Sustainable benchmarking of e-scooter micromobility systems: A hybrid q-rung orthopair fuzzy score function and distance measure-based ranking approach

Journal Article

2025

Published in: https://doi.org/10.1016/j.engappai.2024.109934

Micromobility is an innovative urban transport solution that can effectively tackle greenhouse gases and reduce

the use of private vehicles, especially for short-distance travel options. As a type of shared micromobility service,

the electric scooter (e-scooter) is designed to provide convenient and quick rides for short distances. The

constantly growing adoption of these emerging transportation technologies brings many factors to be considered.

In this paper, we evaluate the available e-scooters and identify the most appropriate choice with respect to

several factors. To this aim, we propose a decision support tool with the combination of modified symmetry point

of criterion (SPC), the pivot pairwise relative criteria importance assessment (PIPRECIA), and the weighted

aggregated sum product assessment (WASPAS) approaches under the setting of q-rung orthopair fuzzy (q-ROF)

sets. First, the developed method calculates the significance values of decision experts through a new q-ROFscore

function. Next, the criteria weights are determined using an incorporated objective-subjective weighting

model. It comprises a q-ROF-modified SPC tool for objective weighting and a q-ROF-PIPRECIA tool for subjective

weighting. Subsequently, we present the stepwise mathematical algorithm of a q-ROF-WASPAS approach using

q-ROF aggregation operators and criteria weighting tools. To verify the compatibility and effectiveness of the

introduced model, it is applied to evaluate and prioritize the e-scooters under the q-ROF environment. Sensitivity

analysis is made to examine the impact of varying values of weighting and utility parameters. The efficiency of

the introduced approach is confirmed by different comparative investigations.

• Publisher Name: PERGAMON-ELSEVIER SCIENCE LTD
• Publishing City: England
• Volume Number: 143
• Magazine \ Newspaper: Engineering Applications of Artificial Intelligence
• Pages: 1 to 20