Multi-layer perceptron based transfer passenger flow prediction in Istanbul transportation system
Estimating passenger movement in transportation networks is a critical aspect of public transportation systems. It allows for a greater understanding of traffic patterns, as well as efficient system evaluation and monitoring. It could also help with precise timing to emergencies or important events, as well as the improvement of urban transport system weaknesses and service quality. The number of transfer passengers demand in Istanbul, Turkey's biggest and most developed metropolis, was used to construct a real-world forecasting model in this study. The number of transfer passengers has been forecasted using popular machine learning methods such as kNN (k-Nearest Neighbours), LR (Linear Regression), RF (Random Forest), SVM (Support Vector Machine), XGBoost and MLP. The dataset utilized is made up of hourly passenger transfer counts gathered at two public transportation transfer stations in Istanbul in January 2020. Using MSE, RMSE, MAE and R2 parameters, each model's experimental data have been thoroughly evaluated. MLP has more successfully other machine learning algorithms in the majority of transportation lines, according to the experimental results.
Abeyrathna, K. D., Rasca, S., Markvica, K., & Granmo, O.C. (2021). Public Transport Passenger Count Forecasting in Pandemic Scenarios Using Regression Tsetlin Machine. Case Study of Agder, Norway. In Smart Transportation Systems 2021 (pp. 27–37). Springer.
Boukerche, A., & Wang, J. (2020). Machine Learning-based traffic prediction models for Intelligent Transportation Systems. Computer Networks, 181, 107530.
Bozanic, D., Tešić, D., Marinković, D., & Milić, A. (2021). Modeling of neuro-fuzzy system as a support in decision-making processes. Reports in Mechanical Engineering, 2(1), 222-234. DOI: https://doi.org/10.31181/rme2001021222b
Ge, M., Junfeng, Z., Jinfei, W., Huiting, H., Xinghua, S., & Hongye, W. (2021). ARIMA-FSVR Hybrid Method for High-Speed Railway Passenger Traffic Forecasting. Mathematical Problems in Engineering, 2021.
Gummadi, R., & Edara, S. R. (2018). Analysis of Passenger Flow Prediction of Transit Buses Along a Route Based on Time Series. In S. C. Satapathy, J. M. R. S. Tavares, V. Bhateja, & J. R. Mohanty (Eds.), Information and Decision Sciences (pp. 31–37). Springer. https://doi.org/10.1007/978-981-10-7563-6_4 DOI: https://doi.org/10.1007/978-981-10-7563-6_4
Guo, X., Grushka-Cockayne, Y., & De Reyck, B. (2021). Forecasting Airport Transfer Passenger Flow Using Real-Time Data and Machine Learning. Manufacturing & Service Operations Management. https://doi.org/10.1287/msom.2021.0975
Hayadi, B. H., Kim, J.-M., Hulliyah, K., & Sukmana, H. T. (2021). Predicting Airline Passenger Satisfaction with Classification Algorithms. International Journal of Informatics and Information Systems, 4(1), 82–94.
In Public Transportation in Istanbul. (2021). https://iett.istanbul/en/main/pages/public-transportation-in-istanbul/316, Accessed 13 January 2022.
Jackson, M. D., Leung, C. K., Mbacke, M. D. B., & Cuzzocrea, A. (2021). A Bayesian framework for supporting predictive analytics over big transportation data. 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC), 332–337.
Kamandanipour, K., Yakhchali, S. H., & Tavakkoli-Moghaddam, R. (2022). Learning-based dynamic ticket pricing for passenger railway service providers. Engineering Optimization, 10(1), 1–15.
Li, W., Sui, L., Zhou, M., & Dong, H. (2021). Short-term passenger flow forecast for urban rail transit based on multi-source data. EURASIP Journal on Wireless Communications and Networking, 2021(1), 1–13.
Li, X., Zhang, Y., Du, M., & Yang, J. (2020). The forecasting of passenger demand under hybrid ridesharing service modes: A combined model based on WT-FCBF-LSTM. Sustainable Cities and Society, 62, 102419.
Liu, W., Tan, Q., & Wu, W. (2020a). Forecast and early warning of regional bus passenger flow based on machine learning. Mathematical Problems in Engineering, 2020, 6625435, https://doi.org/10.1155/2020/6625435.
Liu, W., Tan, Q., & Wu, W. (2020b). Forecast and Early Warning of Regional Bus Passenger Flow Based on Machine Learning. Mathematical Problems in Engineering, 2020, e6625435. https://doi.org/10.1155/2020/6625435.
Messinis, S., & Vosniakos, G. C. (2020). An agent-based flexible manufacturing system controller with Petri-net enabled algebraic deadlock avoidance. Reports in Mechanical Engineering, 1(1), 77-92. DOI: https://doi.org/10.31181/rme200101077m
Milenković, M., Švadlenka, L., Melichar, V., Bojović, N., & Avramović, Z. (2018). Sarima Modelling Approach For Railway Passenger Flow Forecasting. Transport, 33(5), 1113–1120.
Müller-Hannemann, M., Rückert, R., Schiewe, A., & Schöbel, A. (2022). Estimating the robustness of public transport schedules using machine learning. Transportation Research Part C: Emerging Technologies, 137, 103566. https://doi.org/10.1016/j.trc.2022.103566
Ni, M., He, Q., & Gao, J. (2017). Forecasting the Subway Passenger Flow Under Event Occurrences With Social Media. IEEE Transactions on Intelligent Transportation Systems, 18(6), 1623–1632. https://doi.org/10.1109/TITS.2016.2611644 DOI: https://doi.org/10.1109/TITS.2016.2611644
Pamucar, D., Deveci, M., Canıtez, F., & Bozanic, D. (2020). A fuzzy Full Consistency Method-Dombi-Bonferroni model for prioritizing transportation demand management measures. Applied Soft Computing, 87, 105952. https://doi.org/10.1016/j.asoc.2019.105952
Rajendran, S., Srinivas, S., & Grimshaw, T. (2021). Predicting demand for air taxi urban aviation services using machine learning algorithms. Journal of Air Transport Management, 92, 102043. https://doi.org/10.1016/j.jairtraman.2021.102043.
Reitmann, S., & Schultz, M. (2022). An Adaptive Framework for Optimization and Prediction of Air Traffic Management (Sub-) Systems with Machine Learning. Aerospace, 9(2), 77, 1-15.
Rodríguez-Sanz, Á., de Marcos, A. F., Pérez-Castán, J. A., Comendador, F. G., Valdés, R. A., & Loreiro, Á. P. (2021). Queue behavioural patterns for passengers at airport terminals: A machine learning approach. Journal of Air Transport Management, 90, 101940. https://doi.org/10.1016/j.jairtraman.2020.101940
Roos, J., Gavin, G., & Bonnevay, S. (2017). A dynamic Bayesian network approach to forecast short-term urban rail passenger flows with incomplete data. Transportation Research Procedia, 26, 53–61. DOI: https://doi.org/10.1016/j.trpro.2017.07.008
Statista Demographies. (2020). https://www.statista.com/statistics/1101883/largest-european-cities/, Accessed 17 February 2022.
Sun, Y., Leng, B., & Guan, W. (2015). A novel wavelet-SVM short-time passenger flow prediction in Beijing subway system. Neurocomputing, 166, 109–121. DOI: https://doi.org/10.1016/j.neucom.2015.03.085
Tom Mitchell. (2006). The Discipline of Machine Learning. Pittsburgh, PA. http://ra.adm.cs.cmu.edu/anon/usr0/ftp/anon/ml/CMU-ML-06-108.pdf
Toqué, F., Khouadjia, M., Come, E., Trepanier, M., & Oukhellou, L. (2017). Short amp; long term forecasting of multimodal transport passenger flows with machine learning methods. 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), 560–566. https://doi.org/10.1109/ITSC.2017.8317939
Traffic congestion ranking | TomTom Traffic Index. (2021). https://www.tomtom.com/en_gb/traffic-index/ranking/
TUIK. (2021). https://data.tuik.gov.tr/Bulten/Index?p=Adrese-Dayal%C4%B1-N%C3%BCfus-Kay%C4%B1t-Sistemi-Sonu%C3%A7lar%C4%B1-2020-37210&dil=1, Accessed 19 February 2022.
Wang, B., Wu, P., Chen, Q., & Ni, S. (2021). Prediction and Analysis of Train Passenger Load Factor of High-Speed Railway Based on LightGBM Algorithm. Journal of Advanced Transportation, 2021, ID 9963394, https://doi.org/10.1155/2021/9963394.
Wood, J., Yu, Z., & Gayah, V. V. (2022). Development and evaluation of frameworks for real-time bus passenger occupancy prediction. International Journal of Transportation Science and Technology. https://doi.org/10.1016/j.ijtst.2022.03.005
Xie, G., Wang, S., & Lai, K. K. (2014). Short-term forecasting of air passenger by using hybrid seasonal decomposition and least squares support vector regression approaches. Journal of Air Transport Management, 37, 20–26. DOI: https://doi.org/10.1016/j.jairtraman.2014.01.009
Yang, X., Xue, Q., Yang, X., Yin, H., Qu, Y., Li, X., & Wu, J. (2021). A novel prediction model for the inbound passenger flow of urban rail transit. Information Sciences, 566, 347–363.
Ye, Y., Chen, L., & Xue, F. (2019). Passenger Flow Prediction in Bus Transportation System using ARIMA Models with Big Data. 2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 436–443. https://doi.org/10.1109/CyberC.2019.00081
Zhang, J., Shen, D., Tu, L., Zhang, F., Xu, C., Wang, Y., Tian, C., Li, X., Huang, B., & Li, Z. (2017). A Real-Time Passenger Flow Estimation and Prediction Method for Urban Bus Transit Systems. IEEE Transactions on Intelligent Transportation Systems, 18(11), 3168–3178. DOI: https://doi.org/10.1109/TITS.2017.2686877
Zheng, Z., Ling, X., Wang, P., Xiao, J., & Zhang, F. (2021). Hybrid model for predicting anomalous large passenger flow in urban metros. IET Intelligent Transport Systems, 14(14), 1987–1996.