Changes in public transport service demand under the influence of seasonal components
dc.citation.epage | 24 | |
dc.citation.issue | 1 | |
dc.citation.spage | 14 | |
dc.citation.volume | 6 | |
dc.contributor.affiliation | Lviv Polytechnic National University | |
dc.contributor.affiliation | Lviv Scientific Research Institute of Forensic Examinations | |
dc.contributor.author | Zhuk, Mykola | |
dc.contributor.author | Pivtorak, Halyna | |
dc.contributor.author | Gits, Ivanna | |
dc.contributor.author | Hits, Maryan | |
dc.coverage.placename | Львів | |
dc.coverage.placename | Lviv | |
dc.date.accessioned | 2025-07-23T06:57:32Z | |
dc.date.created | 2025-02-28 | |
dc.date.issued | 2025-02-28 | |
dc.description.abstract | У статті розглянуто вплив сезонної компоненти та інших чинників на формування попиту на послуги громадського транспорту. Чинники, які впливають на попит, поділяють на об’єктивні та суб’єктивні. До об’єктивних належать економічні, соціальні та природні аспекти, серед яких саме природні умови (температура повітря, погодні зміни, тривалість світлового дня) відіграють ключову роль у формуванні сезонних коливань. До суб’єктивних зараховують такі параметри, як вартість проїзду, рівень комфорту, доступність інформації та конкурентоспроможність громадського транспорту порівняно із іншими видами перевезень. У контексті сезонності підкреслено, що попит на пасажирські перевезення змінюється протягом року: влітку, наприклад, він знижується через канікули, відпустки та використання альтернативних засобів пересування, тоді як у холодні місяці – навпаки, зростає, зокрема через несприятливі погодні умови та необхідність пересуватись на великі відстані в умовах низьких температур. Весна і осінь характеризуються стабільним попитом на переміщення. Необхідна кількість транспортних засобів, для забезпечення якісного і комфортного проїзду змінюється відповідно до потреб користувачів транспортних послуг. Водночас варто враховувати й специфіку різних міст. У деяких містах попит на громадський транспорт може змінюватися менше навіть у літній період через високу концентрацію робочих місць та культурних подій. Основна мета дослідження полягає у визначенні зміни попиту для забезпечення стабільного та комфортного транспортного обслуговування в умовах змінної сезонної динаміки. Використання сучасних аналітичних методів дасть змогу підвищити точність прогнозування та розробити гнучкіші стратегії управління транспортною інфраструктурою. Це сприятиме підвищенню рівня задоволеності пасажирів і, відповідно, зростанню попиту на громадський транспорт у довгостроковій перспективі. | |
dc.description.abstract | The article investigates the impact of seasonal components and other factors on the formation of demand for public transport services. The influencing factors are categorized into objective and subjective groups. Objective factors encompass economic, social, and environmental dimensions, with natural conditions, such as air temperature, weather fluctuations, and daylight duration, playing a decisive role in the emergence of seasonal variations in demand. Subjective factors include variables such as fare levels, the degree of comfort, accessibility of information, and the competitiveness of public transport relative to alternative modes of transportation. From a seasonal perspective, it is emphasized that passenger transport demand exhibits annual variability. In the summer months, demand typically declines due to school holidays, employee vacations, and the increased use of alternative means of travel. Conversely, during the colder seasons, demand rises, mainly due to adverse weather conditions and the necessity to cover greater distances under low-temperature conditions. Spring and autumn are generally characterized by relatively stable mobility patterns. The required number of vehicles to ensure high-quality and comfortable transport services varies depending on user needs throughout the year. Furthermore, it is essential to account for the specific urban context. In certain cities, public transport demand demonstrates lower seasonal fluctuation, even in summer, due to a high concentration of employment centers and cultural activities. The primary objective of this study is to identify demand variations to ensure consistent and comfortable transport services within the framework of dynamic seasonal trends. The application of modern analytical methods is expected to enhance forecasting accuracy and support the development of more adaptive strategies for managing transport infrastructure. These improvements are anticipated to increase passenger satisfaction and foster long-term growth in public transport usage. | |
dc.format.extent | 14-24 | |
dc.format.pages | 11 | |
dc.identifier.citation | Changes in public transport service demand under the influence of seasonal components / Mykola Zhuk, Halyna Pivtorak, Ivanna Gits, Maryan Hits // Transport Technologies. — Lviv : Lviv Politechnic Publishing House, 2025. — Vol 6. — No 1. — P. 14–24. | |
dc.identifier.citationen | Changes in public transport service demand under the influence of seasonal components / Mykola Zhuk, Halyna Pivtorak, Ivanna Gits, Maryan Hits // Transport Technologies. — Lviv : Lviv Politechnic Publishing House, 2025. — Vol 6. — No 1. — P. 14–24. | |
dc.identifier.doi | doi.org/10.23939/tt2025.01.014 | |
dc.identifier.uri | https://ena.lpnu.ua/handle/ntb/111501 | |
dc.language.iso | en | |
dc.publisher | Видавництво Львівської політехніки | |
dc.publisher | Lviv Politechnic Publishing House | |
dc.relation.ispartof | Transport Technologies, 1 (6), 2025 | |
dc.relation.references | 1. Farahmand, Z. H., Gkiotsalitis, K., & Geurs, K. T. (2023). Predicting bus ridership based on the weather conditions using deep learning algorithms. Transportation Research Interdisciplinary Perspectives, 19, 100833. DOI: 10.1016/j.trip.2023.100833 (in English). | |
dc.relation.references | 2. Nithin, K. S., Mulangi, R. H., Sharma, R., Baishya, H., Panth, P., & Mohtashim, M. D. (2023). Visualisation and Assessment of Seasonal Variations in Bus Passenger Mobility Pattern. In International Conference on Sustainable Infrastructure: Innovation, Opportunities and Challenges (pp. 307–315). Singapore: Springer Nature Singapore. DOI: 10.1007/978-981-97-4852-5_24 (in English). | |
dc.relation.references | 3. Nissen, K. M., Becker, N., Dähne, O., Rabe, M., Scheffler, J., Solle, M., & Ulbrich, U. (2020). How does weather affect the use of public transport in Berlin?. Environmental Research Letters, 15(8), 085001. DOI: 10.1088/1748-9326/ab8ec3 (in English). | |
dc.relation.references | 4. Afonin, M., & Amrutsamanvar, R. (2023). Study of time indicators of public transport operation depending on the season of the year. Transport Technologies, 4(2), 1–11. DOI: 10.23939/tt2023.02.001 (in English). | |
dc.relation.references | 5. Huang, X., Wang, Y., Lin, P., Yu, H., & Luo, Y. (2021). Forecasting the all-weather short-term metro passenger flow based on seasonal and nonlinear LSSVM. Promet-Traffic&Transportation, 33(2), 217–231. DOI: 10.7307/ptt.v33i2.3561 (in English). | |
dc.relation.references | 6. Lubianyi, P. V., Voitovych, O., Mospan, V., & Mospan, N. (2024). Upravlinnia yakistiu transportnoho obsluhovuvannia [Quality management of transport services]. Visnyk Khersonskoho natsionalnoho tekhnichnoho universytetu [Visnyk of Kherson National Technical University], 3(90), 68–74. DOI: 10.35546/kntu20784481.2024.3.9 (in Ukrainian). | |
dc.relation.references | 7. Xiao, M., Chen, L., Feng, H., Peng, Z., & Long, Q. (2024). Sustainable and robust route planning scheme for smart city public transport based on multi-objective optimization: Digital twin model. Sustainable Energy Technologies and Assessments, 65, 103787. DOI: 10.1016/j.trc.2021.103226 (in English). | |
dc.relation.references | 8. Liu, Y., Wang, S., & Xie, B. (2019). Evaluating the effects of public transport fare policy change together with built and non-built environment features on ridership: The case in South East Queensland, Australia. Transport Policy, 76, 78–89. DOI: 10.1016/j.tranpol.2019.02.004 (in English). | |
dc.relation.references | 9. Zhuk, M., Pivtorak, H., Gits, I., & Kozak, M. (2022). Application of bayesian networks to estimate the probability of a transfer at a public transport stop. Transport technologies, 3(2), 22–32. DOI: 10.23939/tt2022.02.022 (in English). | |
dc.relation.references | 10. Litman, T. (2021). Developing Indicators for Sustainable and Livable Transport Planning. Retrieved from: https://policycommons.net/artifacts/1550496/well-measured/2240305/ (in English). | |
dc.relation.references | 11. Shoman, M., & Moreno, A. T. (2021). Exploring preferences for transportation modes in the city of Munich after the recent incorporation of ride-hailing companies. Transportation Research Record, 2675(5), 329–338. DOI: 10.1177/0361198121989726 (in English). | |
dc.relation.references | 12. Lenormand, M., Arias, J. M., San Miguel, M., & Ramasco, J. J. (2020). On the importance of trip destination for modelling individual human mobility patterns. Journal of The Royal Society Interface, 17(171), 20200673. DOI: 10.1098/rsif.2020.0673 (in English). | |
dc.relation.references | 13. Krykhtina, Y. O. (2022). State policy of development of the transport industry of Ukraine: Theory, methodology. Kharkiv (in Ukrainian). | |
dc.relation.references | 14. Pivtorak, H. V., & Bulyshyn, N. A. (2023). Otsinka mozhlyvostei rozvytku kraudshypinhu u Lvovi [Assessment of opportunities for crowdshipping development in Lviv] Scientific Notes of Tavria National University named after V. I. Vernadskyi. Series: Technical Sciences, 34(73), 281–287. DOI: 10.32782/2663-5941/2023.1/43 (in Ukrainian). | |
dc.relation.references | 15. Krueger, R., Rashidi, T. H., & Rose, J. M. (2016). Preferences for shared autonomous vehicles. Transportation research part C: Emerging technologies, 69, 343–355. DOI: 10.1016/j.trc.2016.06.015 (in English). | |
dc.relation.references | 16. Winter, K., Cats, O., Correia, G., & Van Arem, B. (2018). Performance analysis and fleet requirements of automated demand-responsive transport systems as an urban public transport service. International journal of transportation science and technology, 7(2), 151–167. DOI: 10.1016/j.ijtst.2018.04.004 (in English). | |
dc.relation.references | 17. Göransson, J., & Andersson, H. (2023). Factors that make public transport systems attractive: a review of travel preferences and travel mode choices. European Transport Research Review, 15(1), 32. DOI: 10.1186/s12544-023-00609-x. (in English). | |
dc.relation.references | 18. Banerjee, N., Morton, A., & Akartunalı, K. (2020). Passenger demand forecasting in scheduled transportation. European Journal of Operational Research, 286(3), 797–810. DOI: 10.1016/j.ejor.2019.10.032 (in English). | |
dc.relation.references | 19. Abbaspour, M., Karimi, E., Nassiri, P., Monazzam, M. R., & Taghavi, L. (2015). Hierarchal assessment of noise pollution in urban areas–A case study. Transportation Research Part D: Transport and Environment, 34, 95-103. DOI: 10.1016/j.trd.2014.10.002 (in English). | |
dc.relation.references | 20. Miller, C., & Savage, I. (2017). Does the demand response to transit fare increases vary by income?. Transport Policy, 55, 79–86. DOI: 10.1016/j.tranpol.2017.01.006 (in English). | |
dc.relation.references | 21. Zhuk, M., Pivtorak, H., Kovalyshyn, V., & Gits, I. (2024). Simulation of transfer probability in the city route network: Case study of Lviv, Ukraine. Periodica Polytechnica Transportation Engineering, 52(3), 282–291. DOI: 10.3311/PPtr.22322 (in English). | |
dc.relation.references | 22. Li, C., Bai, L., Liu, W., Yao, L., & Waller, S. T. (2020, October). Knowledge adaption for demand prediction based on multi-task memory neural network. In Proceedings of the 29th ACM international conference on information & knowledge management (pp. 715–724). DOI: 10.1145/3340531.3411965 (in English). | |
dc.relation.references | 23. Gkiotsalitis, K., & Cats, O. (2022). Optimal frequency setting of metro services in the age of COVID-19 distancing measures. Transportmetrica A: Transport Science, 18(3), 807–827. DOI: 10.1080/23249935.2021.1896593 (in English). | |
dc.relation.references | 24. Böcker, L., Dijst, M., & Faber, J. (2016). Weather, transport mode choices and emotional travel experiences. Transportation Research Part A: Policy and Practice, 94, 360–373. DOI: 10.1016/j.tra.2016.09.021 (in English). | |
dc.relation.references | 25. Hutsal, L., & Stoliar, V. (2023). Influence of seasonality on excursion demand: statistical approach. Journal of Education, Health and Sport, 49(1), 154–162. DOI: 10.12775/JEHS.2023.49.01.011 (in English). | |
dc.relation.references | 26. Zhang, J., Guo, R., & Li, W. (2024). Research on Dynamic Scheduling and Route Optimization Strategy of Flex-Route Transit Considering Travel Choice Preference of Passenger. Systems, 12(4), 138. DOI: 10.3390/systems12040138 (in English). | |
dc.relation.references | 27. Liyanage, S., Abduljabbar, R., Dia, H., & Tsai, P. W. (2022). AI-based neural network models for bus passenger demand forecasting using smart card data. Journal of Urban Management, 11(3), 365–380. DOI: 10.1016/j.jum.2022.05.002 (in English). | |
dc.relation.references | 28. Huang, Z., de Villafranca, A. E. M., & Sipetas, C. (2022). Sensing multi-modal mobility patterns: A case study of helsinki using bluetooth beacons and a mobile application. In 2022 IEEE International Conference on Big Data (Big Data) (pp. 2007–2016). DOI: 10.1109/BigData55660.2022.10020578 (in English). | |
dc.relation.references | 29. Hits, I. I. (2024). Vplyv popytu na transportni posluhy z perevezennia pasazhyriv na funktsionuvannia transportnoi systemy mist [The impact of the demand for passenger transport services on the functioning of the transport system of cities]. PhD’s thesis. Lviv: LPNU (in Ukrainian). | |
dc.relation.references | 30. Kashfi, S. A., Bunker, J. M., & Yigitcanlar, T. (2016). Modelling and analysing effects of complex seasonality and weather on an area's daily transit ridership rate. Journal of Transport Geography, 54, 310–324. DOI: 10.1016/j.jtrangeo.2016.06.018 (in English). | |
dc.relation.references | 31. Leffler, D., Burghout, W., Cats, O., & Jenelius, E. (2024). An adaptive route choice model for integrated fixed and flexible transit systems. Transportmetrica B: Transport Dynamics, 12(1), 2303047. DOI: 10.1080/21680566.2024.2303047 (in English). | |
dc.relation.references | 32. Farahmand, Z. H., Gkiotsalitis, K., & Geurs, K. T. (2021). Mobility-as-a-Service as a transport demand management tool: A case study among employees in the Netherlands. Case Studies on Transport Policy, 9(4), 1615-1629. DOI: 10.1016/j.cstp.2021.09.001 (in English). | |
dc.relation.referencesen | 1. Farahmand, Z. H., Gkiotsalitis, K., & Geurs, K. T. (2023). Predicting bus ridership based on the weather conditions using deep learning algorithms. Transportation Research Interdisciplinary Perspectives, 19, 100833. DOI: 10.1016/j.trip.2023.100833 (in English). | |
dc.relation.referencesen | 2. Nithin, K. S., Mulangi, R. H., Sharma, R., Baishya, H., Panth, P., & Mohtashim, M. D. (2023). Visualisation and Assessment of Seasonal Variations in Bus Passenger Mobility Pattern. In International Conference on Sustainable Infrastructure: Innovation, Opportunities and Challenges (pp. 307–315). Singapore: Springer Nature Singapore. DOI: 10.1007/978-981-97-4852-5_24 (in English). | |
dc.relation.referencesen | 3. Nissen, K. M., Becker, N., Dähne, O., Rabe, M., Scheffler, J., Solle, M., & Ulbrich, U. (2020). How does weather affect the use of public transport in Berlin?. Environmental Research Letters, 15(8), 085001. DOI: 10.1088/1748-9326/ab8ec3 (in English). | |
dc.relation.referencesen | 4. Afonin, M., & Amrutsamanvar, R. (2023). Study of time indicators of public transport operation depending on the season of the year. Transport Technologies, 4(2), 1–11. DOI: 10.23939/tt2023.02.001 (in English). | |
dc.relation.referencesen | 5. Huang, X., Wang, Y., Lin, P., Yu, H., & Luo, Y. (2021). Forecasting the all-weather short-term metro passenger flow based on seasonal and nonlinear LSSVM. Promet-Traffic&Transportation, 33(2), 217–231. DOI: 10.7307/ptt.v33i2.3561 (in English). | |
dc.relation.referencesen | 6. Lubianyi, P. V., Voitovych, O., Mospan, V., & Mospan, N. (2024). Upravlinnia yakistiu transportnoho obsluhovuvannia [Quality management of transport services]. Visnyk Khersonskoho natsionalnoho tekhnichnoho universytetu [Visnyk of Kherson National Technical University], 3(90), 68–74. DOI: 10.35546/kntu20784481.2024.3.9 (in Ukrainian). | |
dc.relation.referencesen | 7. Xiao, M., Chen, L., Feng, H., Peng, Z., & Long, Q. (2024). Sustainable and robust route planning scheme for smart city public transport based on multi-objective optimization: Digital twin model. Sustainable Energy Technologies and Assessments, 65, 103787. DOI: 10.1016/j.trc.2021.103226 (in English). | |
dc.relation.referencesen | 8. Liu, Y., Wang, S., & Xie, B. (2019). Evaluating the effects of public transport fare policy change together with built and non-built environment features on ridership: The case in South East Queensland, Australia. Transport Policy, 76, 78–89. DOI: 10.1016/j.tranpol.2019.02.004 (in English). | |
dc.relation.referencesen | 9. Zhuk, M., Pivtorak, H., Gits, I., & Kozak, M. (2022). Application of bayesian networks to estimate the probability of a transfer at a public transport stop. Transport technologies, 3(2), 22–32. DOI: 10.23939/tt2022.02.022 (in English). | |
dc.relation.referencesen | 10. Litman, T. (2021). Developing Indicators for Sustainable and Livable Transport Planning. Retrieved from: https://policycommons.net/artifacts/1550496/well-measured/2240305/ (in English). | |
dc.relation.referencesen | 11. Shoman, M., & Moreno, A. T. (2021). Exploring preferences for transportation modes in the city of Munich after the recent incorporation of ride-hailing companies. Transportation Research Record, 2675(5), 329–338. DOI: 10.1177/0361198121989726 (in English). | |
dc.relation.referencesen | 12. Lenormand, M., Arias, J. M., San Miguel, M., & Ramasco, J. J. (2020). On the importance of trip destination for modelling individual human mobility patterns. Journal of The Royal Society Interface, 17(171), 20200673. DOI: 10.1098/rsif.2020.0673 (in English). | |
dc.relation.referencesen | 13. Krykhtina, Y. O. (2022). State policy of development of the transport industry of Ukraine: Theory, methodology. Kharkiv (in Ukrainian). | |
dc.relation.referencesen | 14. Pivtorak, H. V., & Bulyshyn, N. A. (2023). Otsinka mozhlyvostei rozvytku kraudshypinhu u Lvovi [Assessment of opportunities for crowdshipping development in Lviv] Scientific Notes of Tavria National University named after V. I. Vernadskyi. Series: Technical Sciences, 34(73), 281–287. DOI: 10.32782/2663-5941/2023.1/43 (in Ukrainian). | |
dc.relation.referencesen | 15. Krueger, R., Rashidi, T. H., & Rose, J. M. (2016). Preferences for shared autonomous vehicles. Transportation research part C: Emerging technologies, 69, 343–355. DOI: 10.1016/j.trc.2016.06.015 (in English). | |
dc.relation.referencesen | 16. Winter, K., Cats, O., Correia, G., & Van Arem, B. (2018). Performance analysis and fleet requirements of automated demand-responsive transport systems as an urban public transport service. International journal of transportation science and technology, 7(2), 151–167. DOI: 10.1016/j.ijtst.2018.04.004 (in English). | |
dc.relation.referencesen | 17. Göransson, J., & Andersson, H. (2023). Factors that make public transport systems attractive: a review of travel preferences and travel mode choices. European Transport Research Review, 15(1), 32. DOI: 10.1186/s12544-023-00609-x. (in English). | |
dc.relation.referencesen | 18. Banerjee, N., Morton, A., & Akartunalı, K. (2020). Passenger demand forecasting in scheduled transportation. European Journal of Operational Research, 286(3), 797–810. DOI: 10.1016/j.ejor.2019.10.032 (in English). | |
dc.relation.referencesen | 19. Abbaspour, M., Karimi, E., Nassiri, P., Monazzam, M. R., & Taghavi, L. (2015). Hierarchal assessment of noise pollution in urban areas–A case study. Transportation Research Part D: Transport and Environment, 34, 95-103. DOI: 10.1016/j.trd.2014.10.002 (in English). | |
dc.relation.referencesen | 20. Miller, C., & Savage, I. (2017). Does the demand response to transit fare increases vary by income?. Transport Policy, 55, 79–86. DOI: 10.1016/j.tranpol.2017.01.006 (in English). | |
dc.relation.referencesen | 21. Zhuk, M., Pivtorak, H., Kovalyshyn, V., & Gits, I. (2024). Simulation of transfer probability in the city route network: Case study of Lviv, Ukraine. Periodica Polytechnica Transportation Engineering, 52(3), 282–291. DOI: 10.3311/PPtr.22322 (in English). | |
dc.relation.referencesen | 22. Li, C., Bai, L., Liu, W., Yao, L., & Waller, S. T. (2020, October). Knowledge adaption for demand prediction based on multi-task memory neural network. In Proceedings of the 29th ACM international conference on information & knowledge management (pp. 715–724). DOI: 10.1145/3340531.3411965 (in English). | |
dc.relation.referencesen | 23. Gkiotsalitis, K., & Cats, O. (2022). Optimal frequency setting of metro services in the age of COVID-19 distancing measures. Transportmetrica A: Transport Science, 18(3), 807–827. DOI: 10.1080/23249935.2021.1896593 (in English). | |
dc.relation.referencesen | 24. Böcker, L., Dijst, M., & Faber, J. (2016). Weather, transport mode choices and emotional travel experiences. Transportation Research Part A: Policy and Practice, 94, 360–373. DOI: 10.1016/j.tra.2016.09.021 (in English). | |
dc.relation.referencesen | 25. Hutsal, L., & Stoliar, V. (2023). Influence of seasonality on excursion demand: statistical approach. Journal of Education, Health and Sport, 49(1), 154–162. DOI: 10.12775/JEHS.2023.49.01.011 (in English). | |
dc.relation.referencesen | 26. Zhang, J., Guo, R., & Li, W. (2024). Research on Dynamic Scheduling and Route Optimization Strategy of Flex-Route Transit Considering Travel Choice Preference of Passenger. Systems, 12(4), 138. DOI: 10.3390/systems12040138 (in English). | |
dc.relation.referencesen | 27. Liyanage, S., Abduljabbar, R., Dia, H., & Tsai, P. W. (2022). AI-based neural network models for bus passenger demand forecasting using smart card data. Journal of Urban Management, 11(3), 365–380. DOI: 10.1016/j.jum.2022.05.002 (in English). | |
dc.relation.referencesen | 28. Huang, Z., de Villafranca, A. E. M., & Sipetas, C. (2022). Sensing multi-modal mobility patterns: A case study of helsinki using bluetooth beacons and a mobile application. In 2022 IEEE International Conference on Big Data (Big Data) (pp. 2007–2016). DOI: 10.1109/BigData55660.2022.10020578 (in English). | |
dc.relation.referencesen | 29. Hits, I. I. (2024). Vplyv popytu na transportni posluhy z perevezennia pasazhyriv na funktsionuvannia transportnoi systemy mist [The impact of the demand for passenger transport services on the functioning of the transport system of cities]. PhD’s thesis. Lviv: LPNU (in Ukrainian). | |
dc.relation.referencesen | 30. Kashfi, S. A., Bunker, J. M., & Yigitcanlar, T. (2016). Modelling and analysing effects of complex seasonality and weather on an area's daily transit ridership rate. Journal of Transport Geography, 54, 310–324. DOI: 10.1016/j.jtrangeo.2016.06.018 (in English). | |
dc.relation.referencesen | 31. Leffler, D., Burghout, W., Cats, O., & Jenelius, E. (2024). An adaptive route choice model for integrated fixed and flexible transit systems. Transportmetrica B: Transport Dynamics, 12(1), 2303047. DOI: 10.1080/21680566.2024.2303047 (in English). | |
dc.relation.referencesen | 32. Farahmand, Z. H., Gkiotsalitis, K., & Geurs, K. T. (2021). Mobility-as-a-Service as a transport demand management tool: A case study among employees in the Netherlands. Case Studies on Transport Policy, 9(4), 1615-1629. DOI: 10.1016/j.cstp.2021.09.001 (in English). | |
dc.relation.uri | https://policycommons.net/artifacts/1550496/well-measured/2240305/ | |
dc.rights.holder | © Національний університет “Львівська політехніка”, 2025 | |
dc.rights.holder | © Zhuk M., Pivtorak H., Gits I., Hits M., 2025 | |
dc.subject | громадський транспорт | |
dc.subject | попит на пасажирські перевезення | |
dc.subject | умови функціонування маршрутної мережі | |
dc.subject | сезонні компоненти | |
dc.subject | PTV Visum | |
dc.subject | public transport | |
dc.subject | passenger transport demand | |
dc.subject | conditions of the route network functioning | |
dc.subject | seasonal components | |
dc.subject | PTV Visum | |
dc.title | Changes in public transport service demand under the influence of seasonal components | |
dc.title.alternative | Зміна попиту на послуги громадського транспорту під впливом сезонної компоненти | |
dc.type | Article |
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