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Author: Avnish K. Bansal
Publisher:
ISBN:
Category : Traffic engineering
Languages : en
Pages : 152
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Author: Avnish K. Bansal
Publisher:
ISBN:
Category : Traffic engineering
Languages : en
Pages : 152
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Author: Nathan H. Gartner
Publisher: Springer Science & Business Media
ISBN: 3642796419
Category : Business & Economics
Languages : en
Pages : 376
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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: Yosef Sheffi
Publisher: Prentice Hall
ISBN:
Category : Political Science
Languages : en
Pages : 426
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Author: Xueyu Gao
Publisher:
ISBN:
Category :
Languages : en
Pages :
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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: Joanna White
Publisher:
ISBN:
Category :
Languages : en
Pages : 50
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Author: Carter Robert Ren-Deh Wang
Publisher:
ISBN:
Category :
Languages : en
Pages : 82
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Book Description
Exact numerical evaluation of the stationary joint queue-length distribution of a Markovian finite capacity network with arbitrary size and topology can be obtained numerically. Nonetheless, the main challenge to such an approach remains the dimensionality of the joint distribution, which is exponential in the number of queues. This thesis proposes an analytical approximation of the joint distribution with a dimension that is linear in the number of queues. The method decomposes the network into overlapping subnetworks. The state of each subnetwork is described aggregately, i.e. in terms of a reduced state space, while ensuring consistency with the disaggregate, i.e., full state space, distribution. This aggregation-disaggregation technique is proposed for the analysis of Markovian tandem finite capacity queueing networks. The model is validated. We present its use to address an urban traffic control problem, and show the added value of accounting for higher-order spatial between-queue dependency information in the control of congested networks. A second, distinct goal of this thesis is to examine the calibration of route choice parameters in microscopic traffic simulators. Automatically calibrating simulators using traffic counts requires describing the relationship between route choice and traffic flows. This thesis proposes an analytical finite capacity queueing model that accounts for the relationship between route choice and traffic flows. The method is embedded in a simulation-based optimization framework and applied to a calibration problem.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 40
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Author: Julio B. Clempner
Publisher: Springer Nature
ISBN: 3031435753
Category : Technology & Engineering
Languages : en
Pages : 340
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This book considers a class of ergodic finite controllable Markov's chains. The main idea behind the method, described in this book, is to develop the original discrete optimization problems (or game models) in the space of randomized formulations, where the variables stand in for the distributions (mixed strategies or preferences) of the original discrete (pure) strategies in the use. The following suppositions are made: a finite state space, a limited action space, continuity of the probabilities and rewards associated with the actions, and a necessity for accessibility. These hypotheses lead to the existence of an optimal policy. The best course of action is always stationary. It is either simple (i.e., nonrandomized stationary) or composed of two nonrandomized policies, which is equivalent to randomly selecting one of two simple policies throughout each epoch by tossing a biased coin. As a bonus, the optimization procedure just has to repeatedly solve the time-average dynamic programming equation, making it theoretically feasible to choose the optimum course of action under the global restriction. In the ergodic cases the state distributions, generated by the corresponding transition equations, exponentially quickly converge to their stationary (final) values. This makes it possible to employ all widely used optimization methods (such as Gradient-like procedures, Extra-proximal method, Lagrange's multipliers, Tikhonov's regularization), including the related numerical techniques. In the book we tackle different problems and theoretical Markov models like controllable and ergodic Markov chains, multi-objective Pareto front solutions, partially observable Markov chains, continuous-time Markov chains, Nash equilibrium and Stackelberg equilibrium, Lyapunov-like function in Markov chains, Best-reply strategy, Bayesian incentive-compatible mechanisms, Bayesian Partially Observable Markov Games, bargaining solutions for Nash and Kalai-Smorodinsky formulations, multi-traffic signal-control synchronization problem, Rubinstein's non-cooperative bargaining solutions, the transfer pricing problem as bargaining.
Author: Gordon Frank Newell
Publisher: Elsevier Publishing Company
ISBN:
Category : Technology & Engineering
Languages : en
Pages : 476
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Author: Nathan H. Gartner
Publisher:
ISBN: 9780387590738
Category : Technology & Engineering
Languages : en
Pages : 375
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