| 书目名称 | Feedback Control Theory for Dynamic Traffic Assignment |
| 编辑 | Pushkin Kachroo,Kaan M.A. Özbay |
| 视频video | http://file.papertrans.cn/342/341622/341622.mp4 |
| 概述 | Shows the reader how to make more accurate predictions of and control traffic flow by providing a full treatment of the feedback-based dynamic traffic assignment problem.Derives conrol laws usable in |
| 丛书名称 | Advances in Industrial Control |
| 图书封面 |  |
| 描述 | This book develops a methodology for designing feedback control laws for dynamic traffic assignment (DTA) exploiting the introduction of new sensing and information-dissemination technologies to facilitate the introduction of real-time traffic management in intelligent transportation systems. Three methods of modeling the traffic system are discussed:.partial differential equations representing a distributed-parameter setting;.continuous-time ordinary differential equations (ODEs) representing a continuous-time lumped-parameter setting; and.discreet-time ODEs representing a discrete-time lumped-parameter setting..Feedback control formulations for reaching road-user-equilibrium are presented for each setting and advantages and disadvantage of using each are addressed. The closed-loop methods described are proposed expressly to avoid the counter-productive shifting of bottlenecks from one route to another because of driver over-reaction to routing information..The second edition of .Feedback Control Theory for Dynamic Traffic Assignment. has been thoroughly updated with completely new chapters:.a review of the DTA problem and emphasizing real-time-feedback-based problems;.an up-to-da |
| 出版日期 | Book 2018Latest edition |
| 关键词 | Distributed-parameter Systems; Dynamic Traffic Assignment; Feedback Control; Godunov-based Entropy; High |
| 版次 | 2 |
| doi | https://doi.org/10.1007/978-3-319-69231-9 |
| isbn_softcover | 978-3-030-09877-3 |
| isbn_ebook | 978-3-319-69231-9Series ISSN 1430-9491 Series E-ISSN 2193-1577 |
| issn_series | 1430-9491 |
| copyright | Springer International Publishing AG, part of Springer Nature 2018 |
|Archiver|手机版|小黑屋|
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