Abstract
This paper explores the forecasting of public transport demand using mobility data obtained from electronic tickets and smart cards. The research aims to estimate the demand for a selected route at a specific bus stop on a given day and time slot. The study utilizes a large dataset of historical demand data, including approximately 10 million validations collected in 2019 by the Piedmont transport operator Granda Bus, and combines it with additional information such as weather conditions, anonymized user data, and temporal segmentation of the yearly calendar. To identify the peculiarities in demand forecasting for each bus route and stop, a clustering analysis is performed, resulting in the identification of six cohesive and homogeneous clusters. Various machine learning models are tested and compared to determine the most suitable model for forecasting public transport demand at each stop within one-hour time slots. The results demonstrate that machine learning algorithms consistently outperform average-based techniques: the machine learning algorithms exhibit a significant improvement (up to 50% compared to the baseline) when demand uncertainty is greater. The proposed methodology framework is replicable and transferable to other areas, providing a valuable tool for optimizing resource allocation and network planning, while enhancing user satisfaction by accurately forecasting passenger demand at each stop and desired time slot.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Costa, V., Fontes, T., Costa, P.M., Dias, T.G.: Prediction of journey destination in urban public transport. In: Pereira, F., Machado, P., Costa, E., Cardoso, A. (eds.) EPIA 2015. LNCS (LNAI), vol. 9273, pp. 169–180. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23485-4_18
Briand, A.S., Côme, E., Trépanier, M., Oukhellou, L.: Analyzing year-to-year changes in public transport passenger behaviour using smart card data. Transp. Res. Part C: Emerg. Technol. 79, 274–289 (2017)
Arnone, M., Delmastro, T., Giacosa, G., Paoletti, M., Villata, P.: The Potential of e-ticketing for public transport planning: the piedmont region case study. Transp. Res. Procedia 10, 3–10 (2016)
Arnone, M., Delmastro, T., Negrino., Arneodo, F., Botta, C., Friuli, G.: Estimation of public transport user behaviour and trip chains through the piedmont region e-ticketing system. In: Proceedings of 14th ITS European Congress, Lisbon, Portugal, ITS-SP 2273 (2020)
Trépanier, M., Tranchant, N., Chapleau, R.: Individual trip destination estimation in a transit smart card automated fare collection system. J. Intell. Transp. Syst.: Technol. Plan. Oper. 11, 1–14 (2007)
He, L., Trépanier, M.: Estimating the destination of unlinked trips in transit smart card fare data. Transp. Res. Rec.: J. Transp. Res. Board 2535, 97–104 (2015)
Toqué, F., Côme, E., El Mahrsi M.K., Oukhellou, L.: Forecasting dynamic public transport origin-destination matrices with long-short term memory recurrent neural networks. In: IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, Brazil, pp. 1071–1076 (2016)
Zhao, Z., Koutsopoulos, H., Zhao, J.: Individual mobility prediction using transit smart card data. Transp. Res. Part C Emerg. Technol. 10, 19–34 (2018)
Yu, H., Wu, Z., Chen, D., Ma, X.: Probabilistic prediction of bus headway using relevance vector machine regression. IEEE Trans. Intell. Transp. Syst. 18, 1–10 (2016)
Sun, F., Pan, Y., White, J., Dubey, A.: Real-time and predictive analytics for smart public transportation decision support system (2016)
Othman, M.S., Tan, G.: Predictive simulation of public transportation using deep learning. In: Proceedings of 18th Asia Simulation Conference, Kyoto, Japan, 27–29 October, pp. 96–106 (2018)
Cristóbal, T., Padrón, G., Quesada-Arencibia, A., Hernández, F., de Blasio, G., GarcÃa, C.: Using data mining to analyze dwell time and nonstop running time in road-based mass transit systems. Proceedings 2(19), 1217 (2018)
Yu, H., Chen, D., Wu, Z., Ma, X., Wang, Y.: Headway-based bus bunching prediction using transit smart card data. Transp. Res. Part C: Emerg. Technol. 72, 45–59 (2016)
Asmael, N., Waheed, M.: Demand estimation of bus as a public transport based on gravity model. MATEC Web Conf. 162, 01038 (2018)
El Mahrsi, M.K., Côme, E., Oukhellou, L., Verleysen, M.: Clustering smart card data for urban mobility analysis. IEEE Trans. Intell. Transp. Syst. 18(3), 712–728 (2017)
Kieu, L.M., Bhaskar, A., Chung, E.: Passenger segmentation using smart card data. IEEE Trans. Intell. Transp. Syst. 16(3), 1537–1548 (2015)
Kim, K.: Identifying the structure of cities by clustering using a new similarity measure based on smart card data. IEEE Trans. Intell. Transp. Syst. 21(5), 2002–2011 (2020)
Liu, L., Chen, R.C.: A novel passenger flow prediction model using deep learning methods. Transp. Res. Part C: Emerg. Technol. 84, 74–91 (2017)
Ding, C., Wang, D., Ma, X., Li, H.: Predicting short-term subway ridership and prioritizing its influential factors using gradient boosting decision trees. Sustainability 8, 1100 (2016)
Liu, Y., Liu, Z., Jia, R.: DeepPF: A deep learning based architecture for metro passenger flow prediction. Transp. Res. Part C Emerg. Technol. 101, 18–34 (2019)
Guo, J., Xie, Z., Qin, Y., Jia, L., Wang, Y.: Short-term abnormal passenger flow prediction based on the fusion of SVR and LSTM. IEEE Access 7, 42946–42955 (2019)
Milenkovic, M., Svadlenka, L., Melichar, V., Bojovic, N., Avramovic, Z.: SARIMA modelling approach for railway passenger flow forecasting. Transport, 1–8 (2015)
Toqué, F., Khouadjia, M., Côme, E., Trépanier, M., Oukhellou, L.: Short and long term forecasting of multimodal transport passenger flows with machine learning methods, pp. 560–566 (2017)
Gastaldi, E.: Forecasting public transport demand using smart cards data. https://webthesis.biblio.polito.it/20414/. Accessed 24 July 2023
Attili, A.: The demand for public transport: analysis of mobility patterns and bus stops.https://webthesis.biblio.polito.it/17338/. Accessed 24 July 2023
3bmeteo. https://www.3bmeteo.com/. Accessed 30 May 2023
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Caroleo, B. et al. (2024). Machine Learning Methods to Forecast Public Transport Demand Based on Smart Card Validations. In: Martins, A.L., Ferreira, J.C., Kocian, A., Tokkozhina, U., Helgheim, B.I., Bråthen, S. (eds) Intelligent Transport Systems. INTSYS 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 540. Springer, Cham. https://doi.org/10.1007/978-3-031-49379-9_11
Download citation
DOI: https://doi.org/10.1007/978-3-031-49379-9_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-49378-2
Online ISBN: 978-3-031-49379-9
eBook Packages: Computer ScienceComputer Science (R0)