The Location Selection for Roundabout Construction Using Rough BWM-Rough WASPAS Approach Based on a New Rough Hamy Aggregator

Abstract

n adequately functionally located traffic infrastructure is an important factor in the mobility of people because it affects the quality of traffic, safety and efficiency of carrying out transportation activities. Locating a roundabout on an urban network is an imperative for road engineering to address traffic problems such as reduction of traffic congestion, enhancement of security and sustainability, etc. Therefore, this paper evaluates potential locations for roundabout construction using Rough BWM (Best Worst Method) and Rough WASPAS (Weighted Aggregated Sum Product Assessment) models. Determination of relative criterion weights on the basis of which the potential locations were evaluated was carried out using the Rough BWM method. In this paper, in order to enable the most precise consensus for group decision-making, a Rough Hamy aggregator has been developed. The main advantage of the Hamy mean (HM) operator is that it can capture the interrelationships among multi-input arguments and can provide DMs more options. Until now, there is no research based on HM operator for aggregating imprecise and uncertain information. The obtained indicators are described through eight alternatives. The results show that the fifth and sixth alternatives are the locations that should have a priority in the construction of roundabouts from the perspective of sustainable development, which is confirmed throughout changes of parameter k and with comparing to other methods in the sensitivity analysis.

This article belongs to the Special Issue Multi-Objective and Multi-Attribute Optimisation for Sustainable Development Decision Aiding

Document type: Article

Full document

Original document

The different versions of the original document can be found in:

• http://dspace.vgtu.lt/handle/1/3855 under the license https://creativecommons.org/licenses/by

• http://dx.doi.org/10.3390/su10082817

• http://dx.doi.org/10.3390/su10082817 under the license http://creativecommons.org/licenses/by/3.0/

• https://www.mdpi.com/2071-1050/10/8/2817/pdf under the license https://creativecommons.org/licenses/by/4.0

• http://www.mdpi.com/2071-1050/10/8/2817,

https://doaj.org/toc/2071-1050 under the license cc-by

• http://www.mdpi.com/2071-1050/10/8/2817/pdf,

http://dx.doi.org/10.3390/su10082817

• https://ideas.repec.org/a/gam/jsusta/v10y2018i8p2817-d162715.html,

https://EconPapers.repec.org/RePEc:gam:jsusta:v:10:y:2018:i:8:p:2817-:d:162715,

https://doi.org/10.3390%2fsu10082817,

https://academic.microsoft.com/#/detail/2887093654 under the license https://creativecommons.org/licenses/by/4.0/