Published at : 27 Dec 2021
Volume : IJtech
Vol 12, No 7 (2021)
DOI : https://doi.org/10.14716/ijtech.v12i7.5340
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 |
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
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.
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.
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).
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. 207–209
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