The development of science and technology parks (STPs) has become a trendy tool for promoting the economy, innovation, and technology for more than 30 years worldwide. However, STPs poses challenges for urban planners seeking a vision of sustainable urban development. These places become an object of attraction for many highly skilled workers who create daily traffic flows. The proper accessibility and provision of transport infrastructure and services become the challenge for the development of such places because the availability of services influences the choice of travel mode and the possible employees’ travel behaviour. The authors of the research aim to assess the level of development of infrastructure and transport services conducive to the sustainable mobility of science and technology park staff in Vilnius city. Changing mobility behaviour into a more sustainable way is of interest to many scientists and practitioners, so the authors think that STP staff can represent a group of educated, working-age stakeholders within the city population, who has an interest in sustainable mobility travel options and can set an example of sustainable travel. Besides, recommendations for the planning and sustainable development from the sustainable urban mobility point of view of science and technology parks and similar institutions are provided. To achieve this goal, the authors use scientific empirical and theoretical research as well as multi-criteria decision-making (MCDM) methods. The results show the link between the distance from the developed STP site to the city centre and the more sustainable mobility of workers. Therefore, it is suggested to develop STPs closer to the urban centre as it often does not require large-scale development, nor do they engage in the polluting industry. Moreover, the authors suggest the key criteria that should be considered for STP development.

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.

The continuous increase in urban population and the complexity of urban governance encourage local authorities to use technologies that support the higher quality of urban spaces and better public service delivery. Smart city projects have become more and more popular throughout the world over the past year, although the concept of smart cities is far from unambiguous. The experience of the whole world shows that cities define themselves as smart, but in each case the meaning assigned to them is different. Smart cities are an increasingly widespread urban development strategy that addresses urban issues through new technological advances, often by storing massive amounts of data from the daily activities of city residents in order to find the most effective way to use certain systems in the future. The concept of a smart city, its various meanings, features and main aspects are discussed in this article by using scientific sources. The comparison of Vilnius as a smart city with other European cities is done. Santrauka Ištisinis miesto gyventojų skaičiaus didėjimas ir miestų valdymo sudėtingumas skatina vietos valdžios institucijas taikyti technologijas, kurios palaikytų aukštesnę miesto erdvių kokybę ir geresnį viešųjų paslaugų teikimą. Per pastaruosius metus išmaniųjų miestų (angl. Smart City) projektai buvo vis populiaresni ir plačiai paplitę visame pasaulyje, nors išmaniųjų miestų sąvoka toli gražu nėra vienareikšmė. Viso pasaulio patirtis rodo, kad nors miestai apibrėžiami kaip išmanieji, tačiau kiekvienu atveju tam priskiriama reikšmė yra skirtinga. Išmanieji miestai yra vis labiau paplitusi miestų plėtros strategija, kurią taikant miesto problemos sprendžiamos pasitelkiant naują technologinę pažangą, dažnai kaupiant didžiulį duomenų kiekį, surenkamą iš miesto gyventojų kasdienės veiklos, siekiant rasti kuo efektyviausią būdą, kaip ateityje panaudoti tam tikras sistemas. Šiame straipsnyje, naudojantis mokslinės literatūros šaltiniais, aptariama išmaniojo miesto sąvoka, įvairios jos reikšmės, bruožai, pagrindiniai aspektai bei koncepcija, atliekamas Vilniaus, kaip išmaniojo miesto, palyginimas su kitais Europos miestais.

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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