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Purpose — In this chapter, we will review several alternative methods of collecting data from mobile phones for human mobility analysis. We propose considering cellular network location data as a useful complementary source for human mobility research and provide case studies to illustrate the advantages and disadvantages of each method.

Methodology/approach — We briefly describe cellular phone network architecture and the location data it can provide, and discuss two types of data collection: active and passive localization. Active localization is something like a personal travel diary. It provides a tool for recording positioning data on a survey sample over a long period of time. Passive localization, on the other hand, is based on phone network data that are automatically recorded for technical or billing purposes. It offers the advantage of access to very large user populations for mobility flow analysis of a broad area.

Findings — We review several alternative methods of collecting data from mobile phone for human mobility analysis to show that cellular network data, although limited in terms of location precision and recording frequency, offer two major advantages for studying human mobility. First, very large user samples – covering broad geographical areas – can be followed over a long period of time. Second, this type of data allows researchers to choose a specific data collection methodology (active or passive), depending on the objectives of their study. The big mobile phone localization datasets have provided a new impulse for the interdisciplinary research in human mobility.

Originality/value of chapter — We propose considering cellular network location data as a useful complementary source for transportation research and provide case studies to illustrate the advantages and disadvantages of each proposed method. Mobile phones have become a kind of “personal sensor” offering an ever-increasing amount of location data on mobile phone users over long time periods. These data can thus provide a framework for a comprehensive and longitudinal study of temporal dynamics, and can be used to capture ephemeral events and fluctuations in day-to-day mobility behavior offering powerful tools to transportation research, urban planning, or even real-time city monitoring.

One of the most neglected sources of energy loss is streetlights that generate too much light in areas where it is not required. Energy waste has enormous economic and environmental effects. In addition, due to the conventional manual nature of operation, streetlights are frequently seen being turned ‘ON’ during the day and ‘OFF’ in the evening, which is regrettable even in the twenty-first century. These issues require automated streetlight control in order to be resolved. This study aims to develop a novel streetlight controlling method by combining a smart transport monitoring system powered by computer vision technology with a closed circuit television (CCTV) camera that allows the light-emitting diode (LED) streetlight to automatically light up with the appropriate brightness by detecting the presence of pedestrians or vehicles and dimming the streetlight in their absence using semantic image segmentation from the CCTV video streaming. Consequently, our model distinguishes daylight and nighttime, which made it feasible to automate the process of turning the streetlight ‘ON’ and ‘OFF’ to save energy consumption costs. According to the aforementioned approach, geo-location sensor data could be utilised to make more informed streetlight management decisions. To complete the tasks, we consider training the U-net model with ResNet-34 as its backbone. Validity of the models is guaranteed with the use of assessment matrices. The suggested concept is straightforward, economical, energy-efficient, long-lasting and more resilient than conventional alternatives.

Introduction: Entrepreneurship and frugal innovation have emerged as critical drivers for addressing the Sustainable Development Goals (SDGs) in a global context. The United Nations developed the SDGs to address social, economic, and environmental challenges, ranging from poverty and inequality to climate change and sustainable economic growth. Entrepreneurship and frugal innovation offer a unique approach to achieving these goals by promoting innovation, creativity, and sustainability in business practices.

Purpose: This chapter aims to examine the role of entrepreneurship and frugal innovation in achieving SDGs in a global context. This chapter seeks to identify how entrepreneurship and frugal innovation can contribute towards realising the SDGs and how these concepts can be leveraged to create sustainable and scalable businesses that promote sustainable development.

Methodology: In order to examine how entrepreneurship and frugal innovation contribute to the worldwide achievement of the SDGs, the chapter will use a qualitative research technique. The literature review will involve the qualitative analysis of both developed and developing countries on some specific sectors like transportation, education, health sector, and financial services.

Findings: Through analysing relevant literature, qualitative research, and related examples this chapter provides insights into the challenges and opportunities associated with promoting entrepreneurship and frugal innovation for achieving the SDGs in different contexts.

Practical Implications: The chapter aims to contribute towards a better understanding of the role of entrepreneurship and frugal innovation in achieving SDGs and to provide recommendations for policymakers, entrepreneurs, and other stakeholders on supporting and promoting these concepts globally.

It is not in doubt that pollution prevention and resource efficiency projects can sometimes make good business sense for an individual enterprise. For organizations that have previously done little to address their environmental impacts, some opportunity frequently exists to lessen those impacts while raising production efficiency and keeping their basic approach to business intact. This was the experience of many businesses during the 1980s and the origins of the suggestion that the environment was a “win-win” issue for business (Walley & Whitehead, 1996). Simply updating production equipment can offer a double dividend, which is partly why so many businesses are able to claim they are getting greener while aggregate environmental conditions deteriorate (McDonough & Braungart, 2002). The unresolved issue is whether an ongoing commitment to improve environmental performance is reflected in ongoing gains in business performance. As expressed by one advocate of eco-industrial development, the issue is not about doing the same with less but rather about doing far more with far less (Cohen-Rosenthal, 2003, p. 22).

There is a long-going discussion in Russia focusing on finding new stimulus for economic growth. Being very rich with natural recourses, Russia has enjoyed extensive economic growth model for many centuries. The world is changing. Russia as any country which is to keep up with the dynamics and the quality of the world economic growth must find some new technologies and economic triggers. Green investment can be regarded as the key instrument to achieve faster economic growth and to make technological gap narrower. The chapter focuses on state policy and business practice in green investment in Russia.

The study reveals how the role of the largest cities – regional capitals in the urban settlement system – and the economy of the country changed during the period of social and economic transformations at the turn of the twentieth and twenty-first centuries. The object of the study is the largest cities with a population of over 1 million people, the administrative centers of the regions. Unlike Moscow and St. Petersburg, these are administrative centers or “capitals” of large territories that have the status of a constituent entity of the Russian Federation. The authors emphasize the special role of cities – regional capitals in post-socialist and developing countries – where there are significant and rapid changes in the distribution of functions between the center and the regions. At the same time, cities – regional capitals – are not considered as isolated objects, but as elements of a national settlement system, the leaders among Russian second-tier cities. The common features of the evolution of the largest regional capitals, the continuity of the factors of city formation, are revealed. Considerable attention is paid to assessing the consequences of the collapse of the Soviet Union for the development of the urban settlement system. It was concluded that the “compensatory” nature of the accelerated growth of the largest regional capitals in post-Soviet Russia. “Compensatory” growth is considered as a form of adaptation of the urban settlement system to the weakening of links between its essential links. A methodical approach is proposed that allows a comparative analysis of the role of the largest regional capitals and the capital of the state in the economy of the country. In the process of testing it, an assessment was made of population dynamics, as well as key economic indicators, such as the volume of products shipped, retail turnover, investments, the volume of work in the construction of major regional pages, as well as Moscow and St. Petersburg. The evaluation results allowed the classification of the largest regional capitals in terms of their role in the economic development of the country, as well as in terms of the extent of the gap between the indicators; on the one hand, the largest regional capitals, and on the other, Moscow and St. Petersburg.

Automated vehicles are likely to have significant impacts on the transport system such as increased road capacity, more productive/enjoyable time spent travelling in a car, and increased vehicle kilometres travelled. However, there is a great risk that automated driving may negatively impact the environment if adequate policies are not put in place. This chapter examines the effects of driverless vehicles and the types of policies required to attain sustainable implementation of the technology. To understand the effects on a systemic level, and to understand the needs and impacts of policies, the dynamics must be understood. Therefore, a causal loop diagram (CLD) is developed and analysed. One important insight is that the effects of driverless vehicles are mainly on the vehicular level (e.g., the reduced number of accidents per vehicle). These effects can be cancelled out on a systemic level (e.g., due to increased vehicle-kilometre travelled (VKT) that increases total number of accidents). The marginal costs of road transport are central to both freight and passenger transport. Automation will reduce marginal costs and shift the equilibrium in the transport system towards a state with higher VKT. This will lead to greater energy consumption and higher emissions. To attain sustainability goals, there might be a need to balance this reduction of marginal costs by using policy instruments. In the work, CLDs is experienced to be a useful tool to support the collaboration between experts from different fields in the dialogue about policies.

The purpose of the work is to determine conflicts that may appear in clusters.

The methodology of the research is based on combination of systemic, institutional, and evolutional approaches, including private general scientific methods of subject-object, factor, comparative, functional and structural and descriptive analysis, classification, and modeling.

Comparison of programs of development of territorial innovational clusters is conducted, as well as comparative analysis of methodological approaches to analysis of the institutional basis of regional clusters; a ranking of target directions of regional programs of development of innovational territorial clusters is compiled. The authors’ classification of conflicts of regional clusters is offered.

The performed analysis helped to determine presence of various defects, dysfunctions, and negative effects with clusters that emphasize their high susceptibility to conflicts that have to be taken into account during formation of regional cluster policy, aimed at their conflict-free functioning.