Behavioral Model Deployment for the Transportation Projects within a Smart City Ecosystem: Cases of Germany and South Korea
Abstract
:1. Introduction
2. Literature Review: Smart City Concept Development and Related Work
3. Methodology
Scope of the Research and Applicable Methods
- This study analyzes several project efforts that support the ultimate implementation goals of smart cities to identify key parameters (both fixed and variable) to provide a framework for the deployment of smart cities
- This study develops a conceptual framework (behavioral model) for the effective planning and implementation of smart cities.
- To test and validate the suggested model, this paper offers two case studies from Germany and South Korea to illustrate the applicability of the proposed framework developed. The main scenarios, projects, and practices of the smart city initiative are considered and identified.
4. Survey: Case Study of Smart City Project Development
4.1. Case Study of South Korea
- ICT technologies,
- Improvement of civil functions;
- Environment and climate change;
- Economic growth;
- Life quality;
- Civil service;
- Governance.
- Transportation paradigm change;
- Sharing–automation–electrification;
- ICT technologies;
- Sustainability and safety;
- Social inclusiveness and quality of life.
4.2. Case Study of Germany
5. Results (Modeling)
6. Discussion
7. Conclusions (Summary)
- This study analyzed smart city transportation ecosystems in developed countries from Asian and EU regions and found common major influencing factors, such as the economy development level, investment pool, readiness for innovations, and others.
- The authors researched current smart city approaches and found that most of the strategies are focused on industrial 5.0 perspectives, and this trend has a strong impact on transportation project capacity.
- The researchers developed and tested, with two validated examples, the suggested behavioral model for smart city projects with focus on the transportation industry. Different scenarios, projects, and practices of the smart city transportation initiative were considered and identified.
- Among research findings, fixed and variable parameters of the behavioral model were investigated; therefore, the authors noticed that these parameters have a strong impact on the efficiency of transportation project implementation and depend not only on the period of the project’s implementation and flexibility of the smart city ecosystem, but also on the economic environment, global indexes, country’s innovativeness, and other parameters. Therefore, the suggested hypothesis was not only improved but also extended with additional influences.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Project Category | Project Name | Period | Location | Estimated Project Budget (KRW) | Key Innovation Factor | Project Implementer/Constructor |
---|---|---|---|---|---|---|
National pilot smart city | Sejong 5-1 living area | 2017.07~2021.12 | Hapgang-ri, Sejong-si | 1 trillion 4876 billion (in public 9500 billion, private 5376 billion) | Mobility Healthcare Education and jobs Energy and environment Governance Culture and shopping Life and safety | Ministry of Land, Infrastructure, and Transport LH K-Water |
National pilot smart city | Busan Eco Delta Smart City | 2019~2023 | Gangseo-gu, Busan | 2.2 trillion (public 1.45 trillion, private 0.76 trillion) | Space planning for New Smart Growth City Smart City 3 platform based on technology “10 innovation service” that adds value to citizens’ lives | Ministry of Land, Infrastructure, and Transport Busan K-water Busan Metropolitan Corporation |
Creation of a regional base smart city | E100 Creation of an energy-independent smart city for carbon neutrality | 2022–2024 | Gwangju Metropolitan City | 240 billion won (5:5 matching of national and local expenses). | Mobility Energy Green AI | Ministry of Land, Infrastructure and Transport Gwangju |
Creation of a regional base smart city | Citizens’ participatory carbon-neutral city development project | 2022–2024 | Changwon, Gyeongnam | 240 billion won (5:5 matching of national and local expenses) | Eco-friendly Living Space Green Mobility Circular economy Smart Transportation | Ministry of Land, Infrastructure, and Transport Changwon |
Creation of a regional base smart city | Eco-friendly, renewable energy-based new urban tourism, and leisure smart city creation | 2022–2024 | Haenam-gun, Jeollanam- | 240 billion won (5:5 matching of national and local expenses) | Regional economic foundation centered on smart service and balanced national development Eco-friendly mobility | Ministry of Land, Infrastructure, and Transport Haenam-gun |
Creation of a regional base smart city | Creation of a smart work city for the revitalization of a city with a depopulated local population | 2022–2024 | Hoengseong-gun, Gangwon-do | 240 billion won (5:5 matching of national and local expenses) | Expansion of convenient services Strengthening regional competitiveness | Ministry of Land, Infrastructure, and Transport Hoengseong-gun |
Smart City Challenge | AI-based city transportation service | 2021.04–2022 | Daegu Metropolitan City | Preliminary: 215 billion won, Main: 200 billion (50% of local expenses) | Real-time traffic navigation AI-based signal control | Ministry of Land, Infrastructure, and Transport Daegu Metropolitan City Emotion EMG KAIST Kakao Mobility MJVT Continentals |
Smart City Challenge | Realization of a citizen-participating zero carbon city | 2021.04–2023 | Chuncheon, Gangwon | Preliminary: 215 billion won, main: 200 billion (50% of local expenses) | Linking individual eco-friendly efforts and mobility sharing services with the carbon credit revenue structure Provides carbon saving points | Ministry of Land, Infrastructure, and Transport City of ChunCheon Kangwon National University NRIC KT Motovelo GATI KEVC NAMO KOLED |
Smart City Challenge | Smart emergency medical care and autonomous driving mobility service | 2021.04–2024 | Chungcheongbukdo | Preliminary: 215 billion won, main: 200 billion (50% of local expenses) | Smart Energy and Smart Life Automatically classifies patient severity, selects a transfer hospital, provides remote emergency medical guidance Increase convenience in mobility—autonomous driving shuttle | Ministry of Land, Infrastructure and Transport ChungCheongbukdo Springcloud CheongJu University Daechang Motors KB bank |
Smart City Challenge | Road safety and transportation services that are convenient for citizens | 2021.04–2025 | Pohang, Gyeongbuk | Preliminary: 215 billion won, main: 200 billion (50% of local expenses) | Internet of Things (IoT) sensors to traffic light | Ministry of Land, Infrastructure and Transport Pohang City H Energy PM grow ioCrops NeuroSense MHE NC-and |
Smart City Urban Regeneration | Jemulpo Station.J | 2022–2026 | Michuhol-gu, Incheon | 646.71 billion won (228 billion of local expense) | Smart Village Platform Smart Media Platform Creating a smart residential environment | Ministry of Land, Infrastructure and Transport Incheon |
Smart City Urban Regeneration | The center of Tamna, the gateway to Jeju, Yongdam Urban Regeneration | 2022–2025 | Jeju City, Jeju Special Self-Governing Province | 373.60 billion won (80 billion of local expense) | Jeju Northern Region Smart Regeneration-based Service Problem-solving smart village environment service Jeju Northern Region Smart Regeneration-based Service Problem-solving smart village environment service | Ministry of Land, Infrastructure and Transport Jeju |
Creative Talent Promote Project | Smart City Innovative Human Resources Development Project | 2019–2023 | Seoul University Sungkyunkwan University Yonsei University Seoul National University Pusan National University KAIST | 17.4 billion won annually | Satisfaction of high-quality manpower in industry Fostering experts Strengthening trainees’ practical capabilities Establishing a Smart City Research Foundation | KAIA (by 2023) |
Innovative Direction | Implementation Path |
---|---|
Transport network consolidation | Combining traffic lights into a special network that allows it to signal a traffic light to change color when public transport approaches to control traffic congestion. The interaction of traffic lights and personal transport through the signaling of the flow density (considering the fact that more people go to the city in the morning than leave it). Traffic lights are programmed to optimize traffic flow. |
Optimization of digital mobility (transport sector) | Holding international conferences of intelligent transport systems to discuss future strategies and share experiences. Integration between drivers and traffic lights at the level of informing about the change of color of the traffic light in advance of the intersection, or crossing in order to minimize accidents. |
Creation of a public transport ecosystem | A single online platform where not only the options for the route from A to B are shown, but there is also an ability to pay for travel and register—the focus is on rail transport. |
Digital city model | Using research centers, the creation of a digital model of the city, which allows us to see exactly where people spend their time most often and why. Those who want to build a new facility can set their parameters to the digital model program and find out where it is better, more convenient, and more profitable to build a store, hospital, etc. This system allows users to analyze the efficiency of using public transport and identify the need to reduce or increase the interval of public transport for the comfortable movement of citizens. |
Environmental friendliness of transport | Electric vehicles and electric buses to reduce environmental emissions. Germany is expected to have one million new charging stations installed by 2030. Creation of a platform through which any type of vehicle could be rented, track its condition, book it, and see reviews and information about previous users. |
Appendix B. Indicators and Their Meaning
References
- Eskelinen, J. A Global Review of Smart City Strategies, Global Review, UK. 2017. Available online: https://www.arup.com/perspectives/publications/research/section/smart-city-strategies-a-global-review (accessed on 25 November 2022).
- International Institute for Management Development. Data Shows Effects of COVID-19 and Climate Change on Citizens’ Perceptions of How ‘Smart’ Their Cities Are. Available online: https://www.imd.org/news/updates/data-shows-effects-of-covid-and-climate-change-on-citizens-perceptions-of-how-smart-their-cities-are/ (accessed on 11 November 2022).
- Vision Edge Marketing. Available online: https://visionedgemarketing.com/bic-analytics-mastery/ (accessed on 26 November 2022).
- Deakin, M.; Waer, H. From intelligent to smart cities. Intell. Build. Int. J. 2011, 3, 140–152. [Google Scholar] [CrossRef]
- Sharma, A.; Podoplelova, E.; Shapovalov, G.; Tselykh, A.; Tselykh, A. Sustainable Smart Cities: Convergence of Artificial Intelligence and Blockchain. Sustainability 2021, 13, 13076. [Google Scholar] [CrossRef]
- Prashant, S.; Zeinab, E.; Vamshi, K.M.; Junayed, P.; Dulebenets, M.A. Internet of Things for sustainable railway transportation: Past, present, and future. Clean. Logist. Supply Chain 2022, 4, 100–165. [Google Scholar]
- Kong, X.; Wu, Y.; Wang, H.; Xia, F. Edge Computing for Internet of Everything: A Survey. IEEE Internet Things J. 2022, 9, 23472–23485. [Google Scholar] [CrossRef]
- Kim, J.H. Smart city trends: A focus on 5 countries and 15 companies. Cities 2022, 123, 103–551. [Google Scholar] [CrossRef]
- World Bank Report 2020. Available online: https://www.worldbank.org/en/about/annual-report (accessed on 20 November 2022).
- US Department of Transportation. Available online: https://www.standards.its.dot.gov/ (accessed on 10 October 2022).
- Digital Bulletin. Available online: https://medium.com/digital-bulletin/smart-cities-and-the-future-of-transport-c057ce0377b6 (accessed on 1 September 2022).
- Choi, C.; Choi, J.; Kim, C.; Lee, D. The Smart City Evolution in South Korea: Findings from Big Data Analytics. J. Asian Financ. Econ. Bus. 2020, 7, 301–311. [Google Scholar] [CrossRef]
- Yun, Y.; Lee, M. Smart City 4.0 from the Perspective of Open Innovation. J. Open Innov. Technol. Mark. Complex. 2019, 5, 92. [Google Scholar] [CrossRef] [Green Version]
- Correia, D.; Teixeira, L.; João, M. Study and analysis of the relationship between smart cities and Industry 4.0: A systematic literature review. Int. J. Technol. Manag. Sustain. Dev. 2022, 21, 37–66. [Google Scholar] [CrossRef]
- Hollands, R.G. Will the Real Smart City Please Stand Up?: Intelligent, Progressive or Entrepreneurial? Routledge: London, UK, 2020; pp. 179–199. [Google Scholar]
- Alsop, T. Smart City Initiative Investment Worldwide in 2018–2023; Statista GmbH: Hamburg, Germany, 2020. [Google Scholar]
- Pettit, C.; Bakelmun, A.; Lieske, S.N.; Glackin, S.; Thomson, G.; Shearer, H.; Dia, H.; Newman, P. Planning support systems for smart cities. City Cult. Soc. 2018, 12, 13–24. [Google Scholar] [CrossRef]
- Gade; Dipak, S.; Aithal, P.S. Smart Cities Development During and Post COVID-19 Pandemic—A Predictive Analysis. Int. J. Manag. Technol. Soc. Sci. (IJMTS) 2021, 6, 189–202. [Google Scholar]
- Šulyová, D.; Kubina, M. Managing Scarce Resources in Smart Cities. Conference: Quaere 2022; Hradec Králové Publisher: Hradec Králové, Czech Republic, 2022; Volume 12. [Google Scholar]
- Google Trends. Available online: https://trends.google.com/trends/explore?date=today%205-y&gprop=news&q=Smart%20City%20in%20Korea,Smart%20city%20in%20Germany (accessed on 22 December 2022).
- Cohen, B. The 3 Generations of Smart Cities. 2015. Available online: https://www.fastcompany.com/3047795/the-3-generations-of-smart-cities (accessed on 12 November 2022).
- Qonita, M.; Giyarsih, S. Smart city assessment using the Boyd Cohen smart city wheel in Salatiga, Indonesia. GeoJournal 2022, 1–14. [Google Scholar] [CrossRef]
- Petrova-Antonova, A.; Dessislava, M.; Ilieva, S. Methodological Framework for Digital Transition and Performance Assessment of Smart Cities. In Proceedings of the 2019 4th International Conference on Smart and Sustainable Technologies (SpliTech), Split, Croatia, 18–21 June 2019; pp. 1–6. [Google Scholar]
- Eggers, D.; Skowron, J. Forces of change: Smart Cities. 2018. Available online: https://www2.deloitte.com/content/dam/insights/us/articles/4421_Forces-of-change-Smart-cities/DI_Forces-of-change-Smart-cities.pdf (accessed on 19 November 2022).
- Gutierrez-Perez, I.; Rowley, M.J.; Lyu, X.; Valadez-Graham, V.; Vallejo, D.M.; Ballesta-Illan, E.; Lopez-Atalaya, J.P.; Kremsky, I.; Caparros, E.; Corces, V.G.; et al. Ecdysone-Induced 3D Chromatin Reorganization Involves Active Enhancers Bound by Pipsqueak and Polycomb. Cell Rep. 2019, 28, 2715–2727. [Google Scholar] [CrossRef] [PubMed]
- Hwang, J. Smart City Development Outlook and Korea’s Competitiveness. In IT & Future Strategy; NIA: Seoul, Republic of Korea, 2016. [Google Scholar]
- Lee, M.; Ju, K.; Kim, A.; Kim, Y.; Yun, Y.; Jang, A.; Sohn, M.; Lee, M.; Cha, W.; Jung, H. A Policy Study for the Roadmap of the 4th Industrial Revolution; Special Committee on the 4th Industrial Revolution: Seoul, Republic of Korea, 2018. [Google Scholar]
- Jeong, H. Current Status and Implications of Smart Cities in Korea No. 792; The Industry Technology Research Centre under the Future Strategy Research Institute of Korea Development Bank (KDB): Seoul, Republic of Korea, 2021.
- Presidential Committee on The Fourth Industrial Revolution. 2018. Smart City Promotion Strategy. Available online: https://smartcity.go.kr/en/2018/01/29 (accessed on 22 December 2022).
- Ministry of Land, Infrastructure and Transport. Projects, Smart city Korea. 2022. Available online: https://smartcity.go.kr/rd/u-city-rd/ (accessed on 1 October 2022).
- Smart Cities and Inclusive Growth. Report. 2020. Available online: https://www.oecd.org/cfe/cities/OECD_Policy_Paper_Smart_Cities_and_Inclusive_Growth.pdf (accessed on 12 December 2022).
- Lee, J.-Y. Smart City Related Policy and Issues. Urban Aff. 2017, 52, 26–29. [Google Scholar]
- Kim, J.; Kim, T.; Kang, M.; Lee, J.; Choi, C.; Min, J. A Study on the Concept of Smart City and Smart City Transport. In Proceedings of the KOR-KST Conference, Chiang Mai, Thailand, 31 January–3 February 2018; pp. 384–389. [Google Scholar]
- Lee, J.; Tak, J.; Kim, S.; Hong, S. A Study on the Demands on Geospatial Information Technology in Smart City Services: Focusing on Transportation. J. Korean Soc. Geospat. Inf. Sci. 2022, 30, 13–21. [Google Scholar]
- Google Trends. Available online: https://trends.google.com/trends/explore (accessed on 22 December 2022).
- Ministry of Land, Infrastructure and Transport. U-City R&D, Smart city Korea. 2022. Available online: https://smartcity.go.kr/rd/u-city-rd/ (accessed on 1 September 2022).
- Smart City in South Korea. Market Intelligence Report 2019. Intralink Limited. Available online: https://www.intralinkgroup.com/Syndication/media/Syndication/Reports/South-Korea_Market-Intelligence-Report_Smart-Cities_Compressed.pdf (accessed on 1 September 2022).
- Kim, Y.; Hwang, H.; Choi, H. Impediments to driving smart city: A case study of South Korea. Asian J. Innovation Policy 2021, 10/2, 159–176. [Google Scholar]
- Worldometer. European Countries by Population. 2022. Available online: worldometers.info (accessed on 1 August 2022).
- Treude, M.; Schüle, R.; Haake, H. Smart Sustainable Cities—Case Study Südwestfalen Germany. Sustainability 2022, 14, 5957. [Google Scholar] [CrossRef]
- Google Trends Global 2022. Available online: https://trends.google.com/trends/yis/2022/GLOBAL/ (accessed on 22 December 2022).
- Presidential Committee on The Fourth Industrial Revolution, Smart City Promotion Strategy. EU. 2020. Available online: https://www.ellipsis.co.za/wp-content/uploads/2020/10/201023-Report-of-the-Presidential-Commission-on-the-Fourth-Industrial-Revolution.pdf (accessed on 1 December 2022).
- The German Smart City Market 2021–2026. EU Report. 2022. Available online: https://international.eco.de/topics/internet-of-things-home/study-the-smart-city-market-in-germany-2021-2026/ (accessed on 1 August 2022).
- International Trade Administration. 2022. Available online: https://www.trade.gov/ (accessed on 3 October 2022).
- Shvetsova, O.; Tanubamrungsuk, P.; Lee, S.-K. Organization Leadership in the Automobile Industry: Knowledge Management and Intellectual Capital. Open Transp. J. 2021, 15, 16–29. [Google Scholar] [CrossRef]
- Shvetsova, O.; Lee, S.-K. Living Labs in University-Industry Cooperation as a Part of Innovation Ecosystem: Case Study of South Korea. Sustain. J. 2021, 13, 5793. [Google Scholar] [CrossRef]
- EasyPark Group. Available online: https://easyparkgroup.com/news/the-smart-city-mobility-index/ (accessed on 15 November 2022).
- Global Innovation Index. Available online: https://www.globalinnovationindex.org/Home (accessed on 5 September 2022).
Current Megatrends (2022) | Expectations (2030) |
---|---|
1. Responding to climate change issues, eliminating air and water pollution | 1. Increase in energy prosumers number 2. Market addressing ethical concerns |
2. Optimizing urban transport through real-time data analysis | 3. Intelligent public transport becomes more cost-effective 4. User-centric public services |
3. Improving buildings’ energy efficiency | 5. Small- and medium-sized cities copy already working solutions in big cities |
4. Popularization of smart city initiatives among countries and areas | 6. EU regulatory frameworks addressing privacy and safety 7. Participation of private business in projects |
Smart City 1.0 | Smart City 2.0 | Smart City 3.0 | Smart City 4.0 | Smart City 5.0 | |
---|---|---|---|---|---|
Connectivity | Until emergence of PC | Wired Internet | Wireless internet, IoT | Cloud + Edge blockchain | Internet on Everything |
Human | Five senses | Neural network | Brain | Behavior (life) | Mental |
City | Sensors | Sensors + Communication | Sensors + Communication + AI | Sensors + Communication + AI + Citizen | Sensors + Communication + AI + Citizen + cyberproduction actual data |
City size (size/benefit) | Limited (optimization) | Expansion | Giantization | Self-organizing | Cyber-interaction/no boundaries |
Value | Sarnoff’s Law N | Metcalfe’s Law N2 | Reed’s Law 2n |
Year/Project Module | U-City (2008.09–2017.08) | Smart City (2017.09–Ongoing) |
---|---|---|
Concept | Focus on transportation infrastructure and public services | Resolve urban life (including transportation) issues with data of both public and private service |
Progression Party | Central government-oriented (Ministry of Land, Infrastructure, and Transport and LH) | Open governance among central government, local government, corporate entities, and citizens |
Information Sharing | One way with time difference | Both ways in real-time |
Key Projects | 1. Sangam DMC (2003–2010): RnD center, North-east IT hub, oriented by Seoul. 2. U-Paju (2004–2009): Eco-friendly, industrial complex, information-based venture businesses, oriented by Paju and the Ministry of Land, Infrastructure, and Transport. 3. Digital city (2004–2008): First digital pilot city of Korea, materialize FTTH based on the government BcN plan, oriented by the Ministry of Land, Infrastructure, and Transport. | 1. (Sejong city) 5-1 living area (2017–2021): transport mobility, healthcare education, jobs, energy and environment, governance, culture, and shopping life and safety 2. (Busan city) Eco Delta Smart City (2019–2023): Space and transportation planning for New Smart Growth City Smart City 3 platform based on technology 10 innovation service that adds value to citizens lives |
Parameter | Meaning |
---|---|
Country risk assessment (level “A”) | |
Business climate | 1 |
Initial fixed investment, authors suggest one billion dollars (currency dollars-von 2022/11/22: 1 USD = 1354.87 KRW) | 1.75 |
Global innovation index | 59.3 (+) |
Smart mobility market index | 90.19 (+) |
The volume of the GDP (billion US dollars) | 1798.53 (+) |
Parameter | Meaning |
---|---|
Country risk assessment (level “A”) | |
Business climate | 1 |
Initial fixed investment, authors suggest one billion dollars (currency dollars-euro 2022/11/22: 1 USD = 0.97447EUR) | 24.29 |
Global innovation index | 57.3 (−) |
Smart mobility market index | 97.43 (+) |
The volume of the GDP (billion US dollars) | 4223.12 (+) |
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Shvetsova, O.; Bialevich, A.; Kim, J.; Voronina, M. Behavioral Model Deployment for the Transportation Projects within a Smart City Ecosystem: Cases of Germany and South Korea. Processes 2023, 11, 48. https://doi.org/10.3390/pr11010048
Shvetsova O, Bialevich A, Kim J, Voronina M. Behavioral Model Deployment for the Transportation Projects within a Smart City Ecosystem: Cases of Germany and South Korea. Processes. 2023; 11(1):48. https://doi.org/10.3390/pr11010048
Chicago/Turabian StyleShvetsova, Olga, Anastasiya Bialevich, Jihee Kim, and Mariia Voronina. 2023. "Behavioral Model Deployment for the Transportation Projects within a Smart City Ecosystem: Cases of Germany and South Korea" Processes 11, no. 1: 48. https://doi.org/10.3390/pr11010048
APA StyleShvetsova, O., Bialevich, A., Kim, J., & Voronina, M. (2023). Behavioral Model Deployment for the Transportation Projects within a Smart City Ecosystem: Cases of Germany and South Korea. Processes, 11(1), 48. https://doi.org/10.3390/pr11010048