2022 Fiscal Year Final Research Report
Development of Traffic Flow Theory and Control Method for Sag and Tunnel Bottlenecks on Expressways
Project/Area Number |
19K04637
|
Research Category |
Grant-in-Aid for Scientific Research (C)
|
Allocation Type | Multi-year Fund |
Section | 一般 |
Review Section |
Basic Section 22050:Civil engineering plan and transportation engineering-related
|
Research Institution | University of Tsukuba |
Principal Investigator |
Wada Kentaro 筑波大学, システム情報系, 准教授 (20706957)
|
Project Period (FY) |
2019-04-01 – 2023-03-31
|
Keywords | 交通流理論 / サグ・トンネル / Capacity Drop / プローブ / 感知器 / 交通制御 / 自動運転 |
Outline of Final Research Achievements |
Various microscopic (car-following) models have been proposed to explain congestion phenomena at sag and tunnel bottlenecks. However, due to the complexity of these models, analyzing them has been challenging, and as a result, a well-established theory has not been developed. This research aims to develop a theory based on a macroscopic traffic flow model that allows for simple modeling and to create a control method based on this theory. Specifically, we first classified the characteristic traffic congestion phenomena in terms of space and time, and then we constructed and extended a model starting from the stable phenomena. Next, the theory was verified using actual observational data. Finally, we derived driving behaviors that alleviate traffic congestion and also evaluated real-world traffic control based on the proposed theory.
|
Free Research Field |
交通工学
|
Academic Significance and Societal Importance of the Research Achievements |
本研究の最大な特色は,ミクロな視点でのモデル化が主流である単路部ボトルネック現象に対して,交通の流れの記述に力点をおくマクロな視点でモデル化を行う点である.これは,従来のアプローチからの大きな方向転換を志向するものであり,民間プローブやETC2.0,高度な画像処理技術による長時間全車両軌跡など,近年膨大に蓄積されつつある高解像度の交通流データと極めて親和性が高い.そのため,これらのデータによる検証を通した信頼性の高い理論の構築が期待できる.さらに,本研究で得られる知見は実務的な課題に直結するものであり,学術研究としてのみならず,工学的,実務的にも大きな意義がある.
|