Applying the AHP to Smart Mobility Services: A Case Study

Boselli, Roberto; Cesarini, Mirko; Mercorio, Fabio; Mezzanzanica, Mario

doi:10.5220/0005580003540361

Cited by 6 publications

(4 citation statements)

References 18 publications

(17 reference statements)

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“…In the scientific articles, researchers (Broniewicz & Ogrodnik, 2020;Deluka-Tibljaš, Karleuša, & Dragičević, 2013;Erdogan & Kaya, 2019;Mardani, Zavadskas, Khalifah, Jusoh, & MD Nor, 2016) examine the popularity of MCDM methods within the projects associated with transport infrastructure. Based on the literature, it can be stated that the AHP method is one of the most popular, it is used to determine the importance coefficients of smart mobility system indicators (Broniewicz & Ogrodnik, 2020;Boselli et al, 2015;Castillo & Pitfield, 2010;Deluka-Tibljaš et al, 2013, Erdogan & Kaya, 2019Farooq et al, 2019;Podvezko, Sivilevicius, & Podviezko, 2014). The AHP is a powerful tool of multicriteria decision-making developed by Saaty (1980).…”

Section: Review Of Mcdm Methods In Field Of Transportmentioning

confidence: 99%

“…Some researchers present calculated weights of smart mobility indicators and factors in their articles (Boselli, Cesarini, Mercorio, & Mezzanzanica, 2015;Orlowski & Romanowska, 2019;Wibowo & Grandhi, 2015), a system of qualitative smart mobility indicators evaluation (Debnath et al, 2014;Garau, Masala, & Pinna, 2016;Reiber & Huang, 2018).…”

Section: Review Of Indicators Of Smart City Mobility Systemmentioning

confidence: 99%

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Evaluation Criteria of Smart City Mobility System Using MCDM Method

Zapolskytė

Burinskienė

Trépanier

2020

BJRBE

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While many cities around the world qualify themselves as “smart cities”, there is no comprehensive way to evaluate to what extent they are “smart”. This article proposes a framework for comparison of the level of “smartness” of the urban mobility systems. The most relevant indicators that have the greatest impact on smart mobility systems were selected in the course of literature review. The impact of indicators on smart mobility systems is variable. Evaluating smart mobility systems, different authors distinguish between different indicators, which usually do not duplicate. The paper categorizes the indicators of the smart mobility system into five groups, called “factors”: motor travel and congestion reduction measures; pollution reduction measures; travel safety and accident reduction measures; traffic management tools and services; smart infrastructure measures. A number of indicators are attributed to each of the listed groups. A Multiple Criteria Decision-Making method, namely, the Analytic Hierarchy Process (AHP) method, has been used to evaluate the significance of the smartness level used in the research. This method bases the weighting of subjective criteria on expert judgement. Rank correlation is used to determine the consistency of expert opinions. A model has been developed to compare smart mobility systems of individual cities and their infrastructure.

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Section: Review Of Mcdm Methods In Field Of Transportmentioning

confidence: 99%

Section: Review Of Indicators Of Smart City Mobility Systemmentioning

confidence: 99%

Evaluation Criteria of Smart City Mobility System Using MCDM Method

Zapolskytė

Burinskienė

Trépanier

2020

BJRBE

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“…Debnath et al [25] propose a comprehensive and practical framework for com-paring the intelligence of urban transport systems according to 21 indicators divided into three groups. Boselli et al [23] present a set of 16 indicators divided into three groups using a multi-criteria decision-making approach to smart mobility system evaluation. Benevolo et al [11] present 52 indicators of smart mobility as part of smart city indicators.…”

Section: Review Of Smart City Mobility System Indicatorsmentioning

confidence: 99%

Smart Urban Mobility System Evaluation Model Adaptation to Vilnius, Montreal and Weimar Cities

et al. 2022

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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.

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“…Boselli et al . [46] use AHP to rank the factors for innovating a smart-mobility service in the city of Milan. A very interesting use of AHP to evaluate open data portal quality can be found in Kubler et al [47].…”

Section: Using Ahp To Weight the Indicatorsmentioning

confidence: 99%

Framework for Prioritization of Open Data Publication: An Application to Smart Cities

Prieto

Mazón

Lozano-Tello

2021

IEEE Trans. Emerg. Topics Comput.

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Public Sector Information is considered to play a fundamental role in the growth of the knowledge economy and improvements in society. Given the difficulty in publishing and maintaining all available data, due to budget constraints, institutions need to select which data to publish, giving priority to data most likely to generate social and economic impact. Priority of publication could become an even more significant problem in Smart Cities: as huge amounts of information are generated from different domains, the way data is prioritized and thus reused, could be a determining factor in promoting, among others, new and sustainable business opportunities for local entrepreneurs, and to improve citizen quality of life. However, people in charge of prioritizing which data to publish through open data portals (such as Chief Data Officers, or CDOs) do not have available any specific support in their decision-making process. In this work, a proposal of a framework for prioritization of open data publication as well as its application to Smart Cities is presented. This specific application of the framework relies on OSS (Open Source Software) indicators to help making decisions on the most relevant data to publish focused on developers and businesses operating within the Smart City context.

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Applying the AHP to Smart Mobility Services: A Case Study

Cited by 6 publications

References 18 publications

Evaluation Criteria of Smart City Mobility System Using MCDM Method

Evaluation Criteria of Smart City Mobility System Using MCDM Method

Smart Urban Mobility System Evaluation Model Adaptation to Vilnius, Montreal and Weimar Cities

Framework for Prioritization of Open Data Publication: An Application to Smart Cities

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