• International Journal of Technology (IJTech)
  • Vol 12, No 7 (2021)

Evaluation of Readiness of the Urban Environment to the Introduction of the Concept of “Smart Transport” in the Subjects of the Russian Federation

Evaluation of Readiness of the Urban Environment to the Introduction of the Concept of “Smart Transport” in the Subjects of the Russian Federation

Title: Evaluation of Readiness of the Urban Environment to the Introduction of the Concept of “Smart Transport” in the Subjects of the Russian Federation
Svetlana Gutman, Polina Vorontsova , Vladislav Seredin

Corresponding email:


Cite this article as:
Gutman, S., Vorontsova , P., Seredin, V., 2021. Evaluation of Readiness of the Urban Environment to the Introduction of the Concept of “Smart Transport” in the Subjects of the Russian Federation. International Journal of Technology. Volume 12(7), pp. 1369-1378

200
Downloads
Svetlana Gutman 1Peter the Great St. Petersburg Polytechnic University, Polytechnicheskaya, 29, 195251 Saint-Petersburg, Russia
Polina Vorontsova Peter the Great St. Petersburg Polytechnic University
Vladislav Seredin Peter the Great St. Petersburg Polytechnic University
Email to Corresponding Author

Abstract
Evaluation of Readiness of the Urban Environment to the Introduction of the Concept of “Smart Transport” in the Subjects of the Russian Federation















This article expands on the problem of implementation and assessment of the readiness to develop “Smart Transport” in the subjects of the Russian Federation. The authors used qualitative and quantitative methods to achieve the goal of this study, namely, the development of an approach to assess the level of readiness of certain territories for the potential digitalization of public transport through the introduction of the concept of “Smart Transport.” As a result of the study, components of the strategic map for increasing the readiness of the analyzed subject to the implementation of the concept of “Smart Transport” in the subjects of the Russian Federation are proposed. The structure of the components is determined, and the rating scale and the basis of the indicator system for monitoring the readiness of each subject under consideration for the development of “Smart Transport” are formed, the values of which are calculated and presented in the form of an aggregate indicator of the overall assessment for eight subjects of the Russian Federation. The uniqueness of this study lies in the fact that on the basis of the analysis carried out using the fuzzy logic method, as well as the compiled system of balanced indicators, it forms an approach to the general assessment and subsequent monitoring of the level of development of the concept of “Smart Transport” in the constituent entities of the Russian Federation with proposals to increase the relevance of the aggregate indicator by introducing additional indicators that take into account modern trends and the specific features of each region under consideration and allowing making subsequent studies more qualitative. The results obtained describe in sufficient detail the current readiness of the Russian regions both for the introduction and implementation of the concept of “Smart Transport” and for the general “digitalization” of the subjects under consideration.

Digitalization; Smart city; Smart mobility; Smart transport; Transport development

Introduction

Today, “Smart Transport” (ST), along with the development of information and communication technologies (ICTs), is being introduced worldwide at different rates. Its main objectives include improving the efficiency of the transport system, minimizing damage to the environment and existing infrastructure, and improving the characteristics of cities to attract tourists and local residents who do not use public transport. Thus, it is a complex approach to the transformation of the city, both digital and socioeconomic (Dorofeeva et al., 2019; Gutman and Vorontsova, 2020).

    When planning the development of the concept of ST in the context of a dynamically changing surrounding reality, it is vital to understand and clearly identify the readiness of each specific territorial entity to the large-scale “digital” transformations, especially in the field of ground urban passenger transport (GUPT). Therefore, the goal of this study is to develop an approach to assessing the level of readiness of certain territories for the potential digitalization of public transport through the introduction of the concept of ST.

    Considering various works (Leviäkangas, 2013; Al-Nasrawi et al., 2015; Papa and Lauwers, 2015; Benevolo et al., 2016; Garau et al., 2016; Hassn et al., 2016; Mirri et al., 2016; Jeekel, 2017; Pinna et al., 2017; Docherty et al., 2018; Espinoza et al., 2018; Soriano et al., 2018; Woodhead, 2018; Sakai, 2019; Agaton et al., 2020) devoted to research in areas such as ST, “Smart Mobility,” and “Mobility as a Service” (MaaS), we can conclude that in general there are two main approaches to assessing the quality and condition of ST systems: qualitative and quantitative analysis. The methods used in the study of transport phenomena are diverse both in their fields of application and in the tools employed for calculations (Leviäkangas, 2013; Garau et al., 2016; Hassn et al., 2016; Pinna et al., 2017; Espinoza et al., 2018; Agaton et al., 2020). For example, technical analysis of passenger traffic involves the use of mathematical and statistical tools to calculate the parameters existing in real life, which, due to limitations of financial, technical, and other genesis, cannot be calculated on a large scale in real time for monitoring (Hassn et al., 2016; Kulachinskaya et al., 2017; Espinoza et al., 2018; Ivanov et al., 2020). When studying Russian research works on the topic of ST, we are convinced that most of the materials that have any calculations for assessing the development of transport systems are based on foreign studies or on Russian-language analyses of foreign methodologies and the results obtained using them (Kulachinskaya et al., 2017; Kulachinskaya et al., 2018; Dorofeeva et al., 2019; Gutman and Vorontsova, 2020; Ivanov et al., 2020). This is caused by a greater degree of study of this issue by European countries (Garau et al., 2016), a number of Asian and international companies (transport development indices in cities are provided by companies such as Cisco and EasyPark), various universities (the business school of the University of Navarre publishes a report “IESE cities in motion”), and a number of governments. 

    There is currently no unified Russian system for assessing the status and development of ST. Partially, a compilation of a system of balanced indicators of the development of ST in the subjects of the Russian Federation can be the solution to this problem. This will allow us to systematize the existing statistical data on the transport sector and related spheres of the economy, as well as to assess the level of readiness for the introduction of digital technologies, including technologies of the ST, of the subjects of the Russian Federation. Therefore, within the framework of this study, the assessment of territories (constituent entities of the Russian Federation) in this context was carried out using the fuzzy logic method, as well as a compiled system of balanced indicators, which is a fairly new approach for a general assessment and subsequent monitoring of the level of development of the concept of ST in the constituent entities of the Russian Federation.

Conclusion

As a result of the study, the main goal, which was to develop a system of indicators for assessing the level of readiness of a certain territory for the potential digitalization of public transport through the introduction of the concept of ST, was fully achieved. On the basis of the developed aggregate indicator, an assessment of the development of the concept of ST in some large cities of Russia was carried out for their subsequent comparison with each other. Based on the results obtained, their analysis and formulation of conclusions on the state of the subjects of the Russian Federation for 2019 were carried out. As a result of the calculations, it was found that among the studied cities, there are not any with a high level of development both for an aggregate indicator and for individual sub-indicators except Moscow, where the country's finances are concentrated and scientific and social initiatives are scaled up. Also, Moscow acts as an innovator in the public transport sector in Russia. The outsider among the studied regions is Sevastopol, where the lowest indicators reflect the state of the research and environmental areas of the region, as well as the underdeveloped provision of the population with telephone communications and the Internet. This is logically justified only by the nascent processes of digitalization, eco- and user-friendly trends, and a relatively new for Russia mechanism of complying with the principles of sustainable development.

    For further development of the system for monitoring the development of ST, additional highly specialized indicators have been proposed, designed to show a more accurate picture in the future calculation of the aggregate indicator. The results can not only form the basis of further research on a given topic but also serve as a benchmark for city authorities for the annual monitoring of the state of digitalization of each subject of the Russian Federation under consideration and its readiness for the introduction of innovative projects in the sphere of public life, including ST as one of the key factors of sustainable development of a region.

Acknowledgement

The research is funded by the Ministry of Science and Higher Education of the Russian Federation as part of World-class Research Center program: Advanced Digital Technologies (contract No. 075-15-2020-934 dated 17.11.2020).

References

Agaton, C., Collera A., Guno, Ch., 2020. Socio-Economic and Environmental Analyses of Sustainable Public Transport in the Philippines. Sustainability, Volume 12 (11), pp. 1–14

Al-Nasrawi, S., Adams, C., El-Zaart, A., 2015. A Conceptual Multidimensional Model for Assessing Smart Sustainable Cities. Journal of Information Systems and Technology Management, Volume 12 (3), pp. 54–558

Benevolo, C., Dameri, R.P., D’Auria, B., 2016. Smart Mobility in Smart City. Empowering Organizations. Springer. Cham, pp. 13–28

Docherty, I., Marsden, G., Anable, J., 2018. The Governance of Smart Mobility. Transportation Research Part A: Policy and Practice, Volume 115, pp. 114–125

Dorofeeva, L., Rodionov, D., Velichenkova, D., 2019. Infrastructure Potential of Creating "Smart Cities". In: Proceedings of the 2019 International SPBPU Scientific Conference on Innovations in Digital Economy

Espinoza, C., Munizaga, M., Bustos, B., Trépanier, M., 2018. Assessing the Public Transport Travel Behavior Consistency from Smart Card Data. Transportation Research Procedia, Volume 32, pp. 44–53

Garau, C., Masala, F., Pinna, F., 2016. Cagliari and Smart Urban Mobility: Analysis and Comparison. Cities, Volume 56, pp. 35–46

Gorodnova, N., Sokolov, S., 2021. Practical Implementation of the Smart City Concept in the Russian Federation: Analysis of the Current State and Prospects. Economics, Entrepreneurship and Law, Volume 11(6), pp. 1439–1456

Gutman, S., Vorontsova, P., 2020. Issues of Development of Smart Transport Assessment Indicators. In: Proceedings of the International Scientific Conference-Digital Transformation on Manufacturing, Infrastructure and Service, pp. 1–11

Hassn H.A., Ismail A., Borhan M., Syamsunur D., 2016. The Impact of Intelligent Transport System Quality: Drivers’ Acceptance Perspective. International Journal of Technology, Volume 7(4), pp. 553–561

Ivanov, M., Danchenko, M., Barabanov, A., Sokolitsyn, A., 2020. Manage Traffic Flows within the City using Smart City Technologies. In: Proceedings of the International Scientific Conference - Digital Transformation on Manufacturing, Infrastructure and Service, pp. 1–7

Jeekel, H., 2017. Social Sustainability and Smart Mobility: Exploring the Relationship. Transportation Research Procedia, Volume 25, pp. 4296–4310

Kaplan, R., Norton, D., 2014. The Balanced Scorecard: Translating Strategy into Action. (2nd ed.) corr. and amend. / trans. from Eng. by M. Pavlova. - M.: Olimp Biznes

Kulachinskaya, A., Kravchenko, V., Bezdenezhnykh, T., 2018. Organizational Mechanisms of Allocation of Subsidies for Public Transport in St. Petersburg. In: Proceedings of the 31st International Business Information Management Association Conference, pp. 4706– 4711

Kulachinskaya A., Kravchenko, K., Kuporov, K., 2017. Analysis of Tariff Policy in Urban Transport in St. Petersburg.  In: Proceedings of the 30th International Business Information Management Association Conference, Madrid, pp. 2096-2106

Leviäkangas P., 2013. Intelligent Transport Systems–Technological, Economic, System Performance and Market Views. International Journal of Technology, Volume 4(3), pp. 288–298

Mirri S., Prandi, C., Salomoni, P., Callegati, F., Melis, A., Prandini, M., 2016. A Service-oriented Approach to Crowdsensing for Accessible Smart Mobility Scenarios. Mobile Information Systems, Volume 2016, pp. 1–14

Nedoseykin, A.O., 2003. Methodological Bases of Modeling of Financial Activity Using Fuzzy-Multiple Descriptions. Russian-Language Dissertation

Papa, E., Lauwers, D., 2015. Smart Mobility: Opportunity or Threat to Innovate Places and Cities. In: The 20th International Conference on Urban Planning and Regional Development in the Information Society (REAL CORP 2015), pp. 543–550

Pinna, F., Masala, F., Garau, C., 2017. Urban Policies and Mobility Trends in Italian Smart Cities. Sustainability, Volume 9(4), pp. 1–21

Sakai, K., 2019. Maas Trends and Policy-Level Initiatives in the EU. IATSS Research, Volume 43(4), pp. 207209

Soriano, F.R., Samper-Zapater, J.J., Martinez-Dura, J.J., Cirilo-Gimeno, R.V., Plume, J.M., 2018. Smart Mobility Trends: Open Data and Other Tools. IEEE Intelligent Transportation Systems Magazine, Volume 10(2), pp. 6–16

Vedernikov, V.V., 2006. Fuzzy-Logic Modeling in the Analysis and Forecasting of Economic Phenomena and Processes: A Historical Aspect. Problems of Modern Economics, No. 1–2, pp. 446–449

Woodhead, R., 2018. Building Smarter City. International Journal of Technology, Volume 9(7), pp. 1509–1517

Zadeh, L., 1975. The Concept of a Linguistic Variable and its Application to Approximate Reasoning. Information Sciences, Volume 8(3), pp. 199–249