Dynamic Network Traffic Assignment Considered as a Continuous Time Optimal Control Problem

Friesz, Terry L.; Luque, Javier; Tobin, Roger L.; Wie, Byung-Wook

doi:10.1287/opre.37.6.893

Cited by 370 publications

(172 citation statements)

References 13 publications

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“…A remarkable feature of these approaches is that they yield formal performance bounds for the control policies (i.e., factor of sub-optimality) and scaling laws for the quality of service in terms of model data, which can provide useful guidelines for selecting system parameters (e.g., number of vehicles). These approaches take their origin from seminal works on hypercube models for spatial queues [19], on the Dynamic Traveling Repairman problem [20,21,22,23], and on the Dynamic Traffic Assignment problem [24,25].…”

Section: Approaches For Controlling Amod Systemsmentioning

confidence: 99%

Autonomous Mobility-on-Demand Systems for Future Urban Mobility

Pavone

2015

Autonomes Fahren

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Section: Approaches For Controlling Amod Systemsmentioning

confidence: 99%

Autonomous Mobility-on-Demand Systems for Future Urban Mobility

Pavone

2015

Autonomes Fahren

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“…We mention finally in passing that the need to represent the effect of time-varying demand and queuing has already prompted a great deal of effort in developing a dynamic extension of Wardrop's equilibrium (for example : Friesz et al, 1989;Smith & Ghali, 1990;Janson, 1991;Papageorgiou, 1991). At present the problem is unsolved for general transportation networks; indeed, there is not even a consensus over what the dynamic extension should be.…”

Section: Dynamic Effectsmentioning

confidence: 99%

The modelling of dynamic route guidance systems

Watling

Vuren²

1993

Transportation Research Part C: Emerging Technologies

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In attempting to simulate the operation of a dynamic route guidance system, the modelling task is concerned both with the operation of the control system and with the implications this has for modelling driver hehaviour (whether or not the driver is receiving information from the controller) and network conditions. The aim of this paper is to provide an overview of the modelling issues which need to be considered when addressing such a problem, and which have been identified by various authors in reports on experimentallsurvey work and in discussion papers.In discussing the great number of challenges to the modelling world which have arisen from the interest in such systems, we seek to stimulate further discussion and to provide a framework within which any route guidance model may be critically evaluated. We consider such a framework to be particularly timely in the light of the wealth of simulation models currently being proposedand widely varying conclusions being drawn -as a result of many major research initiatives currently underway throughout the developed world.It is our belief that the development of a model which adequately represents the performance of a dynamic route guidance system is of the utmost importance to the success of such an approach.It will not only provide a means for evaluating the potential benefits, but should also provide an essential insight into the most appropriate means for its implementation and improve our understanding of transportation networks.

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“…Typical examples of static combinatorial optimization problems are the traveling salesman problem [1,2], the quadratic assignment problem [3] and the vehicle routing problem [4], whereas examples of dynamic combinatorial optimization problems are the packet routing problem [5][6][7][8][9][10][11][12][13][14][15][16][17] and the traffic flow control problem [18]. In static combinatorial optimization problems, the search space of the solution does not change.…”

Section: Introductionmentioning

confidence: 99%

An improved routing algorithm using chaotic neurodynamics for packet routing problems

Morita

Kimura

2018

NOLTA

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Abstract:Combinatorial optimization problems consist of static problems such as the traveling salesman problem and the quadratic assignment problem, and dynamic problems such as the packet routing problem and the traffic flow control problem. In static combinatorial optimization problems, the search space for the solution does not change over time and, therefore, neither does the optimal solution. On the contrary, in dynamic combinatorial optimization problems, the search space always changes. Thus, there is no guarantee that the optimal solution for one iteration also applies to the next iteration. In the context of dynamic combinatorial optimization problems, we propose in this paper a heuristic routing method that uses chaotic neurodynamics and degree information to solve the packet routing problem. Numerical experiments showed that the proposed method improves average arrival rate by approximately 130% over the conventional shortest hop method.

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Dynamic Network Traffic Assignment Considered as a Continuous Time Optimal Control Problem

Cited by 370 publications

References 13 publications

Autonomous Mobility-on-Demand Systems for Future Urban Mobility

Autonomous Mobility-on-Demand Systems for Future Urban Mobility

The modelling of dynamic route guidance systems

An improved routing algorithm using chaotic neurodynamics for packet routing problems

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