Abstract
To date, there is no developed and validated way to assess urban smartness. When evaluating smart city mobility systems, different authors distinguish different indicators. After analysing the evaluation indicators of the transport system presented in the scientific articles, the most relevant and influential indicators were selected. This article develops a hierarchical evaluation model for evaluating a smart city transportation system. The indicators are divided into five groups called “factors”. Several indicators are assigned to each of the listed groups. A hybrid multi-criteria decision-making (MCDM) method was used to calculate the significance of the selected indicators and to compare urban mobility systems. The applied multi-criteria evaluation methods were simple additive weighting (SAW), complex proportional assessment (COPRAS), and technique for order preference by similiarity to ideal solution (TOPSIS). The significance of factors and indicators was determined by expert evaluation methods: the analytic hierarchy process (AHP), direct, when experts evaluate the criteria as a percentage (sum of evaluations of all criteria 100%) and ranking (prioritisation). The evaluation and comparison of mobility systems were performed in two stages: when the multi-criteria evaluation is performed according to the indicators of each factor separately and when performing a comprehensive assessment of the smart mobility system according to the integrated significance of the indicators. A leading city is identified and ranked according to the smartness level. The aim of this article is to create a hierarchical evaluation model of the smart mobility systems, to compare the smartness level of Vilnius, Montreal, and Weimar mobility systems, and to create a ranking.
Highlights
With the rapid development of technology, the world’s cities and their inhabitants are becoming increasingly smart
This study aims to compare the smartness level of urban mobility systems using
The evaluation of Vilnius, Weimar, and Montreal according to the simple additive weighting (SAW), complex proportional assessment (COPRAS), and TOPSIS methods, and the best of the knowledge that was gathered from the literature, shows that the “smartest” mobility system is in Montreal, and the “least smart” is in Weimar
Summary
With the rapid development of technology, the world’s cities and their inhabitants are becoming increasingly smart. In order to implement the principles of sustainable development in the field of smart technologies and to ensure a better quality of life [1,2,3], each city must find the most rational way to adapt and use smart systems [4] in the living environment of the future. The development of urban mobility infrastructure aims to mitigate climate change while ensuring the vital movement of people, goods and services, and social equality in the choice of quality travel. The factors and indicators for assessing smart urban mobility systems are selected. The numerical values of the significance of the selected factors and indicators are calculated by expert evaluation methods: AHP [12], direct [13], and ranking [14]. Vilnius (Lithuania), Montreal (Canada), and Weimar (Germany) are compared, and the priority line is formed
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