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Author: Liudmila Tumash Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
This research is done in the context of European Research Council's Advanced Grant project Scale-FreeBack. The aim of Scale-FreeBack project is to develop a holistic scale-free control approach to complex systems, and to set new foundations for a theory dealing with complex physical networks with arbitrary dimension. One particular case is intelligent transportation systems that are capable to prevent the occurrence of congestions in rush hours. The contributions of the present PhD work are mainly related to traffic boundary control design and modelling on large-scale urban networks. We consider traffic from the macroscopic viewpoint describing it in terms of aggregated variables such as flow and density of vehicles, i.e., traffic is seen as a fluid whose motion is described using the concept of kinematic waves. The corresponding dynamic equation corresponds to a first-order hyperbolic partial differential equation. Within this PhD thesis, we propose control design techniques that completely rely on the intrinsic properties of the model. First of all, we solve one-dimensional (1D) boundary control problems, i.e., one road traffic. Thereby, the traffic state is driven to a space- and time-dependent desired trajectory that admits traffic regimes switching, i.e., both states can be partially congested and partially in the free-flow regime. This introduces non-linearities into the state equation, which we can handle and achieve the target by acting only from road's boundaries. Then, we extend the problem to a urban network of arbitrary size. The large-scale traffic dynamics are described by a two-dimensional (2D) conservation law model. The model parameters are defined everywhere in the continuum plane from its values on physical roads that are further interpolated as a function of distance to these roads. The traffic flow direction is determined by network's geometry (location of roads and intersections) and infrastructure parameters (speed limits, number of lanes, etc). This 2D model assumes that there exists a preferred direction of motion. For this case, we elaborate a unique method that considerably simplifies control design for traffic systems evolving in large-scale networks. In particular, we present a coordinate transformation that translates a 2D continuous traffic model into a continuous set of 1D systems equations. This enables an explicit elaboration of strategies for various control tasks to solve on large-scale networks: we design boundary control for 2D density in a mixed traffic regime, apply variable speed limit control to drive traffic to any space-dependent equilibrium, and calculate steady-states. Finally, we also present a new multi-directional two-dimensional continuous traffic model. This model is formally derived by solely using the demand-supply concept at one intersection (classical Cell Transmission Model). Our new model is called the NSWE-model, since it consists of four partial differential equations that describe the evolution of vehicle density with respect to cardinal directions: North, South, West and East. The traffic flow direction is determined by turning ratios at intersections. For this model, we design a boundary control that drives multi-directional congested traffic to a desired equilibrium vehicle density mitigating the congestion level. The effectiveness of our contributions were tested using simulated and real data. In the first case, the results are verified by using the well-known commercial traffic Aimsun, which produces microsimulations of vehicles' trajectories in a modelled network. In the second case, real data are obtained from sensors measuring traffic flow in the city of Grenoble, and collected using the Grenoble Traffic Lab.
Author: Liudmila Tumash Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
This research is done in the context of European Research Council's Advanced Grant project Scale-FreeBack. The aim of Scale-FreeBack project is to develop a holistic scale-free control approach to complex systems, and to set new foundations for a theory dealing with complex physical networks with arbitrary dimension. One particular case is intelligent transportation systems that are capable to prevent the occurrence of congestions in rush hours. The contributions of the present PhD work are mainly related to traffic boundary control design and modelling on large-scale urban networks. We consider traffic from the macroscopic viewpoint describing it in terms of aggregated variables such as flow and density of vehicles, i.e., traffic is seen as a fluid whose motion is described using the concept of kinematic waves. The corresponding dynamic equation corresponds to a first-order hyperbolic partial differential equation. Within this PhD thesis, we propose control design techniques that completely rely on the intrinsic properties of the model. First of all, we solve one-dimensional (1D) boundary control problems, i.e., one road traffic. Thereby, the traffic state is driven to a space- and time-dependent desired trajectory that admits traffic regimes switching, i.e., both states can be partially congested and partially in the free-flow regime. This introduces non-linearities into the state equation, which we can handle and achieve the target by acting only from road's boundaries. Then, we extend the problem to a urban network of arbitrary size. The large-scale traffic dynamics are described by a two-dimensional (2D) conservation law model. The model parameters are defined everywhere in the continuum plane from its values on physical roads that are further interpolated as a function of distance to these roads. The traffic flow direction is determined by network's geometry (location of roads and intersections) and infrastructure parameters (speed limits, number of lanes, etc). This 2D model assumes that there exists a preferred direction of motion. For this case, we elaborate a unique method that considerably simplifies control design for traffic systems evolving in large-scale networks. In particular, we present a coordinate transformation that translates a 2D continuous traffic model into a continuous set of 1D systems equations. This enables an explicit elaboration of strategies for various control tasks to solve on large-scale networks: we design boundary control for 2D density in a mixed traffic regime, apply variable speed limit control to drive traffic to any space-dependent equilibrium, and calculate steady-states. Finally, we also present a new multi-directional two-dimensional continuous traffic model. This model is formally derived by solely using the demand-supply concept at one intersection (classical Cell Transmission Model). Our new model is called the NSWE-model, since it consists of four partial differential equations that describe the evolution of vehicle density with respect to cardinal directions: North, South, West and East. The traffic flow direction is determined by turning ratios at intersections. For this model, we design a boundary control that drives multi-directional congested traffic to a desired equilibrium vehicle density mitigating the congestion level. The effectiveness of our contributions were tested using simulated and real data. In the first case, the results are verified by using the well-known commercial traffic Aimsun, which produces microsimulations of vehicles' trajectories in a modelled network. In the second case, real data are obtained from sensors measuring traffic flow in the city of Grenoble, and collected using the Grenoble Traffic Lab.
Author: Pietro Grandinetti Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
The thesis focuses on traffic lights control in large scale urban networks. It starts off with a study of macroscopic modeling based on the Cell Transmission model. We formulate a signalized version of such a model in order to include traffic lights' description into the dynamics. Moreover, we introduce two simplifications of the signalized model towards control design, one that is based on the average theory and considers duty cycles of traffic lights, and a second one that describes traffic lights trajectories with the time instants of the rising and falling edges of a binary signals. We use numerical simulations to validate the models with respect to the signalized Cell Transmission model, and microsimulations (with the software Aimsun), to validate the same model with respect to realistic vehicles' behavior.We propose two control algorithms based on the two models above mentioned. The first one, that uses the average Cell Transmission model, considers traffic lights' duty cycles as controlled variables and it is formulated as an optimization problem of standard traffic measures. We analyze such a problem and we show that it is equivalent to a convex optimization problem, so ensuring its computational efficiency. We analyze its performance with respect to a best-practice control scheme both in MatLab simulations and in Aimsun simulations that emulate a large portion of Grenoble, France. The second proposed approach is an optimization problem in which the decision variables are the activation and deactivation time instants of every traffic lights. We employ the Big-M modeling technique to reformulate such a problem as a mixed integer linear program, and we show via numerical simulations that the expressivity of it can lead to improvements of the traffic dynamics, at the price of the computational efficiency of the control scheme.To pursue the scalability of the proposed control techniques we develop two iterative distributed approaches to the traffic lights control problem. The first, based on the convex optimization above mentioned, uses the dual descent technique and its provably optimal, that is, it gives the same solution of the centralized optimization. The second, based on the mixed integer problem aforesaid, is a suboptimal algorithm that leads to substantial improvements by means of the computational efficiency with respect to the related centralized problem. We analyze via numerical simulations the convergence speed of the iterative algorithms, their computational burden and their performance regarding traffic metrics.The thesis is concluded with a study of the traffic lights control algorithm that is employed in several large intersections in Grenoble. We present the working principle of such an algorithm, detailing technological and methodological differences with our proposed approaches. We create into Aimsun the scenario representing the related part of the city, also reproducing the control algorithm and comparing its performance with the ones given by one of our approaches on the same scenario.
Author: Xueyu Gao Publisher: ISBN: Category : Languages : en Pages :
Book Description
Recent work has proposed using aggregate relationships between urban traffic variables--i.e., Macroscopic Fundamental Diagrams (MFDs)--to describe aggregate traffic dynamics in urban networks. This approach is particularly useful to unveil and explore the effects of various network-wide control strategies. The majority of modeling work using MFDs hinges upon the existence of well-defined MFDs without consideration of uncertain behaviors. However, both empirical data and theoretical analysis have demonstrated that MFDs are expected to be uncertain due to inherent instabilities that exist in traffic networks. Fortunately, sufficient amounts of adaptive drivers who re-route to avoid congestion have been proven to help eliminate the instability of MFDs. Unfortunately, drivers cannot re-route themselves adaptively all the time as routing choices are controlled by multiple factors, and the presence of adaptive drivers is not something that traffic engineers can control. Since MFDs have shown promise in the design and control of urban networks, it is important to seek another strategy to mitigate or eliminate the instability of MFDs. Furthermore, it is necessary to develop a framework to account for the uncertain phenomena that emerges on the macroscopic, network-wide level to address these unavoidable stochastic behaviors.This first half of this work investigates another strategy to eliminate inherent network instabilities and produce more reliable MFDs that is reliable and controllable from an engineering perspective--the use of adaptive traffic signals. A family of adaptive signal control strategies is examined on two abstractions of an idealized grid network using an interactive simulation and analytical model. The results suggest that adaptive traffic signals should provide a stabilizing influence that provides more well-defined MFDs. Adaptive signal control also both increases average flows and decreases the likelihood of gridlock when the network is moderately congested. The benefits achieved at these moderately congested states increase with the level of signal adaptivity. However, when the network is extremely congested, vehicle movements become more constrained by downstream congestion and queue spillbacks than by traffic signals, and adaptive traffic signals appear to have little to no effect on the network or MFD. When a network is extremely congested, other strategies should be used to mitigate the instability, like adaptively routing drivers. Therefore, without sufficient amounts of adaptive drivers, the instability of MFDs could be somewhat controlled, but it cannot be eliminated completely. This is results in more reliable MFDs until the network enters heavily congested states. The second half of this work uses stochastic differential equations (SDEs) to depict the evolutionary dynamics of urban network while accounting for unavoidable uncertain phenomena. General analytical solutions of SDEs only exist for linear functions. Unfortunately, most MFDs observed from simulation and empirical data follow non-linear functions. Even the most simplified theoretical model is piecewise linear with breakpoints that cannot be readily accommodated by the linear SDE approach. To overcome this limitation, the SDE well-known solutions are used to develop an approximate solution method that relies on the discretization of the continuous state space. This process is memoryless and results in the development of a computationally efficient Markov Chain (MC) framework. The MC model is also supported by a well-developed theory which facilitates the estimation of future states or steady state equilibrium conditions in a network that explicitly accounts for MFD uncertainty. Due to the fact that current formalization of Markov Chains is restricted with a countable state space, some assumptions which redefine the traffic state and stochastic dynamic process need to be set for the MC model application in dynamic traffic analysis. These assumptions could be sabotaged by inappropriate parameter selections, producing excessive errors in analytical solutions. Therefore, a parametric study is performed here to illustrate how to select two key parameters, i.e. bin size and time interval to optimize the MC models and minimize errors.The major advantage of MC models is its wide flexibility, which has been demonstrated by showing how this method could well handle a wide variety of variables. A family of numerical tests are designed to include instability of MFD model, stochastic traffic demand, different city layouts and different forms of MFDs in the scenarios under static metering strategies. The results suggest that analytical solutions derived from MC models could accurately predict the future traffic state at any moment. Furthermore, the theoretical analysis also illustrates that Markov chains could easily model dynamic traffic control based on traffic state and pre-determined time-varying strategies by adjusting the transition matrix. Overall, the developed MC models are promising in the dynamic analysis of complicated urban network control under uncertainty for which simpler algebraic solutions do not exist.
Author: Nathan H. Gartner Publisher: Springer Science & Business Media ISBN: 3642796419 Category : Business & Economics Languages : en Pages : 376
Book Description
The problems of urban traffic in the industrially developed countries have been at the top of the priority list for a long time. While making a critical contribution to the economic well being of those countries, transportation systems in general and highway traffic in particular, also have detrimental effects which are evident in excessive congestion, high rates of accidents and severe pollution problems. Scientists from different disciplines have played an important role in the development and refinement of the tools needed for the planning, analysis, and control of urban traffic networks. In the past several years, there were particularly rapid advances in two areas that affect urban traffic: 1. Modeling of traffic flows in urban networks and the prediction of the resulting equilibrium conditions; 2. Technology for communication with the driver and the ability to guide him, by providing him with useful, relevant and updated information, to his desired destination.
Author: Todor Stoilov Publisher: Springer Science & Business Media ISBN: 9400900171 Category : Mathematics Languages : en Pages : 284
Book Description
Multilevel decision theory arises to resolve the contradiction between increasing requirements towards the process of design, synthesis, control and management of complex systems and the limitation of the power of technical, control, computer and other executive devices, which have to perform actions and to satisfy requirements in real time. This theory rises suggestions how to replace the centralised management of the system by hierarchical co-ordination of sub-processes. All sub-processes have lower dimensions, which support easier management and decision making. But the sub-processes are interconnected and they influence each other. Multilevel systems theory supports two main methodological tools: decomposition and co-ordination. Both have been developed, and implemented in practical applications concerning design, control and management of complex systems. In general, it is always beneficial to find the best or optimal solution in processes of system design, control and management. The real tendency towards the best (optimal) decision requires to present all activities in the form of a definition and then the solution of an appropriate optimization problem. Every optimization process needs the mathematical definition and solution of a well stated optimization problem. These problems belong to two classes: static optimization and dynamic optimization. Static optimization problems are solved applying methods of mathematical programming: conditional and unconditional optimization. Dynamic optimization problems are solved by methods of variation calculus: Euler Lagrange method; maximum principle; dynamical programming.
Author: Victor L. Knoop Publisher: Springer ISBN: 3319334824 Category : Computers Languages : en Pages : 641
Book Description
The Conference on Traffic and Granular Flow brings together international researchers from different fields ranging from physics to computer science and engineering to discuss the latest developments in traffic-related systems. Originally conceived to facilitate new ideas by considering the similarities of traffic and granular flow, TGF'15, organised by Delft University of Technology, now covers a broad range of topics related to driven particle and transport systems. Besides the classical topics of granular flow and highway traffic, its scope includes data transport (Internet traffic), pedestrian and evacuation dynamics, intercellular transport, swarm behaviour and the collective dynamics of other biological systems. Recent advances in modelling, computer simulation and phenomenology are presented, and prospects for applications, for example to traffic control, are discussed. The conference explores the interrelations between the above-mentioned fields and offers the opportunity to stimulate interdisciplinary research, exchange ideas, and meet many experts in these areas of research.
Author: Athanasia Karakitsiou Publisher: Springer ISBN: 3319653385 Category : Mathematics Languages : en Pages : 286
Book Description
Sustainable development within urban and rural areas, transportation systems, logistics, supply chain management, urban health, social services, and architectural design are taken into consideration in the cohesive network models provided in this book. The ideas, methods, and models presented consider city landscapes and quality of life conditions based on mathematical network models and optimization. Interdisciplinary Works from prominent researchers in mathematical modeling, optimization, architecture, engineering, and physics are featured in this volume to promote health and well-being through design. Specific topics include: - Current technology that form the basis of future living in smart cities - Interdisciplinary design and networking of large-scale urban systems - Network communication and route traffic optimization - Carbon dioxide emission reduction - Closed-loop logistics chain management and operation - Modeling the effect urban environments on aging - Health care infrastructure - Urban water system management - Architectural design optimization Graduate students and researchers actively involved in architecture, engineering, building physics, logistics, supply chain management, and mathematical optimization will find the interdisciplinary work presented both informative and inspiring for further research.
Author: Liudmila Tumash
Author: Liudmila Tumash
Author: Mohsen Ramezani Ghalenoei
Author: Shu Lin
Author: Pietro Grandinetti
Author: Xueyu Gao
Author: Nathan H. Gartner
Author: Todor Stoilov
Author: Victor L. Knoop
Author: Mohammadreza Saeedmanesh
Author: Athanasia Karakitsiou