The second part covers several case studies and solutions from the operations research perspective for large scale challenges specific to various industry and society related phenomena.
Big Data Driven Optimization In Transportation And Communication Networks
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Author : Longbiao Chen
language : en
Publisher:
Release Date : 2018
Big Data Driven Optimization In Transportation And Communication Networks written by Longbiao Chen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
The evolution of metropolitan structures and the development of urban systems have created various kinds of urban networks, among which two types of networks are of great importance for our daily life, the transportation networks corresponding to human mobility in the physical space, and the communication networks supporting human interactions in the digital space. The rapid expansion in the scope and scale of these two networks raises a series of fundamental research questions on how to optimize these networks for their users. Some of the major objectives include demand responsiveness, anomaly awareness, cost effectiveness, energy efficiency, and service quality. Despite the distinct design intentions and implementation technologies, both the transportation and communication networks share common fundamental structures, and exhibit similar spatio-temporal dynamics. Correspondingly, there exists an array of key challenges that are common in the optimization in both networks, including network profiling, mobility prediction, traffic clustering, and resource allocation. To achieve the optimization objectives and address the research challenges, various analytical models, optimization algorithms, and simulation systems have been proposed and extensively studied across multiple disciplines. Generally, these simulation-based models are not evaluated in real-world networks, which may lead to sub-optimal results in deployment. With the emergence of ubiquitous sensing, communication and computing diagrams, a massive number of urban network data can be collected. Recent advances in big data analytics techniques have provided researchers great potentials to understand these data. Motivated by this trend, we aim to explore a new big data-driven network optimization paradigm, in which we address the above-mentioned research challenges by applying state-of-the-art data analytics methods to achieve network optimization goals. Following this research direction, in this dissertation, we propose two data-driven algorithms for network traffic clustering and user mobility prediction, and apply these algorithms to real-world optimization tasks in the transportation and communication networks. First, by analyzing large-scale traffic datasets from both networks, we propose a graph-based traffic clustering algorithm to better understand the traffic similarities and variations across different area and time. Upon this basis, we apply the traffic clustering algorithm to the following two network optimization applications. 1. Dynamic traffic clustering for demand-responsive bikeshare networks. In this application, we dynamically cluster bike stations with similar usage patterns to obtain stable and predictable cluster-wise bike traffic demands, so as to foresee over-demand stations in the network and enable demand-responsive bike scheduling. Evaluation results using real-world data from New York City and Washington, D.C. show that our framework accurately foresees over-demand clusters (e.g. with 0.882 precision and 0.938 recall in NYC), and outperforms other baseline methods significantly. 2. Complementary traffic clustering for cost-effective C-RAN. In this application, we cluster RRHs with complementary traffic patterns (e.g., an RRH in residential area and an RRH in business district) to reuse the total capacity of the BBUs, so as to reduce the overall deployment cost. We evaluate our framework with real-world network data collected from the city of Milan, Italy and the province of Trentino, Italy. Results show that our method effectively reduces the overall deployment cost to 48.4 % and 51.7 % of the traditional RAN architecture in the two datasets, respectively, and consistently outperforms other baseline methods. Second, by analyzing large-scale user mobility datasets from both networks, we propose [...].