Improving public transport prediction accuracy through multi-source data integration
A case study of Ljubljana
DOI:
https://doi.org/10.66838/sauc.6424Keywords:
Public transport, Congestion, Travel time, Machine learning, LOOCVAbstract
This study proposes a data acquisition, preprocessing and machine learning pipeline to enhance the accuracy of travel time estimates for the public bus network in Ljubljana, Slovenia. Existing prediction services often rely on vehicle location and may insufficiently capture dynamic congestion and contextual conditions. Using data from five Ljubljana Passenger Transport (LPP) routes, we evaluate LPP and Google Routes estimates against observed travel times. We develop a multisource model combining real-time variables with contextual predictors, including bus line, time of day, day type, and weather. The approach demonstrates how multisource integration of local transit feeds, external routing, and environmental indicators can improve prediction accuracy, reliability, and overall performance.
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Bachu, A. K., Reddy, K. K., & Vanajakshi, L. (2021). Bus travel time prediction using support vector machines for high variance conditions. Transport, 36(3), 221–234. https://doi.org/10.3846/transport.2021.15220
Bole, D. (2004). Geografija javnega potniškega prometa na primeru Ljubljane. Geografski vestnik, 76(2), 21–32.
Chen, R., Zhang, J., Xu, X., Han, T.-D., & He, M. (2026). Comparative analysis and case study of travel time reliability measures for public usage of transportation information. Journal of Transport Geography. https://doi.org/10.1016/j.jtrangeo.2026.103928
Dunne, L., Rocco Di Torrepadula, F., Di Martino, S., McArdle, G., & Nardone, D. (2023). Bus journey time prediction with machine learning: An empirical experience in two cities. In M. A. Mostafavi & G. Del Mondo (Eds.), Web and wireless geographical information systems (pp. 105–120). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-34612-5_7
Google. (2024, October 16). GTFS Realtime Overview. Google for Developers. https://developers.google.com/transit/gtfs-realtime
Google. (2026). Routes API. Google for Developers. https://developers.google.com/maps/documentation/routes
Gössling, S., Neger, C., Steiger, R., & Bell, R. (2023). Weather, climate change, and transport: A review. Natural Hazards, 118, 1341–1360. https://doi.org/10.1007/s11069-023-06054-2
Hansson, J., Pettersson-Löfstedt, F., Svensson, H., & Wretstrand, A. (2022). Effects of rural bus stops on travel time and reliability. Public Transport, 14, 683–704. https://doi.org/10.1007/s12469-021-00281-1
Henríquez-Jara, B., Arriagada, J., & Tirachini, A. (2025). Impact of real-time information on passenger satisfaction across varying public transport quality levels in 13 Chilean cities. Transportation Research Part A: Policy and Practice, 200. https://doi.org/10.1016/j.tra.2025.104408
Kandiri, A., Ghiasi, R., Nogal, M., & Teixeira, R. (2024). Travel time prediction for an intelligent transportation system based on a data-driven feature selection method considering temporal correlation. Transportation Engineering, 18, Article 100272. https://doi.org/10.1016/j.treng.2024.100272
Lau, J. (2020, September 3). Google Maps 101: How AI helps predict traffic and determine routes. The Keyword. https://blog.google/products-and-platforms/products/maps/google-maps-101-how-ai-helps-predict-traffic-and-determine-routes/
Liu, L., Porr, A., & Miller, H.J. (2023). Realizable accessibility: Evaluating the reliability of public transit accessibility using high-resolution real-time data. Journal of Geographical Systems, 25(3), 429–451. https://doi.org/10.1007/s10109-022-00382-w
Ljubljanski potniški promet. (2025). Shema dnevnih linij LPP: Mestni javni linijski prevoz in integrirane potniške linije [Public transport network map].
Ljubljanski potniški promet. (2026). LPP API documentation. https://data.lpp.si/doc/
Mestna občina Ljubljana. (2022). Lokalni energetski koncept Mestne občine Ljubljana za obdobje od 2022 do 2030: I. del – Analiza stanja.
Open-Meteo. (2026). Weather Forecast API. https://open-meteo.com/en/docs
Parslov, A., Petersen, N.C., & Rodrigues, F. (2023). Short-term bus travel time prediction for transfer synchronization with intelligent uncertainty handling. Expert Systems with Applications, 232, 120751. https://doi.org/10.1016/j.eswa.2023.120751
Rashvand, N., Hosseini, S., Azarbayjani, M., & Tabkhi, H. (2024). Real-time bus arrival prediction: A deep learning approach for enhanced urban mobility. In Proceedings of the 13th International Conference on Operations Research and Enterprise Systems (pp. 123–132). SCITEPRESS. https://doi.org/10.5220/0012365500003639
Schmitt, A. U., Burgemeister, F., Dorff, H., Finn, T., Hansen, A., Kirsch, B., Lange, I., Radtke, J., & Ament, F. (2023). Assessing the weather conditions for urban cyclists by spatially dense measurements with an agent-based approach. Meteorological Applications, 30(6), e2164. https://doi.org/10.1002/met.2164
scikit-learn developers. (n.d.). sklearn. scikit-learn. Retrieved August 28, 2026, from https://scikit-learn.org/stable/api/sklearn.html
Serin, F., Alisan, Y., & Erturkler, M. (2022). Predicting bus travel time using machine learning methods with three-layer architecture. Measurement, 198, 111403. https://doi.org/10.1016/j.measurement.2022.111403
Slovenian Environment Agency (ARSO). (2025). Ljubljana Bežigrad: Climate statistics and normals 1991–2020.
SURS, Statistical Office of the Republic of Slovenia. (2026). Selected data on municipalities, Slovenia, annually. SiStat Database. https://pxweb.stat.si/SiStatData/pxweb/en/Data/Data/2640010S.px/
Trček, I. S., & Rijavec, R. (2026). Strategic evaluation of cycling infrastructure in emerging contexts: A case study of high-capacity corridors in Ljubljana, Slovenia. European Transport Research Review, 18, Article 67. https://doi.org/10.1186/s12544-026-00833-1
van Ardenne, M. T., Cebecauer, M., Cats, O., & Ma, Z. (2025). Personalised passenger information systems in public transport: A review and a 5-level personalisation taxonomy. Transport Reviews, 45(6), 1016–1047.
Xu, H., & Ying, J. (2017). Bus arrival time prediction with real-time and historic data. Cluster Computing, 20(4), 3099–3106. https://doi.org/10.1007/s10586-017-1006-1
Yu, B., Wang, H., Shan, W., & Yao, B. (2018). Prediction of bus travel time using random forests based on near neighbors. Computer-Aided Civil and Infrastructure Engineering, 33(4), 333–350.
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