Deciphering the Dynamics of Bus Rapid Transit Delays: A Decision Trees and Bayesian Networks Approach for Istanbul's Metrobus System
Keywords:
Bus Rapid Transit, Decision Tree, Bayesian Network, Public Transportation, DelayAbstract
Public transportation systems are vital components of urban infrastructure, shaping mobility and development. The emergence of Bus Rapid Transit (BRT) systems offers a promising solution to challenges faced by traditional bus services. However, delays within BRT systems can compromise their efficiency and reliability. The goal of this study is to investigate and analyze the critical factors influencing delays in Bus Rapid Transit (BRT) systems, specifically focusing on the Istanbul Metrobus, in order to provide actionable insights for optimizing operations and enhancing service reliability in BRT operations. Decision Trees identify critical parameters affecting delays, while Bayesian Networks elucidate causal dependencies among variables. The proposed Bayesian Precedence Network integrates these methodologies. This study employed a range of diverse data sources analyzed through advanced software tools like GeNIe Modeler. The results underscore the effectiveness of decision analysis in quantifying uncertainties and assessing critical factors that inform transit planning and optimization. The findings reveal that a passenger occupancy rate of 43% results in a 76% probability of no delays, while high traffic flow decreases this probability to 55%. Conversely, clear weather conditions enhance this probability to 80%, whereas rainy conditions and non-optimized operational efficiency heighten the risk of delays. Overall, this study provides a blueprint for addressing public transportation challenges, empowering transportation planners and policymakers to create more efficient and reliable transit networks.Downloads
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Azam, M., Nadeem, M., Asmah Hassan, S., & Che Puan, O. (2023). Impacts of Bus Only Lanes on Signalized Intersection Under Heterogeneous Traffic Conditions. Suranaree Journal of Science and Technology, 30(1), 010185(1-11). https://doi.org/10.55766/sujst-2023-01-e01617
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Buchel, B., & Corman, F. (2021). Probabilistic Bus Delay Predictions with Bayesian Networks. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), 3752–3758. https://doi.org/10.1109/ITSC48978.2021.9564537
Corman, F., & Kecman, P. (2018). Stochastic prediction of train delays in real-time using Bayesian networks. Transportation Research Part C: Emerging Technologies, 95, 599–615. https://doi.org/10.1016/j.trc.2018.08.003
de Oña, J., de Oña, R., & López, G. (2016). Transit service quality analysis using cluster analysis and decision trees: a step forward to personalized marketing in public transportation. Transportation, 43(5), 725–747. https://doi.org/10.1007/s11116-015-9615-0
Fernandez, R. (2011). Experimental study of bus boarding and alighting times. European Transport Conference, 1–14.
Halyal, S., Mulangi, R. H., Harsha, M. M., & Laddha, H. (2022). Study on Travel Time Characteristics of Hubli-Dharwad Bus Rapid Transit System in Comparison with Heterogeneous Traffic Lane (pp. 701–712). https://doi.org/10.1007/978-981-16-4396-5_61
Huen, K., Tighe, S., & McCabe, B. (2005). Incorporating User Delay Cost in Project Selection: A Canadian Case Study. 1st Annual Inter-University Symposium on Infrastructure Management, 1–21.
Istanbul Metropolitan Municipality. (2023). Istanbul Metropolitan Municipality. Https://Www.Ibb.Istanbul/En.
Jayakumar, M., & Maji, A. (2023). Decision Tree Analyses of Safety and Comfort Perceptions for Public Transportation in Kalyan-Dombivli Region of Maharashtra. Transportation in Developing Economies, 9(2), 14. https://doi.org/10.1007/s40890-023-00184-9
Kavalov, R. (2019, October 25). Predicting NJ Transit Delays Using Decision Trees. Medium.
Koller, D., & Friedman, N. (2009). Probabilistic Graphical Models: Principles and Techniques Adaptive Computation and Machine Learning series. MIT Press.
Lauría, E. J. M. (2008). An Information-Geometric Approach to Learning Bayesian Network Topologies from Data. In Innovations in Bayesian Networks (pp. 187–217). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-85066-3_8
Levinson, H., Zimmerman, S., Clinger, J., & Rutherford, G. (2002). Bus Rapid Transit: An Overview. Journal of Public Transportation, 5(2), 1–30. https://doi.org/10.5038/2375-0901.5.2.1
Niedermayer, D. (2008). An Introduction to Bayesian Networks and Their Contemporary Applications. In Innovations in Bayesian Networks (pp. 117–130). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-85066-3_5
Seeherman, J., Sisiopiku, V. P., & Skabardonis, A. (2012). Assessment of the Impact of Weather on Freeway Operations in California. Proceedings of the 91st Transportation Research Board Annual Meeting.
Shoman, M., Aboah, A., & Adu-Gyamfi, Y. (2020). Deep Learning Framework for Predicting Bus Delays on Multiple Routes Using Heterogenous Datasets. Journal of Big Data Analytics in Transportation, 2(3), 275–290. https://doi.org/10.1007/s42421-020-00031-y
Stutz, J., & Cheeseman, P. (1994). A Short Exposition on Bayesian Inference and Probability. National Aeronautic and Space Administration Ames Research Centre: Computational Sciences Division.
Ulak, M. B., Yazici, A., & Zhang, Y. (2020). Analyzing network-wide patterns of rail transit delays using Bayesian network learning. Transportation Research Part C: Emerging Technologies, 119, 102749. https://doi.org/10.1016/j.trc.2020.102749
Vuchic, V. R. (2007). Urban Transit Systems and Technology. Wiley. https://doi.org/10.1002/9780470168066
Wang, J., Han, Y., & Li, P. (2022). Integrated Robust Optimization of Scheduling and Signal Timing for Bus Rapid Transit. Sustainability, 14(24), 16922. https://doi.org/10.3390/su142416922
Wu, W., Luo, X., & Shi, B. (2023). Offset Optimization Model for Signalized Intersections Considering the Optimal Location Planning of Bus Stops. Systems, 11(7), 366. https://doi.org/10.3390/systems11070366
Xu, Q., Liu, S., & Li, Z. (2013). Bayesian Network Models for Transportation Planning. Springer Science & Business Media.
Published
2024-12-11
How to Cite
Karakurt, A. (2024). Deciphering the Dynamics of Bus Rapid Transit Delays: A Decision Trees and Bayesian Networks Approach for Istanbul’s Metrobus System. Jurnal Teknik Sipil, 21(1), 01–18. Retrieved from http://224305.koshikahk.tech/index.php/jts/article/view/9943
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Copyright (c) 2024 Ahmet Karakurt
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