(2002) 'Trac ow modeling of large-scale motorway networks using the macroscopic modeling tool METANET.', IEEE transactions on intelligent transportation systems., 3 (4). pp. 282-292. Further information on publisher's website:
Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. Abstract-This paper employs previously developed modeling, validation, and stimulation tools to address, for the first time, the realistic macroscopic simulation of a real large-scale motorway network. More specifically, the macroscopic simulator METANET, involving a second-order traffic flow model as well as network-relevant extensions, is utilized. A rigorous quantitative validation procedure is applied to individual network links, and subsequently a heuristic qualitative validation procedure is employed at a network level. The large-scale motorway network around Amsterdam, The Netherlands, is considered in this investigation. The main goal of the paper is to describe the application approach and procedures and to demonstrate the accuracy and usefulness of macroscopic modeling tools for large-scale motorway networks.
The paper presents a feedback route guidance strategy for complex, meshed traffic networks. Essential components of the strategy are simple, decentralized control laws of the bang-bang, P, or PI types that may be designed based on trial-and-error. Simulation investigations demonstrate the efficiency of the proposed strategy for two example networks under several scenarios of demand and incident conditions. Feedback route guidance, though exclusively based on measurable instantaneous travel times (no predictions, no demand nor origin–destination information are provided), is shown to equalize experienced travel times along any couple of used alternative routes in the network, and to considerably reduce travel delays compared to the no-control case.
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