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Measuring traffic congestion: an approach based on learning weighted inequality, spread and aggregation indices from comparison data

journal contribution
posted on 01.06.2018, 00:00 authored by Gleb BeliakovGleb Beliakov, Marek GagolewskiMarek Gagolewski, Simon JamesSimon James, Shannon Pace, Nicola PastorelloNicola Pastorello, Elodie Thilliez, Rajesh VasaRajesh Vasa
As cities increase in size, governments and councils face the problem of designing infrastructure and approaches to traffic management that alleviate congestion. The problem of objectively measuring congestion involves taking into account not only the volume of traffic moving throughout a network, but also the inequality or spread of this traffic over major and minor intersections. For modeling such data, we investigate the use of weighted congestion indices based on various aggregation and spread functions. We formulate the weight learning problem for comparison data and use real traffic data obtained from a medium-sized Australian city to evaluate their usefulness.

History

Journal

Applied soft computing

Volume

67

Pagination

910 - 919

Publisher

Elsevier B.V.

Location

Amsterdam, The Netherlands

ISSN

1568-4946

Language

eng

Publication classification

C Journal article; C1 Refereed article in a scholarly journal

Copyright notice

2017, Elsevier B.V.