Study of the passengers average waiting time at public transport stops
dc.citation.epage | 28 | |
dc.citation.issue | 1 | |
dc.citation.spage | 21 | |
dc.contributor.affiliation | Lviv Polytechnic National University | |
dc.contributor.author | Zhuk, Mykola | |
dc.contributor.author | Kovalyshyn, Volodymyr | |
dc.contributor.author | Zelemskyi, Vladyslav | |
dc.coverage.placename | Львів | |
dc.coverage.placename | Lviv | |
dc.date.accessioned | 2023-07-05T07:55:29Z | |
dc.date.available | 2023-07-05T07:55:29Z | |
dc.date.created | 2023-06-30 | |
dc.date.issued | 2023-06-30 | |
dc.description.abstract | Зазначено, що у прогнозуванні маршрутів громадського транспорту у містах важливими показниками, які необхідно враховувати, вважають тривалість перебування на маршруті автобусів, пасажирооборот на маршруті, точки притягання та середню тривалість очікування пасажирів на зупинках. Ці показники є основою у плануванні роботи міського транспорту. Зокрема, прогнозування тривалості руху в дорозі методом дослідження середнього часу очікування пасажирів на зупинках є важливим інструментом планування для транспортних компаній. Оскільки це дослідження може покращити якість запланованих послуг зменшенням розриву між фактичним і запланованим часом у дорозі. Обговорено зазначену актуальність і на основі експериментальних даних вказується на користь використання досліджень середньої тривалості очікування пасажирів, особливо із врахуванням груп населення. Насправді, серед великої кількості чинників, що впливають на роботу громадського транспорту, більшість із них, як доведено попередніми дослідженнями, відповідають певній математичній методиці. Аналіз виконано з використанням натурних досліджень пасажиропотоку на зупинках автобусних маршрутів (Львів, Україна). Дослідження пасажиропотоків на зупинках дає можливість покращити якості послуг громадського транспорту (розрахувати точніше тривалість руху між зупинками та тривалість перебування на них). Встановлено тривалість простоїв автобусів на обраних зупинках залежно від кількості пасажирів. Також наведено результати дослідження тривалості очікування пасажирами громадського транспорту на зупинках. Отримано залежності тривалості очікування автобуса від груп населення. На основі цієї інформації оператори системи можуть проєктувати та налаштовувати графіки руху автобусів відповідно до орієнтовної тривалості подорожі. | |
dc.description.abstract | When predicting public transport routes in cities, important indicators should be considered: the duration of stay on the bus route, passenger flow on the bus route, points of attraction and the passenger’s average waiting time at stops. These indicators are the basis for planning the operation of city transport. In particular, predicting the duration of traffic by studying the average passenger’s waiting time at stops is an important planning tool for transport companies. Therefore, this study can improve the quality of scheduled services by reducing the gap between actual and scheduled travel time. This article discusses this relevance and, based on experimental evidence, points to the benefit of using studies of average passenger waiting times, especially considering population groups. In fact, most of the factors which affect public transport operation, as had been proven by previous studies, follow a definite mathematical methodology. The analysis was performed using the data from field studies of passenger flow at bus stops (Lviv, Ukraine). The study of passengers at stopping points makes it possible to improve the quality of public transport services (calculate travel duration between stops and the duration of stay at them more accurately). The duration of stay at selected objects depending on a number of passengers was studied. Also, there are given the results of a study of the waiting time of public transport passengers at bus stops are given. A comparison of the dependence of the bus waiting time on population groups was obtained. After receiving this information, system operators can design and adjust the data according to the estimated trip duration. Nevertheless, it is necessary to carry out research at different types of stops in different parts of cities to clarify these data and for a more detailed analysis. | |
dc.format.extent | 21-28 | |
dc.format.pages | 8 | |
dc.identifier.citation | Zhuk M. Study of the passengers average waiting time at public transport stops / Mykola Zhuk, Volodymyr Kovalyshyn, Vladyslav Zelemskyi // Transport Technologies. — Lviv : Lviv Politechnic Publishing House, 2023. — Vol 4. — No 1. — P. 21–28. | |
dc.identifier.citationen | Zhuk M., Kovalyshyn V., Zelemskyi V. (2023) Study of the passengers average waiting time at public transport stops. Transport Technologies (Lviv), vol. 4, no 1, pp. 21-28. | |
dc.identifier.doi | https://doi.org/10.23939/tt2023.01.021 | |
dc.identifier.uri | https://ena.lpnu.ua/handle/ntb/59382 | |
dc.language.iso | en | |
dc.publisher | Видавництво Львівської політехніки | |
dc.publisher | Lviv Politechnic Publishing House | |
dc.relation.ispartof | Transport Technologies, 1 (4), 2023 | |
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dc.relation.referencesen | 1. Jeong, R. (2004). The prediction of bus arrival time using automatic vehicle location systems data. PhD thesis at Texas A&M University. (in English). | |
dc.relation.referencesen | 2. Fusco, G., Colombaroni, C. & Isaenko, N. (2016). Short-term speed predictions exploiting big data on large urban road networks. In: Transportation Research Part C: Emerging Technologies, 73, 183–201. doi: 10.1016/j.trc.2016.10.019 (in English). | |
dc.relation.referencesen | 3. Comi, A., Nuzzolo, A., Brinchi, S. & Verghini, R. (2017). Bus travel time variability: some experimental evidences. Transportation Research Procedia, 27, 101–108. doi: 10.1016/j.trpro.2017.12.072 (in English). | |
dc.relation.referencesen | 4. Cats, O. (2014). Regularity-driven bus operation: Principles, implementation and business models. In: Transport Policy, 36, 223–230. doi: 10.1016/j.tranpol.2014.09.002 (in English). | |
dc.relation.referencesen | 5. Moreira-Matias, L., Mendes-Moreira, J., de Sousa, J. F. & Gama, J. (2015). Improving Mass Transit Operations by Using AVL-Based Systems: A Survey. In: IEEE Transactions on Intelligent Transportation System, 16(4), 1636"1653. doi: 10.1109/TITS.2014.2376772 (in English). | |
dc.relation.referencesen | 6. Chen, M., Liu, X., Xia, J., & Chien, S. (2004). A Dynamic Bus-Arrival Time Prediction Model Based on APC Data. In: Computer-Aided Civil and Infrastructure Engineering, 19(5), 364–376. doi: 10.1111/j.1467-8667.2004.00363.x (in English). | |
dc.relation.referencesen | 7. Mendes-Moreira, J., Jorge, A. M., de Sousa, J. F. & Soares, J. (2012). Comparing state-of-the-art regression methods for long term travel time prediction. In: Journal Intelligent Data Analysis archive, 16(3), 427–449. doi: 10.3233/IDA-2012-0532 (in English). | |
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dc.relation.referencesen | 9. Hassan, S. M., Moreira-Matias, L., Khiari, J. & Cats, O. (2017). Feature Selection Issues in Long-Term Travel Time Prediction. In: Advances in Intelligent Data Analysis XV – International Symposium on Intelligent Data Analysis, Springer, ( pp. 98–109) (in English). | |
dc.relation.referencesen | 10. Hyndman, R. J. & Athanasopoulos, G. (2018). Forecasting: principles and practice. Second edition. OTexts. (in English). | |
dc.relation.referencesen | 11. Jeon, S. & Hong, B. (2016). Monte Carlo simulation-based traffic speed forecasting using historical big data. In: Future Generation Computer Systems, 65, 182–195. doi: 10.1016/j.future.2015.11.022 (in English). | |
dc.relation.referencesen | 12. Suwardo, Madzlan, N. & Ibrahim, K. (2010). ARIMA models for bus travel time prediction. In: The Journal of the Institution of Engineers, Malaysia, 71(2), 49–58 (in English). | |
dc.relation.referencesen | 13. Williams, B. M. & Hoel, L. A. (2003). Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: Theoretical basis and empirical results. In: Journal of Transportation Engineering, 129(6), 664–672. doi: 10.1061/(ASCE)0733-947X(2003)129:6(664) (in English). | |
dc.relation.referencesen | 14. Yap, M., Luo, D., Cats, O., van Oort, N. & Hoogendoorn, S. (2019). Where shall we sync? Clustering passenger flows to identify urban public transport hubs and their key synchronization priorities. Transportation Research Part C: Emerging Technologies, 98, pp.433–448. doi: 10.1016/j.trc.2018.12.013 (in English). | |
dc.relation.referencesen | 15. Honcharenko, S. (2017). Vyznachennia popytu na posluhy pasazhyrskoho marshrutnoho transportu v serednikh mistakh [The demand determining for passenger route transport service in the middle cities]. Extended abstract of candidate’s thesis. Kharkiv, KhNADU. (in Ukrainian). | |
dc.relation.referencesen | 16. Nielsen, O., Eltved, M., Anderson, M., & Prato, C. (2021). Relevance of detailed transfer attributes in large-scale multimodal route choice models for metropolitan public transport passengers. Transportation Research Part A: Policy And Practice, 147, 76–92. doi: 10.1016/j.tra.2021.02.010. (in English). | |
dc.relation.referencesen | 17. Elidan, G. & Friedman, N. (2005). Learning Hidden Variable Networks: The Information Bottleneck Approach. Journal of Machine Learning Research, 6. 81–127 (in English). | |
dc.relation.referencesen | 18. Zghurovskyi, M., Bidiuk P., Terentev O. (2007). Systemna metodyka pobudovy baiiesovykh merezh [A systematic method of designing Bayesian networks]. Naukovi Visti "NTUU "KPI" [KPI Science News], 4, 47–61. (in Ukrainian). | |
dc.rights.holder | © Національний університет „Львівська політехніка“, 2023 | |
dc.rights.holder | © M. Zhuk, V. Kovalyshyn, V. Zelemskyi, 2023 | |
dc.subject | тривалість руху автобуса | |
dc.subject | пасажиропотік | |
dc.subject | пасажири | |
dc.subject | час очікування | |
dc.subject | зупинка громадського транспорту | |
dc.subject | bus travel time | |
dc.subject | bus traffic | |
dc.subject | passengers | |
dc.subject | waiting time | |
dc.subject | public transport stop | |
dc.title | Study of the passengers average waiting time at public transport stops | |
dc.title.alternative | Дослідження середньої тривалості очікування пасажирів на зупинці громадського транспорту | |
dc.type | Article |