Network Macroscopic Fundamental Diagrams (MFDs) have recently been shown to exist in real-world urban traffic networks. The existence of an MFD facilitates the modeling of urban traffic network dynamics at a regional level, which can be used to identify and refine large-scale network-wide control strategies. To be useful, MFD-based modeling frameworks require an estimate of the functional form of a network's MFD. Analytical methods have been proposed to estimate a network's MFD from its geometric features and traffic control strategies, such as average block length, cycle time, green ratio, free flow speed and link capacity. However, these existing methods fail to account for many features that may significantly impact network MFDs, such as vehicles' turning maneuver, bus dwelling, and street network hierarchy. In light of this, this dissertation analytically studies the MFDs of complicated networks to unveil features that may be expected in real-world MFD. The first part of this dissertation proposes a method to estimate a network's MFD when vehicles are allowed to turn into or out of a corridor. A two-ring abstraction is first used to analyze how turning will impact vehicle travel in a more general network, and then the model is further approximated using a single ring-road or corridor. The second part of this dissertation investigated the impact of bus dwelling on the functional form of network's MFD. The proposed method treats the randomly dwelling buses as stochastic bottlenecks and considers how these bottlenecks affect the network flow-density relationships. The third part of this dissertation examines how the presence of hierarchical roadway structures may influence a network's MFD. It is proved that the presence of roadway hierarchies may lead to MFDs that have non-unimodal or non-concave patterns on the free-flow branch when vehicles route themselves according to user equilibrium principles. The final part of this dissertation investigates the opposing hysteresis patterns in a network's flow- and outflow-MFD. A general explanation is provided for a recently discovered phenomenon that the flow-MFD exhibits a clockwise hysteresis loop, while the output-MFD exhibits a counter-clockwise loop.

The MATSim (Multi-Agent Transport Simulation) software project was started around 2006 with the goal of generating traffic and congestion patterns by following individual synthetic travelers through their daily or weekly activity programme. It has since then evolved from a collection of stand-alone C++ programs to an integrated Java-based framework which is publicly hosted, open-source available, automatically regression tested. It is currently used by about 40 groups throughout the world. This book takes stock of the current status. The first part of the book gives an introduction to the most important concepts, with the intention of enabling a potential user to set up and run basic simulations. The second part of the book describes how the basic functionality can be extended, for example by adding schedule-based public transit, electric or autonomous cars, paratransit, or within-day replanning. For each extension, the text provides pointers to the additional documentation and to the code base. It is also discussed how people with appropriate Java programming skills can write their own extensions, and plug them into the MATSim core. The project has started from the basic idea that traffic is a consequence of human behavior, and thus humans and their behavior should be the starting point of all modelling, and with the intuition that when simulations with 100 million particles are possible in computational physics, then behavior-oriented simulations with 10 million travelers should be possible in travel behavior research. The initial implementations thus combined concepts from computational physics and complex adaptive systems with concepts from travel behavior research. The third part of the book looks at theoretical concepts that are able to describe important aspects of the simulation system; for example, under certain conditions the code becomes a Monte Carlo engine sampling from a discrete choice model. Another important aspect is the interpretation of the MATSim score as utility in the microeconomic sense, opening up a connection to benefit cost analysis. Finally, the book collects use cases as they have been undertaken with MATSim. All current users of MATSim were invited to submit their work, and many followed with sometimes crisp and short and sometimes longer contributions, always with pointers to additional references. We hope that the book will become an invitation to explore, to build and to extend agent-based modeling of travel behavior from the stable and well tested core of MATSim documented here.

The objective of this PhD research was to explore how the complex interdependencies of a dense urban environment can be simulated through a hybrid multi-layered model, integrating agents at different resolutions. The model incorporated: (1) the graph model of an existing road network within Philadelphia, recognizing the engineering and operational characteristics of various road segments within this network; (2) vehicles and drivers as mesoscopic agents, where the vehicle types, counts and behaviors were calibrated by actual data; (3) a time and use based deterioration model and repair techniques for different elements of the road network; and, (4) an owner agent making various asset management decisions for the road network leading to four distinct scenarios. The mesoscopic traffic simulator therefore simulated feedback mechanisms linking the drivers' decisions and behaviors to the roadway steward's decisions, revealing the short-term consequences of management policies. This provided a realistic understanding of the "user costs," corresponding to different decisions on maintenance and replacement scheduling. The model proved to be a low cost, modular, sociotechnical, cloud-based model of a surface transportation urban infrastructure asset management simulator. The main computational engine selected for the model was an open-source mesoscopic traffic simulator, SUMO, which can represent driver agents, routing algorithms, and traffic control systems for a road network. This traffic model was defined and calibrated with data and reports from the local municipal planning organization. The deterioration of each roadway segment was modeled as a function of time and the simulated traffic's vehicular loads. The roadways' speed limit was a function of the deterioration and as a result, over time, the roadways became less favorable to the vehicles trying to optimize their daily path. At the beginning of each year the owner-agents assess the current deterioration levels and develop a plan and schedule either maintenance or replacement activities in order to meet specific condition objectives while also satisfying real-world constraints. The long-term planning was accomplished through the use of linear programming, while the short-term yearly schedule was achieved through a custom prioritized scheduling algorithm. A total of four different scenarios were generated to observe and compare the effects of different management policies and approaches. These included a baseline Do-Nothing scenario, a scenario based on a reactive short-term yearly scheduling scheme, and two scenarios that incorporated both short-term and long-term approaches to planning and scheduling with different constraints. A computer program was written in the Python language and adapted to take advantage of elastic cloud computing processing power to integrate the different modular components in order to simulate the daily activities within the network during a five-year period. The data from the simulations were then processed, analyzed, and visualized utilizing a combination of Python and Excel. The results for all the scenarios were compared with regards to the roadways' overall and yearly condition ratings as well as user-costs. Given the relatively short run-time of the simulation in comparison to the life-span of the assets, the short-term reactive scenario performed better than the two long-term based scenarios in the overall condition of the network. However, for longer-periods of simulation we should expect changes in the resulting operational performance and cost for different scenarios. The study was successful in demonstrating the potential of integrative modeling of complex sociotechnical infrastructures, and the computational requirements for simulating entire urban regions with interdependent infrastructures.

This book constitutes revised, selected, and invited papers from the First International Workshop on Agent Based Modelling of Urban Systems, ABMUS 2016, held in conjunction with AAMAS 2016 in Singapore in May 2016. The 11 papers presented in this volume were carefully reviewed and selected from 20 submissions. They were organized in topical sections named: urban systems modeling; traffic simulation in urban modeling; and applications.

The increasing power of computer technologies, the evolution of software en- neering and the advent of the intelligent transport systems has prompted traf c simulation to become one of the most used approaches for traf c analysis in s- port of the design and evaluation of traf c systems. The ability of traf c simulation to emulate the time variability of traf c phenomena makes it a unique tool for capturing the complexity of traf c systems. In recent years, traf c simulation – and namely microscopic traf c simulation – has moved from the academic to the professional world. A wide variety of traf- c simulation software is currently available on the market and it is utilized by thousands of users, consultants, researchers and public agencies. Microscopic traf c simulation based on the emulation of traf c ows from the dynamics of individual vehicles is becoming one the most attractive approaches. However, traf c simulation still lacks a uni ed treatment. Dozens of papers on theory and applications are published in scienti c journals every year. A search of simulation-related papers and workshops through the proceedings of the last annual TRB meetings would support this assertion, as would a review of the minutes from speci cally dedicated meetings such as the International Symposiums on Traf c Simulation (Yokohama, 2002; Lausanne, 2006; Brisbane, 2008) or the International Workshops on Traf c Modeling and Simulation (Tucson, 2001; Barcelona, 2003; Sedona, 2005; Graz 2008). Yet, the only comprehensive treatment of the subject to be found so far is in the user’s manuals of various software products.

Road Traffic Modeling and Management: Using Statistical Monitoring and Deep Learning provides a framework for understanding and enhancing road traffic monitoring and management. The book examines commonly used traffic analysis methodologies as well the emerging methods that use deep learning methods. Other sections discuss how to understand statistical models and machine learning algorithms and how to apply them to traffic modeling, estimation, forecasting and traffic congestion monitoring. Providing both a theoretical framework along with practical technical solutions, this book is ideal for researchers and practitioners who want to improve the performance of intelligent transportation systems. - Provides integrated, up-to-date and complete coverage of the key components for intelligent transportation systems: traffic modeling, forecasting, estimation and monitoring - Uses methods based on video and time series data for traffic modeling and forecasting - Includes case studies, key processes guidance and comparisons of different methodologies

Data-Driven Solutions to Transportation Problems explores the fundamental principle of analyzing different types of transportation-related data using methodologies such as the data fusion model, the big data mining approach, computer vision-enabled traffic sensing data analysis, and machine learning. The book examines the state-of-the-art in data-enabled methodologies, technologies and applications in transportation. Readers will learn how to solve problems relating to energy efficiency under connected vehicle environments, urban travel behavior, trajectory data-based travel pattern identification, public transportation analysis, traffic signal control efficiency, optimizing traffic networks network, and much more. - Synthesizes the newest developments in data-driven transportation science - Includes case studies and examples in each chapter that illustrate the application of methodologies and technologies employed - Useful for both theoretical and technically-oriented researchers

While the significance of networks in various human behavior and activities has a history as long as human's existence, network awareness is a recent scientific phenomenon. The neologism network science is just one or two decades old. Nevertheless, with this limited time, network thinking has substantially reshaped the recent development in economics, and almost all solutions to real-world problems involve the network element. This book integrates agent-based modeling and network science. It is divided into three parts, namely, foundations, primary dynamics on and of social networks, and applications. The authors begin with the network origin of agent-based models, known as cellular automata, and introduce a number of classic models, such as Schelling's segregation model and Axelrod's spatial game. The essence of the foundation part is the network-based agent-based models in which agents follow network-based decision rules. Under the influence of the substantial progress in network science in late 1990s, these models have been extended from using lattices into using small-world networks, scale-free networks, etc. The text also shows that the modern network science mainly driven by game-theorists and sociophysicists has inspired agent-based social scientists to develop alternative formation algorithms, known as agent-based social networks. It reviews a number of pioneering and representative models in this family. Upon the given foundation, the second part reviews three primary forms of network dynamics, such as diffusions, cascades, and influences. These primary dynamics are further extended and enriched by practical networks in goods-and-service markets, labor markets, and international trade. At the end, the book considers two challenging issues using agent-based models of networks: network risks and economic growth.

The purpose of this fantastically useful book is to lay out an overview on possible tools for state reconstruction in nonlinear systems. Here, basic observability notions and observer structures are recalled, together with ingredients for advanced designs on this basis. The problem of state reconstruction in dynamical systems, known as observer problem, is crucial for controlling or even merely monitoring processes. For linear systems, the theory has been well established for several years, so this book attempts to tackle the problem for non-linear systems.