Analysis of the boarding and disembarking process time on the example of the Pesa 122NaB tram operated in the city of Bydgoszcz
DOI:
https://doi.org/10.37660/PJTI.2025.25.1.4Keywords:
passenger flow, vestibules, public transport, train designAbstract
The processes of boarding and disembarking passengers in public transport significantly affect the travel time between tram stops. As a part of sustainable urban development, it is necessary to implement new techniques to streamline traffic and promptly respond to the public transport passengers' demand for efficient movement around the city. The structural solutions used in tram and bus vestibule areas are constantly changing to speed up these processes. Understanding passenger behaviors is a necessary element in optimizing passenger flow between the stop and the rail vehicle, as well as within the vehicle itself. However, there is a lack of data that could be used to evaluate specific solutions. The article measured boarding and disembarking times from a Pesa 122NaB tram example operated in the city of Bydgoszcz, which has a population of approximately 340,500 residents. Due to the city's functioning specificity, the analysis of passenger flow in trams differs from that in metropolitan areas with over a million residents because tram journeys in cities of this size occur on longer routes than in metropolitan transport, which results from the lack of alternatives to tram transport. Most analyses are conducted within metropolitan transport, where the dynamics of passenger flow will be significantly different than in medium-sized cities. This study analyzed a specific case where the district served by the tram line under analysis – Fordon – is separated from the city, generating a greater number of long-distance journeys. The study recorded 106 boarding and 126 disembarking instances from the tram. The studies were preliminary as part of a development project for an AI platform enabling real-time passenger flow analysis. The article analyzes the frequency of passenger numbers occurrence. The average passenger flow time was approximated by a linear function R2 = 0.94. The time per person decreased with the number of passengers; it ranged from 1 to 1.5 seconds for 5 or more passengers. The presented data can be used for further comparative analysis of passenger flow processes, both for the proposed AI algorithm and for analytical purposes for rail vehicle manufacturers and municipal public transport operators.
References
Fujii, H., Uchida, H., Yoshimura, S.: Agent-based simulation framework for mixed traffic of cars, pedestrians and trams. Transp Res Part C Emerg Technol 85, 234-48 (2017). https://doi.org/10.1016/j.trc.2017.09.018 DOI: https://doi.org/10.1016/j.trc.2017.09.018
Tomczak, U., Mielczarek, L.: Two-level Structure for Tram and Road Traffic in the Centre of City – Lodz in Poland. Procedia Eng 142, 574-581 (2016). https://doi.org/10.1016/j.proeng.2016.06.075. DOI: https://doi.org/10.1016/j.proeng.2016.06.075
Caban, J.: A study of eco-driving possibilities in passenger car used in urban traffic. Arch Automot Eng - Arch Motoryz. 91(1), 37-48 (2021). https://doi.org/10.14669/AM.VOL91.ART3 DOI: https://doi.org/10.14669/AM.VOL91.ART3
Kagramanian, A., Aulin, D., Trubchaninova, K., Caban, J,, Voronin, A., Basov, A.: Perspectives of multifunctional integrated suburban-urban rail transport development. Sci J Silesian Univ Technol Ser Transp 120, 105-115 (2023). https://doi.org/10.20858/sjsutst.2023.120.7. DOI: https://doi.org/10.20858/sjsutst.2023.120.7
Ravensbergen, L., Van Liefferinge, M., Isabella, J., Zhang, M., El-Geneidy, A.: Accessibility by public transport for older adults: A systematic review. J Transp Geogr. 103, 103408 (2022). https://doi.org/10.1016/j.jtrangeo.2022.103408. DOI: https://doi.org/10.1016/j.jtrangeo.2022.103408
Mamcarz, P., Drozdziel, P., Gzik, A., Rybicka, I., Drozdziel, P.: Characteristics of urban transport users and their level of satisfaction with transport services. A longitudinal study of passengers in Lublin city in 2018 and 2020. Transp Res Procedia 74, 371-378 (2023). https://doi.org/10.1016/j.trpro.2023.11.157. DOI: https://doi.org/10.1016/j.trpro.2023.11.157
Gnap, J., Dydkowski, G., Urbanek, A.: The experience economy in the systems of urban and regional transport - from a change of location to positive emotional impressions during movements. Commun Sci Lett Univ Žilina 26(1), A22-A36 (2024). https://doi.org/10.26552/com.C.2024.009 DOI: https://doi.org/10.26552/com.C.2024.009
L'upták, V., Pecman, J.: Assessment of the Quality of Connections on the Rail Transport Network: a Case Study. In: Transport Means - Proceedings of the International Conference, 842-846 (2021).
Hurtová, I., Sejkorová, M., Verner, J.: Experience operating a trolleybus equipped with traction batteries nLTO. In: Transport Means - Proceedings of the International Conference, 242-247 (2017).
Stopka, O., Stopkova, M., Rybicka, I., Gross, P., Jeřábek, K.: Use of activity-based costing approach for cost management in a railway transport enterprise. Sci J Silesian Univ Technol Ser Transp. 111, 151-160 (2021). https://doi.org/10.20858/sjsutst.2021.111.13. DOI: https://doi.org/10.20858/sjsutst.2021.111.13
Caban, J., Droździel, P., Stoma, M., Dudziak, A., Vrábel, J., Stopka, O.: Road Traffic Safety in Poland, Slovakia, and Czech Republic – Statistic Analysis. In: 2020 XII International Science-Technical Conference Automotive Safety, 1-7 (2020). https://doi.org/10.1109/AUTOMOTIVESAFETY47494.2020.9293507 DOI: https://doi.org/10.1109/AUTOMOTIVESAFETY47494.2020.9293507
Gnap, J., Dydkowski, G., Ondruš, J., Synák, F.: Tariff systems and distance measurement of public passenger transport stops. Commun Sci Lett Univ Žilina 25(1), A26-A39 (2023). https://doi.org/10.26552/com.C.2023.009 DOI: https://doi.org/10.26552/com.C.2023.009
Kalašová, A., Čulík, K.: The Micromobility Tendencies of People and Their Transport Behavior. Appl Sci. 13(19), 10559 (2023). https://doi.org/10.3390/app131910559. DOI: https://doi.org/10.3390/app131910559
Kalašová, A., Hájnik, A., Kubaľák, S., Beňuš, J., Harantová, V.: The impact of actuated control on the environment and the traffic flow. J Appl Eng Sci. 20(2), 305-314 (2022). https://doi.org/10.5937/jaes0-33043. DOI: https://doi.org/10.5937/jaes0-33043
Ližbetin, J., Pečman, J., Hanzl, J., Stopka, O.: Modeling the intelligent transport systems elements functionality testing plan: a case study. Commun Sci Lett Univ Žilina 24(4), F97-F108 (2022). https://doi.org/10.26552/com.C.2022.4.F97-F108. DOI: https://doi.org/10.26552/com.C.2022.4.F97-F108
Bi, L., Zhou, S., Ke, J., Song, X.: Knowledge-Mapping Analysis of Urban Sustainable Transportation Using CiteSpace. Sustainability 15, 958 (2023). https://doi.org/10.3390/su15020958 DOI: https://doi.org/10.3390/su15020958
Son, TH., Weedon, Z., Yigitcanlar, T., Sanchez, T., Corchado, JM., Mehmood, R.: Algorithmic urban planning for smart and sustainable development: Systematic review of the literature. Sustain Cities Soc. 94, 105562 (2023). https://doi.org/10.1016/j.scs.2023.104562. DOI: https://doi.org/10.1016/j.scs.2023.104562
Tang, T., Fonzone, A., Liu, R., Zhu, X.: Multi-stage deep learning approaches to predict boarding behavior of bus passengers. Sustain Cities Soc., 103111 (2021). https://doi.org/10.1016/j.scs.2021.103111. DOI: https://doi.org/10.1016/j.scs.2021.103111
Simović S, Ivanišević T, Bradić B, Čičević S, Trifunović A. What Causes Changes in Passenger Behavior in South-East Europe during the COVID-19 Pandemic? Sustainability 13(15), 8398 (2021). https://doi.org/10.3390/su13158398. DOI: https://doi.org/10.3390/su13158398
Mickiewicz, P., Radecka, K.: Urban transport quality in terms of the sustainable development. In: Proceedings of the 2019 International Conference "Economic Science for Rural Development", 185-191 (2019). https://doi.org/10.22616/ESRD.2019.07. DOI: https://doi.org/10.22616/ESRD.2019.074
Szopińska, K.: Sustainable urban transport and the level of road noise – a case study of the city of Bydgoszcz. Geomatics Environ Eng. 13(4), 93-104 (2019). https://doi.org/10.7494/geom.2019.13.4.93. DOI: https://doi.org/10.7494/geom.2019.13.4.93
Skorupa, J., Toader, CS., Kryszak, L.: The impact of transport investment on the quality of life for citizens of Bydgoszcz. Lucrari Stiintifice Univ Stiinte Agric Med Vet Banat Timisoara Ser I Manag Agricol. 22(3), 243-249 (2020).
Podolski, J.: Transport w miastach (ang. Transport in the cities). Warsaw: Wydawnictwo Komunikacji i Łączności; 1985.
Zhai, H., Cui, L., Nie, Y., Xu, X., Zhang, W.: A Comprehensive Comparative Analysis of the Basic Theory of the Short Term Bus Passenger Flow Prediction. Symmetry (Basel) 10, 369 (2018). https://doi.org/10.3390/sym10090369. DOI: https://doi.org/10.3390/sym10090369
Baghbani, A., Bouguila, N., Patterson, Z.: Short-Term Passenger Flow Prediction Using a Bus Network Graph Convolutional Long Short-Term Memory Neural Network Model. Transp Res Rec. 2677, 1331-1340 (2023). https://doi.org/10.1177/03611981221112673 DOI: https://doi.org/10.1177/03611981221112673
Guo, J., Xie, Z., Li, Q., Zhan, S., Xu, J.: Railway Passenger Flow Recognition Algorithm for Terminal Station Based on Cost Theory and Automatic Frequency Control. IEEE Access. 8, 26885-26892 (2020). https://doi.org/10.1109/ACCESS.2020.2968721. DOI: https://doi.org/10.1109/ACCESS.2020.2968721
Zou, L., Hu, S., Zhu, L.: Based on AFC Data Calculation of Walking Time in Metro Stations Considering the Impact of Passenger Flows. Sustainability 15, 6660 (2023). https://doi.org/10.3390/su15086660. DOI: https://doi.org/10.3390/su15086660
Samaras, P., Fachantidis, A., Tsoumakas, G., Vlahavas, I.: A prediction model of passenger demand using AVL and APC data from a bus fleet, pp. 129–134. In: Proceedings of the 19th Panhellenic Conference on Informatics. New York, NY, USA: ACM; (2015). https://doi.org/10.1145/2801948.2801984. DOI: https://doi.org/10.1145/2801948.2801984
Petrouš, M., Suzdaleva, E., Nagy, I.: Modeling of Passenger Demand using Mixture of Poisson Components. In: Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics, 617-624 (2019). https://doi.org/10.5220/0007831306170624. DOI: https://doi.org/10.5220/0007831306170624
Małek, A., Caban, J., Dudziak, A., Marciniak, A., Vrábel, J.: The Concept of Determining Route Signatures in Urban and Extra-Urban Driving Conditions Using Artificial Intelligence Methods. Machines 11, 575 (2023). https://doi.org/10.3390/machines11050575. DOI: https://doi.org/10.3390/machines11050575
Pekel, E., Soner Kara, S.: Passenger Flow Prediction Based on Newly Adopted Algorithms. Appl Artif Intell. 31, 64-79 (2017). https://doi.org/10.1080/08839514.2017.1296682.
Özuysal, M., Tayfur, G., Tanyel, S.: Passenger Flows Estimation of Light Rail Transit (LRT) System in Izmir, Turkey Using Multiple Regression and ANN Methods. Promet Traffic Transp. 24, 1-14 (2012) https://doi.org/10.7307/ptt.v24i1.264. DOI: https://doi.org/10.7307/ptt.v24i1.264
Wang, P., Chen, X., Chen, J., Hua, M., Pu, Z.: A two-stage method for bus passenger load prediction using automatic passenger counting data. ET Intell Transp Syst. 15, 248-260 (2021). https://doi.org/10.1049/itr2.12018. DOI: https://doi.org/10.1049/itr2.12018
Zhou, C., Dai, P., Li, R.: The passenger demand prediction model on bus networks. In: Proceedings of the IEEE 13th International Conference on Data Mining Workshops. IEEE, 1069-1076 (2013). https://doi.org/10.1109/ICDMW.2013.20. DOI: https://doi.org/10.1109/ICDMW.2013.20
Zhao, SZ., Ni, TH., Wang, Y., Gao, XT.: A new approach to the prediction of passenger flow in a transit system. Comput Math Appl. 61, 1968-1974 (2011). https://doi.org/10.1016/j.camwa.2010.08.023. DOI: https://doi.org/10.1016/j.camwa.2010.08.023
Wei, Y., Chen, MC.: Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks. Transp Res Part C Emerg Technol. 21, 148-162 (2012). https://doi.org/10.1016/j.trc.2011.06.009. DOI: https://doi.org/10.1016/j.trc.2011.06.009
Du, B., Peng, H., Wang, S., Bhuiyan, MZA., Wang, L., Gong, Q, et al. Deep Irregular Convolutional Residual LSTM for Urban Traffic Passenger Flows Prediction. IEEE Trans Intell Transp Syst. 21, 972-985 (2020). https://doi.org/10.1109/TITS.2019.2900481 DOI: https://doi.org/10.1109/TITS.2019.2900481
Izudheena, S., Mulerikkalb, JP., Johnc, MJ., Kd, M., Joshye, J., Beveira, GM.: Short-Term Passenger Count Prediction for Metro Stations using LSTM Network. Turk J Comput Math Educ. 12(3), 4026-4034 (2021). https://doi.org/10.17762/turcomat.v12i3.1694. DOI: https://doi.org/10.17762/turcomat.v12i3.1693
Feng, F., Zou, Z., Liu, C., Zhou, Q., Liu, C.: Forecast of Short-Term Passenger Flow in Multi-Level Rail Transit Network Based on a Multi-Task Learning Model. Sustainability 15, 3296 (2023). https://doi.org/10.3390/su15043296 DOI: https://doi.org/10.3390/su15043296
Li, M-T., Zhao, F., Chow, L-F., Zhang, H., Li, S-C.: Simulation Model for Estimating Bus Dwell Time by Simultaneously Considering Numbers of Disembarking and Boarding Passengers. Transp Res Rec. 1971, 59-65 (2006). https://doi.org/10.3141/1971-09. DOI: https://doi.org/10.1177/0361198106197100107
Nagaraj, N., Gururaj, HL., Swathi, BH., Yu-Chen, H.: Passenger flow prediction in bus transportation system using deep learning. Multimed Tools Appl. 81, 12519-12542 (2022). https://doi.org/10.1007/s11042-022-12306-3. DOI: https://doi.org/10.1007/s11042-022-12306-3
Yildiz, B., Förster, P., Feuerle, T., Hecker, P., Bugow, S., Helber, S.: A generic approach to analyze the impact of a future aircraft design on the boarding process. Energies 11, 303 (2018). https://doi.org/10.3390/en11020303. DOI: https://doi.org/10.3390/en11020303
World Population Review. Bydgoszcz Population 2024. Retrieved from https://worldpopulationreview.com/world-cities/bydgoszcz-population.
Bydgoszcz Information Center. Public transport. Retrieved from https://visitbydgoszcz.pl/en/plan/moving-around/2402-public-transport
Municipal Roads and Public Transport Authority in Bydgoszcz. Schematy sieci komunikacji miejskiej (ang. Schemes of the public transport network). Retrieved from https://zdmikp.bydgoszcz.pl/pl/transport/schematy-sieci-komunikacji-miejskiej.
Urban Transport Enthusiasts Club. Konstal 105Na #2566,#2567. Retrieved from https://kstm.pl/en/105na-opis.html.
Laughlin, PR., Hatch, EC., Silver, JS., Boh, L.: Groups perform better than the best individuals on letters-to-numbers problems: Effects of group size. J Pers Soc Psychol. 90, 644-651 (2006). https://doi.org/10.1037/0022-3514.90.4.644. DOI: https://doi.org/10.1037/0022-3514.90.4.644
Yetton, P., Bottger, P.: The relationships among group size, member ability, social decision schemes, and performance. Organ Behav Hum Perform. 32, 145-159 (1983). https://doi.org/10.1016/0030-5073(83)90144-7. DOI: https://doi.org/10.1016/0030-5073(83)90144-7
Sołtysiak A, Migawa K, Sołtysiak R, Maćkowiak P. Time to replace passengers at bus stations in Bydgoszcz. Autobusy Tech Eksploat Syst Transp. 6, 1078-1083 (2017).
Downloads
Published
Versions
- 2025-07-03 (3)
- 2025-06-30 (2)
- 2025-06-30 (1)
How to Cite
Issue
Section
License
Copyright (c) 2025 Polish Journal of Technical Issue
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
