Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/6778
DC FieldValueLanguage
dc.contributor.authorDr. KWOK Pak Ki, Alexen_US
dc.contributor.authorChau, D. W. H.en_US
dc.contributor.authorLau, Y. K. Henryen_US
dc.date.accessioned2021-11-29T01:45:04Z-
dc.date.available2021-11-29T01:45:04Z-
dc.date.issued2014-
dc.identifier.citationIn Bramer, M. & Petridis, M. (eds.) (2014). Research and development in intelligent systems XXXI (pp. 339-344).en_US
dc.identifier.urihttp://hdl.handle.net/20.500.11861/6778-
dc.description.abstractWith the increasing number of vehicles on roads, choosing a proper Road Junction Control (RJC) Method has become an important decision for reducing traffic congestion and cost. However, the public awareness of environmental sustainability and diverse voices from different stakeholders make such decision a knotty one. In this paper, an artificial intelligent decision-making framework using Hierarchical Half Fuzzy TOPSIS (HHF-TOPSIS) is proposed for RJC method selection. Compared with the existing qualitative comparison method suggested in the Design Manual for Roads and Bridges, this method can provide a more efficient and objective approach to reach the best compromise against all relevant objectives.en_US
dc.language.isoenen_US
dc.titleA study on road junction control method selection using an artificial intelligent multi-criteria decision making frameworken_US
dc.typeConference Paperen_US
dc.relation.conferenceInternational Conference on Innovative Techniques and Applications of Artificial Intelligenceen_US
crisitem.author.deptDepartment of Applied Data Science-
item.fulltextNo Fulltext-
Appears in Collections:Applied Data Science - Publication
Show simple item record

Page view(s)

82
Last Week
3
Last month
checked on Apr 3, 2025

Google ScholarTM

Impact Indices

PlumX

Metrics


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.