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Title:

Spatiotemporal Clustering of Urban Networks: Real Case Scenario in London

Accession Number:

01557019

Record Type:

Component

Availability:

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Washington, DC 20001 United States
Order URL: http://www.trb.org/Main/Blurbs/173559.aspx

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Order URL: http://worldcat.org/isbn/9780309369275

Abstract:

The problem of modeling traffic in urban areas is especially relevant because of the urgent need for managing congestion in big cities. In recent years, the macroscopic fundamental diagram (MFD) has been proposed as a macroscopic description of urban traffic. The MFD has been proved useful in the design of control strategies. However, the MFD cannot be properly defined if congestion across the city is heterogeneously distributed and if its characteristics change with time. This situation is common in large cities, and a method for identifying homogeneous areas in space and time is needed. This paper proposes a spatiotemporal clustering method with which to detect homogeneous areas over the network and over time. In each of these areas, an MFD can be defined. In particular, a segmentation method is proposed that can divide the time intervals into segments optimizing the clustering results within each of them. The algorithm is applied to data collected in the city of London, where a control system called SCOOT (split-cycle offset optimization technique) is in operation.

Monograph Accession #:

01586813

Report/Paper Numbers:

15-4344

Language:

English

Authors:

Pascale, Alessandra
Mavroeidis, Dimitrios
Lam, Hoang Thanh

Pagination:

pp 81–89

Publication Date:

2015

Serial:

Transportation Research Record: Journal of the Transportation Research Board

Issue Number: 2491
Publisher: Transportation Research Board
ISSN: 0361-1981

ISBN:

9780309369275

Media Type:

Print

Features:

Figures (8) ; Maps; References (19)

Geographic Terms:

Subject Areas:

Highways; Operations and Traffic Management; Planning and Forecasting; I72: Traffic and Transport Planning

Files:

TRIS, TRB, ATRI

Created Date:

Dec 30 2014 1:25PM

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