Forecasting the mobility parameters of the inhabitants of suburban areas

dc.citation.epage12
dc.citation.issue1
dc.citation.spage1
dc.contributor.affiliationNational University of Water and Environmental Engineering
dc.contributor.authorKrystopchuk, Mykhailo
dc.coverage.placenameЛьвів
dc.coverage.placenameLviv
dc.date.accessioned2021-12-21T11:11:49Z
dc.date.available2021-12-21T11:11:49Z
dc.date.created2021-03-10
dc.date.issued2021-03-10
dc.description.abstractПотенційна мобільність, що відповідає вимогам населення щодо пересування, визначається відповідно до біологічних і соціальних потреб, соціально-економічних характеристик, виробничої необхідності та культурних потреб. Через багатофакторний характер і складність взаємозв’язків неможливо визначити потенційну мобільність методом простого розрахунку. Доцільність різних цільових переміщень, залежно від їх відстані, сільське населення оцінює по-різному. Кожне сільське поселення розташоване серед багатьох інших сільських та міських поселень з індивідуальним кількісним та якісним набором соціального, культурного і промислового потенціалу. Завдяки розвиненій дорожній мережі та системі громадського транспорту населення вибирає центр тяжіння з обмеженнями, накладеними цією транспортною системою, і ґрунтується на суб’єктивних оцінках щодо якості обслуговування. На розподіл пересувань жителів у приміських зонах впливає розмір поселення, відстань, мета пересування, тобто такі самі чинники, як і щодо переміщення сільських жителів до міст. Відмінність полягає у тому, що радіус розподілу міських жителів набагато менший. Отже, зона інтенсивних та регулярних рухів у циклі робочого дня охоплює лише найближчі до міст сільські райони радіусом 15 км. У вихідні дні через гостьові поїздки радіус цієї зони розширюється приблизно в 1,5–2 рази. На основі розподілу поїздок можна отримати зони розсіювання початкової та кінцевої точок пересувань. Оскільки щільність розсіювання варіює відносно до населених пунктів, то за їх множинами можна виділити територіальні одиниці, які становитимуть зону обслуговування. Результати досліджень можуть бути частиною комплексних досліджень із визначення щільності транспортних зв’язків, центрів зародження та погашення пасажирських потоків для побудови математичних моделей ефективної роботи системи пасажирського транспорту.
dc.description.abstractPotential mobility that meets the requirements of population displacement is determined following the biological and social needs, socio-economic characteristics, production necessity, and cultural needs. Because of the multifactor character and complexity of relationships, it is impossible to determine the potential mobility by a calculation method. The feasibility of different target movements, depending on their distance, is regarded by rural populations differently. Each rural settlement is located among many other rural and urban settlements with an individual quantitative and qualitative set of social, cultural, and industrial potential. With the developed road network and public transport system, the population selects the center of gravity with the limitations imposed by this transport system and is based on subjective considerations about the quality of service. The distribution of urban residents’ movements to the rural areas is affected by the size of the city, movement distance, movement purpose, i.e. the same factors as rural residents’ movement to cities. The difference is that the radius of urban residents’ movements distribution is much smaller. Thus, the zone of intensive and regular movements in the working day cycle covers only nearest to cities rural area with a radius of 15 km. On weekends, due to guest visits and holiday trips, the radius of this zone extends approximately 1,5–2 times. Based on the links distribution, the scatter band of the initial and final points of movement can be obtained. Since the density of scattering varies with respect to settlements, then we can allocate the territorial units that will make service zone on their sets. Research results can be an integral part of comprehensive studies of determining the transport links density, hubs of passenger flows’ origin, and suppression to construct mathematical models of the most efficient passenger transport system operation.
dc.format.extent1-12
dc.format.pages12
dc.identifier.citationKrystopchuk M. Forecasting the mobility parameters of the inhabitants of suburban areas / Mykhailo Krystopchuk // Transport Technologies. — Lviv : Lviv Politechnic Publishing House, 2021. — Vol 2. — No 1. — P. 1–12.
dc.identifier.citationenKrystopchuk M. Forecasting the mobility parameters of the inhabitants of suburban areas / Mykhailo Krystopchuk // Transport Technologies. — Lviv : Lviv Politechnic Publishing House, 2021. — Vol 2. — No 1. — P. 1–12.
dc.identifier.doidoi.org/10.23939/tt2021.01.001
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/56535
dc.language.isoen
dc.publisherВидавництво Львівської політехніки
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofTransport Technologies, 1 (2), 2021
dc.relation.references1. European Comission. (2016). Horizon 2020 – Smart, Green and Integrated transport. Important Notice on the Second Horizon 2020 Work Programme, 2017 (July 2016), 129. (in English)
dc.relation.references2. Li, Y. & Voege, T. (2017) Mobility as a Service (MaaS): Challenges of Implementation and Policy Required. Journal of Transportation Technologies, Volume 7, 95–106. doi: 10.4236/jtts.2017.72007. (in English)
dc.relation.references3. Yatskiv, I., Pticina, I., & Savrasovs, M. (2012). Urban Public Transport System's Reliability Estimation Using Microscopic Simulation, Transport and Telecommunication Journal, Volume 13(3), 219–228. doi: https://doi.org/10.2478/v10244-012-0018-4 (in English)
dc.relation.references4. Gidebo, F., & Szpytko, J. (2019). Reliability Assessment of the Transport System, Addis Ababa Case Study, Journal of KONBiN, Volume 49(4), 27–36. doi: https://doi.org/10.2478/jok-2019-0073. (in English)
dc.relation.references5. Khitrov, I., & Tkhoruk, Y. (2020). Formation and Distribution Flows of External Transport in the City. In Reliability and Statistics in Transportation and Communication: Selected Papers from the 19th International Conference on Reliability and Statistics in Transportation and Communication, RelStat’19, 16–19 October 2019, Riga, Latvia (Vol. 117, p. 141). Springer Nature. (in English)
dc.relation.references6. Tkhoruk Y., Kucher O., Holotiuk M., Krystopchuk M. & Tson O. (2019) Modeling of assessment of reliability transport systems. Proceedings of ICCPT 2019 (Tern., May 28–29, 2019), pp. 204–210. (in English)
dc.relation.references7. Dumbliauskas, V. (2019). Development and application of tour-based travel demand model for planning of urban transport networks (Doctoral dissertation, VGTU leidykla “Technika “) (in English)
dc.relation.references8. Raux, C. (2003). A systems dynamics model for the urban travel system. In AET. European Transport Conference 2003–ETC 2003, 8–10 october 2003, Strasbourg (pp. 32-p). AET. (in English)
dc.relation.references9. Ortúzar, J. de D. & Willumsen, L. G. (2011) “Modelling Transport”. Fourth Edition, John Wiley and Sons, Chichester. (in English)
dc.relation.references10. Balcombe, R., Mackett, R., Paulley, N., Preston, J., Shires, J., Titheridge, H. & et al. (2004). The demand for public transport: a practical guide. (in English)
dc.relation.references11. Bhat, C. R., & Koppelman, F. S. (1999). Activity-based modeling of travel demand. In Handbook of transportation Science (pp. 35–61). Springer, Boston, MA. (in English)
dc.relation.references12. Dianat, L., Habib, K. N., & Miller, E. J. (2020). Modeling and forecasting daily non-work/school activity patterns in an activity-based model using skeleton schedule constraints. Transportation research part A: policy and practice, 133, 337–352. doi:10.1016/j.tra.2020.01.017. (in English)
dc.relation.references13. Andersson, A., Hiselius, L. W., & Adell, E. (2018). Promoting sustainable travel behaviour through the use of smartphone applications: A review and development of a conceptual model. Travel behaviour and society, 11, 52–61. doi:10.1016/j.tbs.2017.12.008. (in English)
dc.relation.references14. Dolya, V. K., Gricyuk, P. M., Kristopchuk, M. E. (2006) “Investigation of the transport network of the region by the method of constructing the population density function”, Journal of Municipal Services of Cities, Tekhnіka Publisher, Volume 69, 205–211. (in Ukrainian)
dc.relation.references15. Sivakumar, A. (2007). Modelling transport: a synthesis of transport modelling methodologies. Imperial College of London. (in English)
dc.relation.references16. Hunt, J. D., & Simmonds, D. C. (1993). Theory and application of an integrated land-use and transport modelling framework. Environment and Planning B: Planning and Design, Volume 20(2), 221–244. (in English)
dc.relation.references17. Hunt, J. D., Kriger, D. S., & Miller, E. J. (2005). Current operational urban land–use–transport modelling frameworks: A review. Transport reviews, Volume 25(3), 329–376. (in English)
dc.relation.references18. Krystopchuk, M. Ye. (2012) “Social and economic efficiency of passenger transportation system suburban communication”, Monograph. NUWEE, Rivne, Ukraine. (in Ukrainian).
dc.relation.references19. Profillidis, V. A., & Botzoris, G. N. (2018). Modeling of transport demand: Analyzing, calculating, and forecasting transport demand. Elsevier. (in English)
dc.relation.references20. Antwi, T., Quaye-Ballard, J. A., Arko-Adjei, A., Osei-wusu, W., & Quaye-Ballard, N. L. (2020). Comparing Spatial Accessibility and Travel Time Prediction to Commercial Centres by Private and Public Transport: A Case Study of Oforikrom District. Journal of Advanced Transportation, 2020. doi:10.1155/2020/8319089. (in English)
dc.relation.referencesen1. European Comission. (2016). Horizon 2020 – Smart, Green and Integrated transport. Important Notice on the Second Horizon 2020 Work Programme, 2017 (July 2016), 129. (in English)
dc.relation.referencesen2. Li, Y. & Voege, T. (2017) Mobility as a Service (MaaS): Challenges of Implementation and Policy Required. Journal of Transportation Technologies, Volume 7, 95–106. doi: 10.4236/jtts.2017.72007. (in English)
dc.relation.referencesen3. Yatskiv, I., Pticina, I., & Savrasovs, M. (2012). Urban Public Transport System's Reliability Estimation Using Microscopic Simulation, Transport and Telecommunication Journal, Volume 13(3), 219–228. doi: https://doi.org/10.2478/v10244-012-0018-4 (in English)
dc.relation.referencesen4. Gidebo, F., & Szpytko, J. (2019). Reliability Assessment of the Transport System, Addis Ababa Case Study, Journal of KONBiN, Volume 49(4), 27–36. doi: https://doi.org/10.2478/jok-2019-0073. (in English)
dc.relation.referencesen5. Khitrov, I., & Tkhoruk, Y. (2020). Formation and Distribution Flows of External Transport in the City. In Reliability and Statistics in Transportation and Communication: Selected Papers from the 19th International Conference on Reliability and Statistics in Transportation and Communication, RelStat’19, 16–19 October 2019, Riga, Latvia (Vol. 117, p. 141). Springer Nature. (in English)
dc.relation.referencesen6. Tkhoruk Y., Kucher O., Holotiuk M., Krystopchuk M. & Tson O. (2019) Modeling of assessment of reliability transport systems. Proceedings of ICCPT 2019 (Tern., May 28–29, 2019), pp. 204–210. (in English)
dc.relation.referencesen7. Dumbliauskas, V. (2019). Development and application of tour-based travel demand model for planning of urban transport networks (Doctoral dissertation, VGTU leidykla "Technika ") (in English)
dc.relation.referencesen8. Raux, C. (2003). A systems dynamics model for the urban travel system. In AET. European Transport Conference 2003–ETC 2003, 8–10 october 2003, Strasbourg (pp. 32-p). AET. (in English)
dc.relation.referencesen9. Ortúzar, J. de D. & Willumsen, L. G. (2011) "Modelling Transport". Fourth Edition, John Wiley and Sons, Chichester. (in English)
dc.relation.referencesen10. Balcombe, R., Mackett, R., Paulley, N., Preston, J., Shires, J., Titheridge, H. & et al. (2004). The demand for public transport: a practical guide. (in English)
dc.relation.referencesen11. Bhat, C. R., & Koppelman, F. S. (1999). Activity-based modeling of travel demand. In Handbook of transportation Science (pp. 35–61). Springer, Boston, MA. (in English)
dc.relation.referencesen12. Dianat, L., Habib, K. N., & Miller, E. J. (2020). Modeling and forecasting daily non-work/school activity patterns in an activity-based model using skeleton schedule constraints. Transportation research part A: policy and practice, 133, 337–352. doi:10.1016/j.tra.2020.01.017. (in English)
dc.relation.referencesen13. Andersson, A., Hiselius, L. W., & Adell, E. (2018). Promoting sustainable travel behaviour through the use of smartphone applications: A review and development of a conceptual model. Travel behaviour and society, 11, 52–61. doi:10.1016/j.tbs.2017.12.008. (in English)
dc.relation.referencesen14. Dolya, V. K., Gricyuk, P. M., Kristopchuk, M. E. (2006) "Investigation of the transport network of the region by the method of constructing the population density function", Journal of Municipal Services of Cities, Tekhnika Publisher, Volume 69, 205–211. (in Ukrainian)
dc.relation.referencesen15. Sivakumar, A. (2007). Modelling transport: a synthesis of transport modelling methodologies. Imperial College of London. (in English)
dc.relation.referencesen16. Hunt, J. D., & Simmonds, D. C. (1993). Theory and application of an integrated land-use and transport modelling framework. Environment and Planning B: Planning and Design, Volume 20(2), 221–244. (in English)
dc.relation.referencesen17. Hunt, J. D., Kriger, D. S., & Miller, E. J. (2005). Current operational urban land–use–transport modelling frameworks: A review. Transport reviews, Volume 25(3), 329–376. (in English)
dc.relation.referencesen18. Krystopchuk, M. Ye. (2012) "Social and economic efficiency of passenger transportation system suburban communication", Monograph. NUWEE, Rivne, Ukraine. (in Ukrainian).
dc.relation.referencesen19. Profillidis, V. A., & Botzoris, G. N. (2018). Modeling of transport demand: Analyzing, calculating, and forecasting transport demand. Elsevier. (in English)
dc.relation.referencesen20. Antwi, T., Quaye-Ballard, J. A., Arko-Adjei, A., Osei-wusu, W., & Quaye-Ballard, N. L. (2020). Comparing Spatial Accessibility and Travel Time Prediction to Commercial Centres by Private and Public Transport: A Case Study of Oforikrom District. Journal of Advanced Transportation, 2020. doi:10.1155/2020/8319089. (in English)
dc.relation.urihttps://doi.org/10.2478/v10244-012-0018-4
dc.relation.urihttps://doi.org/10.2478/jok-2019-0073
dc.rights.holder© Національний університет “Львівська політехніка”, 2021
dc.rights.holder© Krystopchuk M., 2021
dc.subjectнадійність
dc.subjectстійкість
dc.subjectтранспортні системи
dc.subjectтріангуляція Делоне
dc.subjectпоказникова функція
dc.subjectreliability
dc.subjectsustainability
dc.subjecttransport systems
dc.subjectDelaunay triangulation
dc.subjectpower function
dc.titleForecasting the mobility parameters of the inhabitants of suburban areas
dc.title.alternativeПрогнозування параметрів мобільності жителів приміських зон
dc.typeArticle

Files

Original bundle

Now showing 1 - 2 of 2
Thumbnail Image
Name:
2021v2n1_Krystopchuk_M-Forecasting_the_mobility_1-12.pdf
Size:
1.07 MB
Format:
Adobe Portable Document Format
Thumbnail Image
Name:
2021v2n1_Krystopchuk_M-Forecasting_the_mobility_1-12__COVER.png
Size:
415.86 KB
Format:
Portable Network Graphics

License bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
2.94 KB
Format:
Plain Text
Description: