Application of the K-means clustering method in the organisation of passenger transport in a smart city

Authors

DOI:

https://doi.org/10.30837/2522-9818.2025.1.083

Keywords:

passenger transport; smart city; clustering analysis; K-means method; systems analysis.

Abstract

Every year, big data clustering methods are gaining popularity for decision-making on organizing passenger transportation in a smart city, ensuring the efficiency, adaptability and environmental friendliness of the transport system. Their relevance is due to the growth of data volumes, changing demand and the negative impact of transport on the environment. The object of the study is the process of clustering data sets on organizing passenger transportation. The subject of the study is the principles of studying clustering metrics when calculating the number of clusters for executing transportation schedules. The purpose of the study is to apply the K-means algorithm based on quality metrics for clustering data on organizing passenger transportation in a smart city. The following tasks are solved in the article: study of the features of clustering methods and their metrics; analysis of a large-scale heterogeneous data set on the duration of electric transport trips in an average-sized city; development of an effective algorithm for choosing a method for calculating the number of clusters based on metrics for assessing the quality of data clustering. The methods of analysis, synthesis, generalization, comparison, grouping, cluster analysis, system analysis, K-means method were used. The following results were obtained: It was established that the choice of the clustering method depends on the specifics of the task, data characteristics and the objectives of the analysis of transport flows. A complex, heterogeneous and raw data structure was revealed regarding the duration of electric transport journeys. Cluster analysis using the K-means method was due to the need for accurate data distribution between clusters. An algorithm for choosing a method for calculating the number of clusters based on metrics for assessing the quality of data clustering, including the elbow method, the silhouette method and the Kalinsky–Kharabash index, was proposed. It is recommended to use clustering to create routes with reduced waiting time, fewer transfers and compliance with passenger needs. Conclusions: the K-means method was used to analyze the duration of electric transport journeys. Data analysis revealed sections of routes with different traffic flow intensity, which depends on their location in urban areas, seasonality, etc. An algorithm for choosing a method for calculating the number of clusters based on internal metrics is proposed.

Author Biographies

Yurii Matseliukh, Lviv Polytechnic National University

PhD Student of Information Systems and Networks Department

Vasyl Lytvyn, Lviv Polytechnic National University

Doctor of Sciences (Engineering), Professor, Head of Information Systems and Networks Department

References

Список літератури

Saxena A., Prasad M., Gupta A., Bharill N., Patel O. P., Tiwari A., Er M. J., Ding W., Lin C. A review of clustering techniques and developments. Neurocomputing. 2017. No. 267. P. 664–681. DOI: 10.1016/j.neucom.2017.06.053

Isoli N., Chaczykowski M. Net energy analysis and net carbon benefits of CO2 capture and transport infrastructure for energy applications and industrial clusters. Applied Energy. 2025. No. 382, 125227 р. DOI: 10.1016/j.apenergy.2024.125227

Kowalska-Styczeń A., Bublyk M., Lytvyn V. Green innovative economy remodeling based on economic complexity. Journal of Open Innovation: Technology, Market, and Complexity. 2023. V.9 (3), 100091 р. DOI: 10.1016/j.joitmc.2023.100091

Podlesna L., Bublyk M., Grybyk I., Matseliukh Y., Burov Y., Kravets P., Lozynska O., Karpov I., Peleshchak I., Peleshchak R. Optimization model of the buses number on the route based on queueing theory in a Smart City. CEUR Workshop Proceedings. 2020. Vol. 2631. P. 502–515. URL: https://ceur-ws.org/Vol-2631/paper37.pdf (дата звернення: 01.02.2025).

Bianchini D., De Antonellis V., Garda M. A big data exploration approach to exploit in-vehicle data for smart road maintenance. Future Generation Computer Systems. 2023. No. 149. P. 701–716. DOI: 10.1016/j.future.2023.08.004

Katrenko A., Krislata I. Veres O., Oborska O., Basyuk T., Vasyliuk A., Rishnyak I., Demyanovskyi N., Meh O. Development of traffic flows and smart parking system for smart city. CEUR Workshop Proceedings. 2020. Vol. 2604. P. 730–745. URL: https://ceur-ws.org/Vol-2604/paper50.pdf (дата звернення: 01.02.2025).

Matseliukh Y., Bublyk M., Vysotska V. Development of intelligent system for visual passenger flows simulation of public transport in Smart City based on neural network. CEUR Workshop Proceedings. 2021. Vol. 2870. P. 1087–1138. URL: https://ceur-ws.org/Vol-2870/paper82.pdf (дата звернення: 01.02.2025).

Visan M., Negrea S. L., Mone F. Towards intelligent public transport systems in Smart Cities; Collaborative decisions to be made. Procedia Computer Science. 2021. No. 199. P. 1221–1228. DOI: 10.1016/j.procs.2022.01.155

Ezugwu A. E., Ikotun A. M., Oyelade O. O., Abualigah L., Agushaka J. O., Eke C. I., Akinyelu A. A. A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects. Engineering Applications of Artificial Intelligence. 2022. No. 110, 104743 р. DOI: 10.1016/j.engappai.2022.104743

Chavent M., Lechevallier Y., Briant O. DIVCLUS-T: A monothetic divisive hierarchical clustering method. Computational Statistics & Data Analysis. 2007. No. 52(2). P. 687–701. DOI: 10.1016/j.csda.2007.03.013

Celebi M. E., Kingravi H. A., Vela P. A. A comparative study of efficient initialization methods for the k-means clustering algorithm. Expert Systems With Applications. 2012. No. 40(1). P. 200–210. DOI: 10.1016/j.eswa.2012.07.021

Bublyk M., Kowalska-Styczeń A., Lytvyn V., Vysotska V. The Ukrainian economy transformation into the circular based on fuzzy-logic cluster analysis. Energies. 2021. No. 14(18). 5951 р. DOI: 10.3390/en14185951

Singh J., Singh D. A comprehensive review of clustering techniques in artificial intelligence for knowledge discovery: Taxonomy, challenges, applications and future prospects. Advanced Engineering Informatics. 2024. No. 62, 102799 р. DOI: 10.1016/j.aei.2024.102799

Yan J., Liu J., Tseng F. An evaluation system based on the self-organizing system framework of smart cities: A case study of smart transportation systems in China. Technological Forecasting and Social Change. 2020. No. 153, 119371 р. DOI: 10.1016/j.techfore.2018.07.009

Matseliukh Y., Vysotska V., Bublyk M. Intelligent system of visual simulation of passenger flows. CEUR Workshop Proceedings. 2020. Vol. 2604. P. 906–920. URL: https://ceur-ws.org/Vol-2604/paper60.pdf (дата звернення: 01.02.2025).

Prasetio E. A., Novizayanti D., Putri A. N. A. Cluster analysis of potential autonomous vehicle (AV) adopters in Indonesia’s new capital. Transportation Research Interdisciplinary Perspectives. 2024. No. 29, 101318 р. DOI: 10.1016/j.trip.2024.101318

Singh J., Singh D. A comprehensive review of clustering techniques in artificial intelligence for knowledge discovery: Taxonomy, challenges, applications and future prospects. Advanced Engineering Informatics. 2024. No. 62, 102799 р. DOI: 10.1016/j.aei.2024.102799

Liu J., Li J., Chen Y., Lian S., Zeng J., Geng M., Zheng S., Dong Y., He Y., Huang P., Zhao Z., Yan X., Hu Q., Wang L., Yang D., Zhu Z., Sun Y., Shang W., Wang D., Chen X. Multi-scale urban passenger transportation CO2 emission calculation platform for smart mobility management. Applied Energy. 2023. No. 331, 120407 р. DOI: 10.1016/j.apenergy.2022.120407

Matseliukh Y., Bublyk M., Bosak A., Naychuk-Khrushch M. The role of public transport network optimization in reducing carbon emissions. CEUR Workshop Proceedings. 2024. Vol. 3723. P. 340–364. URL: https://ceur-ws.org/Vol-3723/paper19.pdf

Bezdek J. C. Pattern Recognition with Fuzzy Objective Function Algorithms. Bon, Springer, 1981, 245 p.

Bublyk M., Vysotska V., Matseliukh Y., Mayik V., Nashkerska M. Assessing Losses of Human Capital Due to Man-Made Pollution Caused by Emergencies. CEUR Workshop Proceedings. 2020. Vol. 2805. P. 74–86. URL: https://ceur-ws.org/Vol-2805/paper6.pdf (дата звернення: 01.02.2025).

Бублик М.І., Бабій Т.І. Розвиток логістики в сучасних умовах функціонування ринку. Науковий вісник НЛТУ України. 2009. No. 19(6). C. 138–142.

EsterM., Kriegel H.-P., Sander J., Xu X. A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining. 1996. P. 226–231.

Jain A. K. Data clustering: 50 years beyond K-means. Pattern Recognition Letters. 2010. No. 31(8). P. 651–666. DOI: 10.1016/j.patrec.2009.09.011

Kohonen T. Self-Organizing Maps (3rd ed.). Bon, Springer, 2001. 554 p.

Koshtura D., Bublyk M., Matseliukh Y., Dosyn D., Chyrun L., Lozynska O., Karpov I., Peleshchak I., Maslak M., Sachenko O. Analysis of the demand for bicycle use in a smart city based on machine learning. CEUR workshop proceedings. 2020. Vol. 2631, P. 172–183. URL: https://ceur-ws.org/Vol-2631/paper13.pdf (дата звернення: 01.02.2025).

Литвин В., Бублик М., Висоцька В., Мацелюх Ю. Технологія візуальної симуляції пасажиропотоків у сфері громадського транспорту Smart City. Радіоелектроніка, iнформатика, управління. 2022. №4. С. 106–121. DOI: 10.15588/1607-3274-2021-4-10

Law A. M. Simulation Modeling and Analysis (5th ed.). Bon, McGraw-Hill, 2015. 495 p.

Nath N., Nitanai R., Manabe R., Murayama A. A global-scale review of smart city practice and research focusing on residential neighbourhoods. Habitat International. 2023. Vol. 142, P. 102963 р. DOI: 10.1016/j.habitatint.2023.102963

Sun L., Zhao J., Zhang J., Zhang F., Ye, K., Xu C. Activity-based individual travel regularity exploring with entropy-space K-means clustering using smart card data. Physica A: Statistical Mechanics and its Applications. 2024. No. 636, 129522 р. DOI: 10.1016/j.physa.2024.129522

Tibshirani R., Walther G., Hastie T. Estimating the number of clusters in a data set via the gap statistic. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 2001. No. 63(2). P. 411–423.

Subudhi S., Panigrahi S. Use of optimized Fuzzy C-Means clustering and supervised classifiers for automobile insurance fraud detection. Journal of King Saud University – Computer and Information Sciences. 2020. No. 32(5). P. 568–575. DOI: 10.1016/j.jksuci.2017.09.010

Duan Y., Liu C., Li S., Guo X., Yang C. An automatic affinity propagation clustering based on improved equilibrium optimizer and t-SNE for high-dimensional data. Information Sciences, 2023.No. 623. P. 434–454. DOI: 10.1016/j.ins.2022.12.057

Bide P., Shedge R. Improved document clustering using K-means algorithm. 2015 IEEE Int. Conf. on Electrical, Computer and Communication Technologies (ICECCT). New York: IEEE, 2015. 1048 p.

Zhong C., Miao D., Wang R., Zhou X. DIVFRP: An automatic divisive hierarchical clustering method based on the furthest reference points. Pattern Recognition Letters. 2008. No. 29(16). P. 2067–2077. DOI: 10.1016/j.patrec.2008.07.002

Elassy M., Al-Hattab M., Takruri M., Badawi S. Intelligent transportation systems for sustainable smart cities. Transportation Engineering. 2024. No. 16, 100252 р. DOI: 10.1016/j.treng.2024.100252

Zhu W. A spatial decision-making model of smart transportation and urban planning based on coupling principle and Internet of Things. Computers and Electrical Engineering. 2022. No. 102, 108222 р. DOI: 10.1016/j.compeleceng.2022.108222

Bushuev S., Inna L., Alla B., Alexander L., Khusainova M. Creating Urban Transportation Networks Grounded In the Principles of the Smart Port-City Paradigm. Procedia Computer Science. 2023. No. 231. P. 323–328. DOI: 10.1016/j.procs.2023.12.211

Balbin P. P., Barker J. C., Leung C. K., Tran M., Wall R. P., Cuzzocrea A. Predictive analytics on open big data for supporting smart transportation services. Procedia Computer Science. 2019. No. 176. P. 3009–3018. DOI: 10.1016/j.procs.2020.09.202

Khemakhem S., Krichen L. A comprehensive survey on an IoT-based smart public street lighting system application for smart cities. Franklin Open. 2024. No. 8, 100142 р. DOI: 10.1016/j.fraope.2024.100142

Vidović K., Čolić P., Vojvodić S., Blavicki A. Methodology for public transport mode detection using telecom big data sets: Case study in Croatia. Transportation Research Procedia. 2021. No. 64. P. 76–83. DOI: 10.1016/j.trpro.2022.09.010

Bublyk M., Udovychenko T., Medvid R. Concept of smart specialization in the context of the development of Ukraines economy. Economics. Ecology. Socium. 2019. Vol. 3. No 2. P. 55–61. DOI: 10.31520/2616-7107/2019.3.2-6

Bublyk M., Matseliukh Y. Small-batteries utilization analysis based on mathematical statistics methods in challenges of circular economy. CEUR workshop proceedings. 2021. Vol. 2870, P. 1594–1603. URL: https://ceur-ws.org/Vol-2870/paper118.pdf (last accessed: 01.02.2025).

Бублик М.І. Модель економічного оцінювання техногенних збитків в національному господарстві. Black Sea Scientific Journal of Academic Research. Economic Science. 2014. Vol. 12. No. 05, С. 44–50.

Dai Y., Hasanefendic S., Bossink B. A systematic literature review of the smart city transformation process: The role and interaction of stakeholders and technology. Sustainable Cities and Society. 2024. Vol. 101, 105112 р. DOI: 10.1016/j.scs.2023.105112

Cai J., Luo J., Wang S., Yang S. Feature selection in machine learning: A new perspective. Neurocomputing. 2018. No.300. P. 70–79. DOI: 10.1016/j.neucom.2017.11.077

Chung S. Applications of smart technologies in logistics and transport: A review. Transportation Research Part E: Logistics and Transportation Review. 2021. Vol. 153, 102455 р. DOI: 10.1016/j.tre.2021.102455

European Commission. Sustainable Urban Mobility Package. URL: https://ec.europa.eu/transport/themes/urban/ (last accessed: 01.02.2025).

Kidmose B. A review of smart vehicles in smart cities: Dangers, impacts, and the threat landscape. Vehicular Communications. 2025. No. 51, 100871 р. DOI: 10.1016/j.vehcom.2024.100871

Sood S. K. A scientometric analysis of quantum driven innovations in intelligent transportation systems. Engineering Applications of Artificial Intelligence. 2024. No. 138, 109258 р. DOI: 10.1016/j.engappai.2024.109258

References

Saxena, A., Prasad, M., Gupta, A., Bharill, N., Patel, O. P., Tiwari, A., Er, M. J., Ding, W., Lin, C. (2017), "A review of clustering techniques and developments", Neurocomputing, No. 267, P. 664–681. DOI: https://doi.org/10.1016/j.neucom.2017.06.053

Isoli, N., Chaczykowski, M. (2025), "Net energy analysis and net carbon benefits of CO2 capture and transport infrastructure for energy applications and industrial clusters", Applied Energy, No. 382, 125227 р. DOI: https://doi.org/10.1016/j.apenergy.2024.125227

Kowalska-Styczeń, A., Bublyk, M., Lytvyn, V. (2023), "Green innovative economy remodeling based on economic complexity", Journal of Open Innovation: Technology, Market, and Complexity. No.9(3), 100091 р. DOI: https://doi.org/10.1016/j.joitmc.2023.100091

Podlesna, L., Bublyk, M., Grybyk, I., Matseliukh, Y., Burov, Y., Kravets, P., Lozynska, O., Karpov, I., Peleshchak, I., Peleshchak, R. (2020), "Optimization model of the buses number on the route based on queueing theory in a Smart City", CEUR Workshop Proceedings, Vol. 2631, P. 502–515, available at: https://ceur-ws.org/Vol-2631/paper37.pdf (last accessed: 01.02.2025).

Bianchini, D., De Antonellis, V., Garda, M. (2023), "A big data exploration approach to exploit in-vehicle data for smart road maintenance", Future Generation Computer Systems, No. 149, P. 701-716. DOI: https://doi.org/10.1016/j.future.2023.08.004

Katrenko, A., Krislata, I. Veres, O., Oborska, O., Basyuk, T., Vasyliuk, A., Rishnyak, I., Demyanovskyi, N., Meh, O. (2020), "Development of traffic flows and smart parking system for smart city", CEUR Workshop Proceedings, Vol. 2604, P. 730–745, available at: https://ceur-ws.org/Vol-2604/paper50.pdf (last accessed: 01.02.2025).

Matseliukh, Y., Bublyk, M., Vysotska, V. (2021), "Development of intelligent system for visual passenger flows simulation of public transport in Smart City based on neural network", CEUR Workshop Proceedings, Vol. 2870, P. 1087–1138, available at: https://ceur-ws.org/Vol-2870/paper82.pdf (last accessed: 01.02.2025).

Visan, M., Negrea, S. L., Mone, F. (2021), "Towards intelligent public transport systems in Smart Cities; Collaborative decisions to be made", Procedia Computer Science, No. 199, P. 1221–1228. DOI: https://doi.org/10.1016/j.procs.2022.01.155

Ezugwu, A. E., Ikotun, A. M., Oyelade, O. O., Abualigah, L., Agushaka, J. O., Eke, C. I., Akinyelu, A. A. (2022), "A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects", Engineering Applications of Artificial Intelligence, No. 110, 104743 р. DOI: https://doi.org/10.1016/j.engappai.2022.104743

Chavent, M., Lechevallier, Y., Briant, O. (2007), "DIVCLUS-T: A monothetic divisive hierarchical clustering method", Computational Statistics & Data Analysis, No. 52 (2), P. 687-701. DOI: https://doi.org/10.1016/j.csda.2007.03.013

Celebi, M. E., Kingravi, H. A., Vela, P. A. (2012), "A comparative study of efficient initialization methods for the k-means clustering algorithm", Expert Systems With Applications, No. 40 (1), P. 200–210. DOI: https://doi.org/10.1016/j.eswa.2012.07.021

Bublyk, M., Kowalska-Styczeń, A., Lytvyn, V., Vysotska, V. (2021), "The Ukrainian economy transformation into the circular based on fuzzy-logic cluster analysis", Energies, No. 14 (18), 5951 р. DOI: https://doi.org/10.3390/en14185951

Singh, J., Singh, D. (2024), "A comprehensive review of clustering techniques in artificial intelligence for knowledge discovery: Taxonomy, challenges, applications and future prospects", Advanced Engineering Informatics, No. 62, 102799 р. DOI: https://doi.org/10.1016/j.aei.2024.102799

Yan, J., Liu, J., Tseng, F. (2020), "An evaluation system based on the self-organizing system framework of smart cities: A case study of smart transportation systems in China", Technological Forecasting and Social Change, No. 153, 119371 р. DOI: https://doi.org/10.1016/j.techfore.2018.07.009

Matseliukh, Y., Vysotska, V., Bublyk, M. (2020), "Intelligent system of visual simulation of passenger flows", CEUR Workshop Proceedings, Vol. 2604, P. 906–920, available at: https://ceur-ws.org/Vol-2604/paper60.pdf (last accessed: 01.02.2025).

Prasetio, E. A., Novizayanti, D., Putri, A. N. A. (2024), "Cluster analysis of potential autonomous vehicle (AV) adopters in Indonesia’s new capital", Transportation Research Interdisciplinary Perspectives, No. 29, 101318 р. DOI: https://doi.org/10.1016/j.trip.2024.101318

Singh, J., Singh, D. (2024), "A comprehensive review of clustering techniques in artificial intelligence for knowledge discovery: Taxonomy, challenges, applications and future prospects", Advanced Engineering Informatics, No. 62, 102799 р. DOI: https://doi.org/10.1016/j.aei.2024.102799

Liu, J., Li, J., Chen, Y., Lian, S., Zeng, J., Geng, M., Zheng, S., Dong, Y., He, Y., Huang, P., Zhao, Z., Yan, X., Hu, Q., Wang, L., Yang, D., Zhu, Z., Sun, Y., Shang, W., Wang, D., Chen, X. (2023), "Multi-scale urban passenger transportation CO2 emission calculation platform for smart mobility management", Applied Energy, No. 331, 120407 р. DOI: https://doi.org/10.1016/j.apenergy.2022.120407

Matseliukh, Y., Bublyk, M., Bosak, A., Naychuk-Khrushch, M. (2024), "The role of public transport network optimization in reducing carbon emissions", CEUR Workshop Proceedings, Vol. 3723, P. 340–364, available at: https://ceur-ws.org/Vol-3723/paper19.pdf (last accessed: 01.02.2025).

Bezdek, J. C. (1981), Pattern Recognition with Fuzzy Objective Function Algorithms, Bon, Springer, 245 p.

Bublyk, M., Vysotska, V., Matseliukh, Y., Mayik, V., Nashkerska, M. (2020), "Assessing losses of human capital due to man-made pollution caused by emergencies", CEUR Workshop Proceedings, Vol. 2805, P. 74–86, available at: https://ceur-ws.org/Vol-2805/paper6.pdf (last accessed: 01.02.2025).

Bublyk, M.I., Babiy, T.I. (2009), "Development of logistic in modern operating of market conditions", Scientific Bulletin of UNFU, No. 19 (6), P. 138–142.

Ester, M., Kriegel, H.-P., Sander, J., Xu, X. (1996), "A density-based algorithm for discovering clusters in large spatial databases with noise", Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, P. 226–231.

Jain, A. K. (2010), "Data clustering: 50 years beyond K-means", Pattern Recognition Letters, No. 31 (8), P. 651–666. DOI: https://doi.org/10.1016/j.patrec.2009.09.011

Kohonen, T. (2001), Self-Organizing Maps, 3rd ed., Springer, Bon, 554 p.

Koshtura, D., Bublyk, M., Matseliukh, Y., Dosyn, D., Chyrun, L., Lozynska, O., Karpov, I., Peleshchak, I., Maslak, M., Sachenko, O. (2020), "Analysis of the demand for bicycle use in a smart city based on machine learning", CEUR workshop proceedings, Vol. 2631, P. 172–183, available at: https://ceur-ws.org/Vol-2631/paper13.pdf (last accessed: 01.02.2025).

Lytvyn, V., Bublyk, M., Vysotska, V., Matseliukh, Y. (2022), "Visual simulation technology for passenger flows in the public transport field at smart сity", Radio Electronics, Computer Science, Control, No. 4, P. 106–121. DOI: https://doi.org/10.15588/1607-3274-2021-4-10

Law, A. M. (2015), Simulation Modeling and Analysis. 5th ed., McGraw-Hill, Bon, 495 p.

Nath, N., Nitanai, R., Manabe, R., Murayama, A. (2023), "A global-scale review of smart city practice and research focusing on residential neighbourhoods", Habitat International, Vol. 142, 102963 р. DOI: https://doi.org/10.1016/j.habitatint.2023.102963

Sun, L., Zhao, J., Zhang, J., Zhang, F., Ye, K., Xu, C. (2024), "Activity-based individual travel regularity exploring with entropy-space K-means clustering using smart card data", Physica A: Statistical Mechanics and its Applications, No. 636, 129522 р. DOI: https://doi.org/10.1016/j.physa.2024.129522

Tibshirani, R., Walther, G., Hastie, T. (2001), "Estimating the number of clusters in a data set via the gap statistic", Journal of the Royal Statistical Society: Series B (Statistical Methodology), No. 63 (2), P. 411–423.

Subudhi, S., Panigrahi, S. (2020), "Use of optimized Fuzzy C-Means clustering and supervised classifiers for automobile insurance fraud detection", Journal of King Saud University – Computer and Information Sciences, No. 32 (5), P. 568-575. DOI: https://doi.org/10.1016/j.jksuci.2017.09.010

Duan, Y., Liu, C., Li, S., Guo, X., Yang, C. (2023), "An automatic affinity propagation clustering based on improved equilibrium optimizer and t-SNE for high-dimensional data", Information Sciences, No. 623, P. 434-454. DOI: https://doi.org/10.1016/j.ins.2022.12.057

Bide, P., Shedge, R. (2015), "Improved document clustering using K-means algorithm", 2015 IEEE Int. Conf. on Electrical, Computer and Communication Technologies (ICECCT), IEEE, New York, 1048 p.

Zhong, C., Miao, D., Wang, R., Zhou, X. (2008), "DIVFRP: An automatic divisive hierarchical clustering method based on the furthest reference points", Pattern Recognition Letters, No. 29 (16), P. 2067-2077. DOI: https://doi.org/10.1016/j.patrec.2008.07.002

Elassy, M., Al-Hattab, M., Takruri, M., Badawi, S. (2024), "Intelligent transportation systems for sustainable smart cities", Transportation Engineering, No. 16, 100252 р. DOI: https://doi.org/10.1016/j.treng.2024.100252

Zhu, W. (2022), "A spatial decision-making model of smart transportation and urban planning based on coupling principle and Internet of Things", Computers and Electrical Engineering, No. 102, 108222 р. DOI: https://doi.org/10.1016/j.compeleceng.2022.108222

Bushuev, S., Inna, L., Alla, B., Alexander, L., Khusainova, M. (2023), "Creating urban transportation networks grounded in the principles of the smart port-city paradigm", Procedia Computer Science, No. 231, P. 323–328. DOI: https://doi.org/10.1016/j.procs.2023.12.211

Balbin, P. P., Barker, J. C., Leung, C. K., Tran, M., Wall, R. P., Cuzzocrea, A. (2019), "Predictive analytics on open big data for supporting smart transportation services", Procedia Computer Science, No. 176, P. 3009-3018. DOI: https://doi.org/10.1016/j.procs.2020.09.202

Khemakhem, S., Krichen, L. (2024), "A comprehensive survey on an IoT-based smart public street lighting system application for smart cities". Franklin Open, No. 8, 100142 р. DOI: https://doi.org/10.1016/j.fraope.2024.100142

Vidović, K., Čolić, P., Vojvodić, S., Blavicki, A. (2021), "Methodology for public transport mode detection using telecom big data sets: Case study in Croatia", Transportation Research Procedia, No. 64, P. 76-83. DOI: https://doi.org/10.1016/j.trpro.2022.09.010

Bublyk, M., Udovychenko, T., Medvid, R. (2019), Concept of smart specialization in the context of the development of Ukraines economy", Economics. Ecology. Socium, Vol. 3, No. 2, P. 55–61. DOI: https://doi.org/10.31520/2616-7107/2019.3.2-6

Bublyk, M., Matseliukh, Y. (2021), "Small-batteries utilization analysis based on mathematical statistics methods in challenges of circular economy", CEUR workshop proceedings, Vol. 2870, P. 1594–1603, available at: https://ceur-ws.org/Vol-2870/paper118.pdf (last accessed: 01.02.2025).

Bublyk, M.I. (2014), "Model of economic evaluation of man-made damage to the national economy", Black Sea Scientific Journal of Academic Research. Economic Science, No. 12 (05), P. 44-50.

Dai, Y., Hasanefendic, S., Bossink, B. (2024), "A systematic literature review of the smart city transformation process: The role and interaction of stakeholders and technology", Sustainable Cities and Society, Vol. 101, 105112 р. DOI: https://doi.org/10.1016/j.scs.2023.105112

Cai, J., Luo, J., Wang, S., Yang, S. (2018), "Feature selection in machine learning: A new perspective", Neurocomputing, No. 300, P. 70-79. DOI: https://doi.org/10.1016/j.neucom.2017.11.077

Chung, S. (2021), "Applications of smart technologies in logistics and transport: A review", Transportation Research Part E: Logistics and Transportation Review, No. 153, 102455 р. DOI: https://doi.org/10.1016/j.tre.2021.102455

European Commission, "Sustainable Urban Mobility Package", available at: https://ec.europa.eu/transport/themes/urban (last accessed: 01.02.2025).

Kidmose, B. (2025), "A review of smart vehicles in smart cities: Dangers, impacts, and the threat landscape. Vehicular Communications, 51, 100871 р. DOI: https://doi.org/10.1016/j.vehcom.2024.100871

Sood, S. K. (2024), "A scientometric analysis of quantum driven innovations in intelligent transportation systems", Engineering Applications of Artificial Intelligence, No. 138, 109258 р. DOI: https://doi.org/10.1016/j.engappai.2024.109258

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Published

2025-03-31

How to Cite

Matseliukh, Y., & Lytvyn, V. (2025). Application of the K-means clustering method in the organisation of passenger transport in a smart city. INNOVATIVE TECHNOLOGIES AND SCIENTIFIC SOLUTIONS FOR INDUSTRIES, (1(31), 83–101. https://doi.org/10.30837/2522-9818.2025.1.083

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No. 1(31) (2025): Innovative Technologies and Scientific Solutions for Industries

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INFORMATION TECHNOLOGY

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