Research Article
BibTex RIS Cite

A Comparative Analysis of Passenger Flow Forecasting in Trams Using Machine Learning Algorithms

Year 2024, , 1 - 14, 24.03.2024
https://doi.org/10.17798/bitlisfen.1292003

Abstract

Forecasting tram passenger flow is an important part of the intelligent transportation system since it helps with resource allocation, network design, and frequency setting. Due to varying destinations and departure times, it is difficult to notice large fluctuations, non-linearity, and periodicity of tram passenger flows. In this paper, the first-order difference technique is used to eliminate seasonal structure from the time series data and the performance of different machine learning algorithms on passenger flow forecasting in trams is evaluated. Furthermore, the impact of the Covid-19 pandemic on forecasting success is examined. For this purpose, the tram data of Kayseri Transportation Inc. for the years 2018-2021 are used. Different estimation models including Linear Regression, Support Vector Regression, Random Forest, Artificial Neural Network, Convolutional Neural Network, and LongTerm Short Memory are applied and the time series forecasting performances of the models are evaluated with MAPE and R2 metrics.

References

  • [1] D. Li et al., “Percolation transition in dynamical traffic network with evolving critical bottlenecks,” Proceedings of the National Academy of Sciences, vol. 112, no. 3, pp. 669–672, 2014. doi:10.1073/pnas.1419185112.
  • [2] M. Ni, Q. He, and J. Gao, “Forecasting the subway passenger flow under event occurrences with social media,” IEEE Transactions on Intelligent Transportation Systems, pp. 1–10, 2016. doi:10.1109/tits.2016.2611644.
  • [3] J. Yu, “Short-Term Airline Passenger Flow Prediction Based on the Attention Mechanism and Gated Recurrent Unit Model,” Cognitive Computation, vol. 14, no. 2, pp. 693–701, 2022. doi:10.1007/s12559-021-09991-x .
  • [4] A. Kanavos, F. Kounelis, L. Iliadis, and C. Makris, “Deep Learning Models for Forecasting Aviation Demand Time Series,” Neural Computing and Applications, vol. 33, no. 23, pp. 16329–16343, 2021. doi:10.1007/s00521-021-06232-y
  • [5] C. Li, X. Wang, Z. Cheng, and Y. Bai, “Forecasting Bus Passenger Flows by Using a Clustering-Based Support Vector Regression Approach,” IEEE Access, vol. 8, pp. 19717–19725, 2020, doi: 10.1109/access.2020.2967867.
  • [6] S. Anvari, S. Tuna, M. Canci, and M. Turkay, “Automated Box–Jenkins Forecasting Tool With an Application for Passenger Demand in Urban Rail Systems,” Journal of Advanced Transportation, vol. 50, no. 1, pp. 25–49, Sep. 2015, doi: 10.1002/atr.1332.
  • [7] A. Samagaio and M. Wolters, “Comparative Analysis of Government Forecasts for the Lisbon Airport,” Journal of Air Transport Management, vol. 16, no. 4, pp. 213–217, Jul. 2010, doi: 10.1016/j.jairtraman.2009.09.002.
  • [8] M. Milenković, L. Švadlenka, V. Melichar, N. Bojović, and Z. Avramović, “Sarima Modelling Approach for Railway Passenger Flow Forecasting,” Transport, pp. 1–8, Mar. 2016, doi: 10.3846/16484142.2016.1139623.
  • [9] X. Xiao, J. Yang, S. Mao, and J. Wen, “An Improved Seasonal Rolling Grey Forecasting Model Using a Cycle Truncation Accumulated Generating Operation for Traffic Flow,” Applied Mathematical Modelling, vol. 51, pp. 386–404, Nov. 2017, doi: https://doi.org/10.1016/j.apm.2017.07.010.
  • [10] A. Stathopoulos and M. G. Karlaftis, “A Multivariate State Space Approach for Urban Traffic Flow Modeling and Prediction,” Transportation Research Part C: Emerging Technologies, vol. 11, no. 2, pp. 121–135, Apr. 2003, doi: https://doi.org/10.1016/s0968-090x(03)00004-4.
  • [11] X. Tang and G. Deng, “Prediction of Civil Aviation Passenger Transportation Based on ARIMA Model,” Open Journal of Statistics, vol. 06, no. 05, pp. 824–834, 2016, doi: https://doi.org/10.4236/ojs.2016.65068.
  • [12] N. K. Chauhan and K. Singh, “A Review on Conventional Machine Learning vs Deep Learning,” in 2018 International Conference on Computing, Power and Communication Technologies (GUCON), Sep. 2018, doi: https://doi.org/10.1109/gucon.2018.8675097.
  • [13] Y. Park, Y. Choi, K. Kim, and J. K. Yoo, “Machine Learning Approach for Study on Subway Passenger Flow,” Scientific Reports, vol. 12, no. 1, p. 2754, Feb. 2022, doi: https://doi.org/10.1038/s41598-022-06767-7.
  • [14] F. Moretti, S. Pizzuti, S. Panzieri, and M. Annunziato, “Urban Traffic Flow Forecasting Through Statistical and Neural Network Bagging Ensemble Hybrid Modeling,” Neurocomputing, vol. 167, pp. 3–7, Nov. 2015, doi: https://doi.org/10.1016/j.neucom.2014.08.100.
  • [15] B. Sun, W. Cheng, P. Goswami, and G. Bai, “Short-Term Traffic Forecasting using Self-Adjusting K-Nearest Neighbours,” IET Intelligent Transport Systems, vol. 12, no. 1, pp. 41–48, Feb. 2018, doi: https://doi.org/10.1049/iet-its.2016.0263.
  • [16] J. Guo, W. Huang, and B. M. Williams, “Adaptive Kalman Filter Approach for Stochastic Short-Term Traffic Flow Rate Prediction and Uncertainty Quantification,” Transportation Research Part C: Emerging Technologies, vol. 43, pp. 50–64, Jun. 2014, doi: https://doi.org/10.1016/j.trc.2014.02.006.
  • [17] Y. Sun, B. Leng, and W. Guan, “A Novel Wavelet-Svm Short-Time Passenger Flow Prediction in Beijing Subway System,” Neurocomputing, vol. 166, pp. 109–121, Oct. 2015, doi: https://doi.org/10.1016/j.neucom.2015.03.085.
  • [18] L. Li, X. Chen, and L. Zhang, “Multimodel Ensemble for Freeway Traffic State Estimations,” IEEE Transactions on Intelligent Transportation Systems, vol. 15, no. 3, pp. 1323–1336, Jun. 2014, doi: https://doi.org/10.1109/tits.2014.2299542.
  • [19] J. Zhang, F. Chen, Z. Cui, Y. Guo, and Y. Zhu, “Deep Learning Architecture for Short-Term Passenger Flow Forecasting in Urban Rail Transit,” IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 11, pp. 7004–7014, Nov. 2021, doi: https://doi.org/10.1109/tits.2020.3000761.
  • [20] X. Feng, T. Gan, and X. Wang, “Feedback Analysis of Interaction between Urban Densities and Travel Mode Split,” International Journal of Simulation Modelling, vol. 14, no. 2, pp. 349–358, Jun. 2015, doi: 10.2507/ijsimm14(2)co9.
  • [21] Y. Wei and M.-C. Chen, “Forecasting The Short-Term Metro Passenger Flow with Empirical Mode Decomposition and Neural Networks,” Transportation Research Part C: Emerging Technologies, vol. 21, no. 1, pp. 148–162, Apr. 2012, doi: https://doi.org/10.1016/j.trc.2011.06.009.
  • [22] X. Ma, Z. Tao, Y. Wang, H. Yu, and Y. Wang, “Long Short-Term Memory Neural Network for Traffic Speed Prediction Using Remote Microwave Sensor Data,” Transportation Research Part C: Emerging Technologies, vol. 54, pp. 187–197, May 2015, doi: https://doi.org/10.1016/j.trc.2015.03.014.
  • [23] P. He, “Optimization and Simulation of Remanufacturing Production Scheduling under Uncertainties,” International Journal of Simulation Modelling, vol. 17, no. 4, pp. 734–743, Dec. 2018, doi: https://doi.org/10.2507/ijsimm17(4)co20.
  • [24] M. Castro-Neto, Y.-S. Jeong, M.-K. Jeong, and L. D. Han, “Online-Svr for Short-Term Traffic Flow Prediction under Typical and Atypical Traffic Conditions,” Expert Systems with Applications, vol. 36, no. 3, pp. 6164–6173, Apr. 2009, doi: https://doi.org/10.1016/j.eswa.2008.07.069.
  • [25] G. Xie, S. Wang, Y. Zhao, and K. K. Lai, “Hybrid Approaches Based on Lssvr Model for Container Throughput Forecasting: A Comparative Study,” Applied Soft Computing, vol. 13, no. 5, pp. 2232–2241, May 2013, doi: https://doi.org/10.1016/j.asoc.2013.02.002.
  • [26] X. Wang, N. Zhang, Y. Zhang, and Z. Shi, “Forecasting of Short-Term Metro Ridership with Support Vector Machine Online Model,” Journal of Advanced Transportation, vol. 2018, pp. 1–13, Jun. 2018, doi: https://doi.org/10.1155/2018/3189238.
  • [27] A. Singhal, C. Kamga, and A. Yazici, “Impact of Weather on Urban Transit Ridership,” Transportation Research Part A: Policy and Practice, vol. 69, pp. 379–391, Nov. 2014, doi: https://doi.org/10.1016/j.tra.2014.09.008.
  • [28] A. Koesdwiady, R. Soua, and F. Karray, “Improving Traffic Flow Prediction With Weather Information in Connected Cars: A Deep Learning Approach,” IEEE Transactions on Vehicular Technology, vol. 65, no. 12, pp. 9508–9517, Dec. 2016, doi: https://doi.org/10.1109/tvt.2016.2585575.
  • [29] C. Li, R.-V. Sanchez, G. Zurita, M. Cerrada, D. Cabrera, and R. E. Vásquez, “Multimodal Deep Support Vector Classification with Homologous Features and Its Application to Gearbox Fault Diagnosis,” Neurocomputing, vol. 168, pp. 119–127, Nov. 2015, doi: https://doi.org/10.1016/j.neucom.2015.06.008
  • [30] M.-L. Huang, “Intersection Traffic Flow Forecasting Based on Ν-Gsvr with a New Hybrid Evolutionary Algorithm,” Neurocomputing, vol. 147, pp. 343–349, Jan. 2015, doi: https://doi.org/10.1016/j.neucom.2014.06.054
  • [31] Y. Zhang and Y. Liu, “Analysis of Peak and Non-Peak Traffic Forecasts Using Combined Models,” Journal of Advanced Transportation, vol. 45, no. 1, pp. 21–37, Sep. 2010, doi: https://doi.org/10.1002/atr.128.
  • [32] M. Khashei and M. Bijari, “A Novel Hybridization of Artificial Neural Networks and Arıma Models for Time Series Forecasting,” Applied Soft Computing, vol. 11, no. 2, pp. 2664–2675, Mar. 2011, doi: https://doi.org/10.1016/j.asoc.2010.10.015.
  • [33] T. Pant, C. Han, and H. Wang, “Examination of Errors of Table Integration in Flamelet/Progress Variable Modeling of a Turbulent Non-Premixed Jet Flame,” Applied Mathematical Modelling, vol. 72, pp. 369–384, Aug. 2019, doi: https://doi.org/10.1016/j.apm.2019.03.016.
  • [34] X. Wang, L. Huang, H. Huang, B. Li, Z. Xia, and J. Li, “An Ensemble Learning Model for Short-Term Passenger Flow Prediction,” Complexity, vol. 2020, pp. 1–13, Dec. 2020, doi: https://doi.org/10.1155/2020/6694186.
  • [35] H. Li, J. Bai, and Y. Li, “A Novel Secondary Decomposition Learning Paradigm with Kernel Extreme Learning Machine for Multi-Step Forecasting of Container Throughput,” Physica A: Statistical Mechanics and its Applications, vol. 534, p. 122025, Nov. 2019, doi: https://doi.org/10.1016/j.physa.2019.122025.
  • [36] M. Niu, Y. Hu, S. Sun, and Y. Liu, “A Novel Hybrid Decomposition-Ensemble Model Based on Vmd and Hgwo for Container Throughput Forecasting,” Applied Mathematical Modelling, vol. 57, pp. 163–178, May 2018, doi: https://doi.org/10.1016/j.apm.2018.01.014.
  • [37] Y. Li and C. Ma, “Short-Time Bus Route Passenger Flow Prediction Based on a Secondary Decomposition Integration Method,” Journal of Transportation Engineering, Part A: Systems, vol. 149, no. 2, Feb. 2023, doi: https://doi.org/10.1061/jtepbs.teeng-7496.
  • [38] H. Nguyen, L. Kieu, T. Wen, and C. Cai, “Deep Learning Methods in Transportation Domain: A Review,” IET Intelligent Transport Systems, vol. 12, no. 9, pp. 998–1004, Jul. 2018, doi: https://doi.org/10.1049/iet-its.2018.0064.
  • [39] H. Gao, X. Qin, R. J. D. Barroso, W. Hussain, Y. Xu, and Y. Yin, “Collaborative Learning-Based Industrial IoT API Recommendation for Software-Defined Devices: The Implicit Knowledge Discovery Perspective,” IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 6, no. 1, pp. 66–76, Feb. 2022, doi: https://doi.org/10.1109/TETCI.2020.3023155.
  • [40] H. Gao, Y. Zhang, H. Miao, R. J. D. Barroso, and X. Yang, “SDTIOA: Modeling the Timed Privacy Requirements of IoT Service Composition: A User Interaction Perspective for Automatic Transformation from BPEL to Timed Automata,” Mobile Networks and Applications, vol. 26, no. 6, pp. 2272–2297, Nov. 2021, doi: https://doi.org/10.1007/s11036-021-01846-x.
  • [41] A. A. Amer, I. E. Talkhan, R. Ahmed, and T. Ismail, “An Optimized Collaborative Scheduling Algorithm for Prioritized Tasks with Shared Resources in Mobile-Edge and Cloud Computing Systems,” Mobile Networks and Applications, Apr. 2022, doi: https://doi.org/10.1007/s11036-022-01974-y.
  • [42] X. Ma, H. Xu, H. Gao, and M. Bian, “Real-time Multiple-Workflow Scheduling in Cloud Environment,” Research Square, Feb. 2021, Published, doi: 10.21203/rs.3.rs-170491/v1.
  • [43] K. Peng, W. Bai, and L. WU, “Passenger Flow Forecast of Railway Station Based on Improved Lstm,” 2020 2nd International Conference on Advances in Computer Technology, Information Science and Communications (CTISC), Mar. 2020, doi: https://doi.org/10.1109/ctisc49998.2020.00033.
  • [44] Dedeturk, B.K.: Dataset. https://raw.githubusercontent.com/kagandedeturk/TimeSeries/main/Tramvay.csv (accessed May 12, 2023).
  • [45] W. Xu, H. Peng, X. Zeng, F. Zhou, X. Tian, and X. Peng, “A Hybrid Modelling Method for Time Series Forecasting Based nn a Linear Regression Model and Deep Learning,” Applied Intelligence, vol. 49, no. 8, pp. 3002–3015, Feb. 2019, doi: https://doi.org/10.1007/s10489-019-01426-3.
  • [46] Y. Bai et al., “A Comparison of Dimension Reduction Techniques for Support Vector Machine Modeling of Multi-Parameter Manufacturing Quality Prediction,” Journal of Intelligent Manufacturing, vol. 30, no. 5, pp. 2245–2256, Jan. 2018, doi: https://doi.org/10.1007/s10845-017-1388-1.
  • [47] X. Qiu, L. Zhang, P. Nagaratnam Suganthan, and G. A. J. Amaratunga, “Oblique Random Forest Ensemble via Least Square Estimation for Time Series Forecasting,” Information Sciences, vol. 420, pp. 249–262, Dec. 2017, doi: https://doi.org/10.1016/j.ins.2017.08.060.
  • [48] T. Xiong, Y. Bao, and Z. Hu, “Beyond One-Step-Ahead Forecasting: Evaluation of Alternative Multi-Step-Ahead Forecasting Models for Crude Oil Prices,” Energy Economics, vol. 40, pp. 405–415, Nov. 2013, doi: https://doi.org/10.1016/j.eneco.2013.07.028.
  • [49] H. I. Kazıcı, S. Kosunalp, and M. Arucu, “Its-Pro-Flow: A New Enhanced Short-Term Traffıc Flow Prediction For Intelligent Transportation Systems,” Scientific Journal of Silesian University of Technology. Series Transport, vol. 120, pp. 117–136, Sep. 2023, doi: 10.20858/sjsutst.2023.120.8.
Year 2024, , 1 - 14, 24.03.2024
https://doi.org/10.17798/bitlisfen.1292003

Abstract

References

  • [1] D. Li et al., “Percolation transition in dynamical traffic network with evolving critical bottlenecks,” Proceedings of the National Academy of Sciences, vol. 112, no. 3, pp. 669–672, 2014. doi:10.1073/pnas.1419185112.
  • [2] M. Ni, Q. He, and J. Gao, “Forecasting the subway passenger flow under event occurrences with social media,” IEEE Transactions on Intelligent Transportation Systems, pp. 1–10, 2016. doi:10.1109/tits.2016.2611644.
  • [3] J. Yu, “Short-Term Airline Passenger Flow Prediction Based on the Attention Mechanism and Gated Recurrent Unit Model,” Cognitive Computation, vol. 14, no. 2, pp. 693–701, 2022. doi:10.1007/s12559-021-09991-x .
  • [4] A. Kanavos, F. Kounelis, L. Iliadis, and C. Makris, “Deep Learning Models for Forecasting Aviation Demand Time Series,” Neural Computing and Applications, vol. 33, no. 23, pp. 16329–16343, 2021. doi:10.1007/s00521-021-06232-y
  • [5] C. Li, X. Wang, Z. Cheng, and Y. Bai, “Forecasting Bus Passenger Flows by Using a Clustering-Based Support Vector Regression Approach,” IEEE Access, vol. 8, pp. 19717–19725, 2020, doi: 10.1109/access.2020.2967867.
  • [6] S. Anvari, S. Tuna, M. Canci, and M. Turkay, “Automated Box–Jenkins Forecasting Tool With an Application for Passenger Demand in Urban Rail Systems,” Journal of Advanced Transportation, vol. 50, no. 1, pp. 25–49, Sep. 2015, doi: 10.1002/atr.1332.
  • [7] A. Samagaio and M. Wolters, “Comparative Analysis of Government Forecasts for the Lisbon Airport,” Journal of Air Transport Management, vol. 16, no. 4, pp. 213–217, Jul. 2010, doi: 10.1016/j.jairtraman.2009.09.002.
  • [8] M. Milenković, L. Švadlenka, V. Melichar, N. Bojović, and Z. Avramović, “Sarima Modelling Approach for Railway Passenger Flow Forecasting,” Transport, pp. 1–8, Mar. 2016, doi: 10.3846/16484142.2016.1139623.
  • [9] X. Xiao, J. Yang, S. Mao, and J. Wen, “An Improved Seasonal Rolling Grey Forecasting Model Using a Cycle Truncation Accumulated Generating Operation for Traffic Flow,” Applied Mathematical Modelling, vol. 51, pp. 386–404, Nov. 2017, doi: https://doi.org/10.1016/j.apm.2017.07.010.
  • [10] A. Stathopoulos and M. G. Karlaftis, “A Multivariate State Space Approach for Urban Traffic Flow Modeling and Prediction,” Transportation Research Part C: Emerging Technologies, vol. 11, no. 2, pp. 121–135, Apr. 2003, doi: https://doi.org/10.1016/s0968-090x(03)00004-4.
  • [11] X. Tang and G. Deng, “Prediction of Civil Aviation Passenger Transportation Based on ARIMA Model,” Open Journal of Statistics, vol. 06, no. 05, pp. 824–834, 2016, doi: https://doi.org/10.4236/ojs.2016.65068.
  • [12] N. K. Chauhan and K. Singh, “A Review on Conventional Machine Learning vs Deep Learning,” in 2018 International Conference on Computing, Power and Communication Technologies (GUCON), Sep. 2018, doi: https://doi.org/10.1109/gucon.2018.8675097.
  • [13] Y. Park, Y. Choi, K. Kim, and J. K. Yoo, “Machine Learning Approach for Study on Subway Passenger Flow,” Scientific Reports, vol. 12, no. 1, p. 2754, Feb. 2022, doi: https://doi.org/10.1038/s41598-022-06767-7.
  • [14] F. Moretti, S. Pizzuti, S. Panzieri, and M. Annunziato, “Urban Traffic Flow Forecasting Through Statistical and Neural Network Bagging Ensemble Hybrid Modeling,” Neurocomputing, vol. 167, pp. 3–7, Nov. 2015, doi: https://doi.org/10.1016/j.neucom.2014.08.100.
  • [15] B. Sun, W. Cheng, P. Goswami, and G. Bai, “Short-Term Traffic Forecasting using Self-Adjusting K-Nearest Neighbours,” IET Intelligent Transport Systems, vol. 12, no. 1, pp. 41–48, Feb. 2018, doi: https://doi.org/10.1049/iet-its.2016.0263.
  • [16] J. Guo, W. Huang, and B. M. Williams, “Adaptive Kalman Filter Approach for Stochastic Short-Term Traffic Flow Rate Prediction and Uncertainty Quantification,” Transportation Research Part C: Emerging Technologies, vol. 43, pp. 50–64, Jun. 2014, doi: https://doi.org/10.1016/j.trc.2014.02.006.
  • [17] Y. Sun, B. Leng, and W. Guan, “A Novel Wavelet-Svm Short-Time Passenger Flow Prediction in Beijing Subway System,” Neurocomputing, vol. 166, pp. 109–121, Oct. 2015, doi: https://doi.org/10.1016/j.neucom.2015.03.085.
  • [18] L. Li, X. Chen, and L. Zhang, “Multimodel Ensemble for Freeway Traffic State Estimations,” IEEE Transactions on Intelligent Transportation Systems, vol. 15, no. 3, pp. 1323–1336, Jun. 2014, doi: https://doi.org/10.1109/tits.2014.2299542.
  • [19] J. Zhang, F. Chen, Z. Cui, Y. Guo, and Y. Zhu, “Deep Learning Architecture for Short-Term Passenger Flow Forecasting in Urban Rail Transit,” IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 11, pp. 7004–7014, Nov. 2021, doi: https://doi.org/10.1109/tits.2020.3000761.
  • [20] X. Feng, T. Gan, and X. Wang, “Feedback Analysis of Interaction between Urban Densities and Travel Mode Split,” International Journal of Simulation Modelling, vol. 14, no. 2, pp. 349–358, Jun. 2015, doi: 10.2507/ijsimm14(2)co9.
  • [21] Y. Wei and M.-C. Chen, “Forecasting The Short-Term Metro Passenger Flow with Empirical Mode Decomposition and Neural Networks,” Transportation Research Part C: Emerging Technologies, vol. 21, no. 1, pp. 148–162, Apr. 2012, doi: https://doi.org/10.1016/j.trc.2011.06.009.
  • [22] X. Ma, Z. Tao, Y. Wang, H. Yu, and Y. Wang, “Long Short-Term Memory Neural Network for Traffic Speed Prediction Using Remote Microwave Sensor Data,” Transportation Research Part C: Emerging Technologies, vol. 54, pp. 187–197, May 2015, doi: https://doi.org/10.1016/j.trc.2015.03.014.
  • [23] P. He, “Optimization and Simulation of Remanufacturing Production Scheduling under Uncertainties,” International Journal of Simulation Modelling, vol. 17, no. 4, pp. 734–743, Dec. 2018, doi: https://doi.org/10.2507/ijsimm17(4)co20.
  • [24] M. Castro-Neto, Y.-S. Jeong, M.-K. Jeong, and L. D. Han, “Online-Svr for Short-Term Traffic Flow Prediction under Typical and Atypical Traffic Conditions,” Expert Systems with Applications, vol. 36, no. 3, pp. 6164–6173, Apr. 2009, doi: https://doi.org/10.1016/j.eswa.2008.07.069.
  • [25] G. Xie, S. Wang, Y. Zhao, and K. K. Lai, “Hybrid Approaches Based on Lssvr Model for Container Throughput Forecasting: A Comparative Study,” Applied Soft Computing, vol. 13, no. 5, pp. 2232–2241, May 2013, doi: https://doi.org/10.1016/j.asoc.2013.02.002.
  • [26] X. Wang, N. Zhang, Y. Zhang, and Z. Shi, “Forecasting of Short-Term Metro Ridership with Support Vector Machine Online Model,” Journal of Advanced Transportation, vol. 2018, pp. 1–13, Jun. 2018, doi: https://doi.org/10.1155/2018/3189238.
  • [27] A. Singhal, C. Kamga, and A. Yazici, “Impact of Weather on Urban Transit Ridership,” Transportation Research Part A: Policy and Practice, vol. 69, pp. 379–391, Nov. 2014, doi: https://doi.org/10.1016/j.tra.2014.09.008.
  • [28] A. Koesdwiady, R. Soua, and F. Karray, “Improving Traffic Flow Prediction With Weather Information in Connected Cars: A Deep Learning Approach,” IEEE Transactions on Vehicular Technology, vol. 65, no. 12, pp. 9508–9517, Dec. 2016, doi: https://doi.org/10.1109/tvt.2016.2585575.
  • [29] C. Li, R.-V. Sanchez, G. Zurita, M. Cerrada, D. Cabrera, and R. E. Vásquez, “Multimodal Deep Support Vector Classification with Homologous Features and Its Application to Gearbox Fault Diagnosis,” Neurocomputing, vol. 168, pp. 119–127, Nov. 2015, doi: https://doi.org/10.1016/j.neucom.2015.06.008
  • [30] M.-L. Huang, “Intersection Traffic Flow Forecasting Based on Ν-Gsvr with a New Hybrid Evolutionary Algorithm,” Neurocomputing, vol. 147, pp. 343–349, Jan. 2015, doi: https://doi.org/10.1016/j.neucom.2014.06.054
  • [31] Y. Zhang and Y. Liu, “Analysis of Peak and Non-Peak Traffic Forecasts Using Combined Models,” Journal of Advanced Transportation, vol. 45, no. 1, pp. 21–37, Sep. 2010, doi: https://doi.org/10.1002/atr.128.
  • [32] M. Khashei and M. Bijari, “A Novel Hybridization of Artificial Neural Networks and Arıma Models for Time Series Forecasting,” Applied Soft Computing, vol. 11, no. 2, pp. 2664–2675, Mar. 2011, doi: https://doi.org/10.1016/j.asoc.2010.10.015.
  • [33] T. Pant, C. Han, and H. Wang, “Examination of Errors of Table Integration in Flamelet/Progress Variable Modeling of a Turbulent Non-Premixed Jet Flame,” Applied Mathematical Modelling, vol. 72, pp. 369–384, Aug. 2019, doi: https://doi.org/10.1016/j.apm.2019.03.016.
  • [34] X. Wang, L. Huang, H. Huang, B. Li, Z. Xia, and J. Li, “An Ensemble Learning Model for Short-Term Passenger Flow Prediction,” Complexity, vol. 2020, pp. 1–13, Dec. 2020, doi: https://doi.org/10.1155/2020/6694186.
  • [35] H. Li, J. Bai, and Y. Li, “A Novel Secondary Decomposition Learning Paradigm with Kernel Extreme Learning Machine for Multi-Step Forecasting of Container Throughput,” Physica A: Statistical Mechanics and its Applications, vol. 534, p. 122025, Nov. 2019, doi: https://doi.org/10.1016/j.physa.2019.122025.
  • [36] M. Niu, Y. Hu, S. Sun, and Y. Liu, “A Novel Hybrid Decomposition-Ensemble Model Based on Vmd and Hgwo for Container Throughput Forecasting,” Applied Mathematical Modelling, vol. 57, pp. 163–178, May 2018, doi: https://doi.org/10.1016/j.apm.2018.01.014.
  • [37] Y. Li and C. Ma, “Short-Time Bus Route Passenger Flow Prediction Based on a Secondary Decomposition Integration Method,” Journal of Transportation Engineering, Part A: Systems, vol. 149, no. 2, Feb. 2023, doi: https://doi.org/10.1061/jtepbs.teeng-7496.
  • [38] H. Nguyen, L. Kieu, T. Wen, and C. Cai, “Deep Learning Methods in Transportation Domain: A Review,” IET Intelligent Transport Systems, vol. 12, no. 9, pp. 998–1004, Jul. 2018, doi: https://doi.org/10.1049/iet-its.2018.0064.
  • [39] H. Gao, X. Qin, R. J. D. Barroso, W. Hussain, Y. Xu, and Y. Yin, “Collaborative Learning-Based Industrial IoT API Recommendation for Software-Defined Devices: The Implicit Knowledge Discovery Perspective,” IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 6, no. 1, pp. 66–76, Feb. 2022, doi: https://doi.org/10.1109/TETCI.2020.3023155.
  • [40] H. Gao, Y. Zhang, H. Miao, R. J. D. Barroso, and X. Yang, “SDTIOA: Modeling the Timed Privacy Requirements of IoT Service Composition: A User Interaction Perspective for Automatic Transformation from BPEL to Timed Automata,” Mobile Networks and Applications, vol. 26, no. 6, pp. 2272–2297, Nov. 2021, doi: https://doi.org/10.1007/s11036-021-01846-x.
  • [41] A. A. Amer, I. E. Talkhan, R. Ahmed, and T. Ismail, “An Optimized Collaborative Scheduling Algorithm for Prioritized Tasks with Shared Resources in Mobile-Edge and Cloud Computing Systems,” Mobile Networks and Applications, Apr. 2022, doi: https://doi.org/10.1007/s11036-022-01974-y.
  • [42] X. Ma, H. Xu, H. Gao, and M. Bian, “Real-time Multiple-Workflow Scheduling in Cloud Environment,” Research Square, Feb. 2021, Published, doi: 10.21203/rs.3.rs-170491/v1.
  • [43] K. Peng, W. Bai, and L. WU, “Passenger Flow Forecast of Railway Station Based on Improved Lstm,” 2020 2nd International Conference on Advances in Computer Technology, Information Science and Communications (CTISC), Mar. 2020, doi: https://doi.org/10.1109/ctisc49998.2020.00033.
  • [44] Dedeturk, B.K.: Dataset. https://raw.githubusercontent.com/kagandedeturk/TimeSeries/main/Tramvay.csv (accessed May 12, 2023).
  • [45] W. Xu, H. Peng, X. Zeng, F. Zhou, X. Tian, and X. Peng, “A Hybrid Modelling Method for Time Series Forecasting Based nn a Linear Regression Model and Deep Learning,” Applied Intelligence, vol. 49, no. 8, pp. 3002–3015, Feb. 2019, doi: https://doi.org/10.1007/s10489-019-01426-3.
  • [46] Y. Bai et al., “A Comparison of Dimension Reduction Techniques for Support Vector Machine Modeling of Multi-Parameter Manufacturing Quality Prediction,” Journal of Intelligent Manufacturing, vol. 30, no. 5, pp. 2245–2256, Jan. 2018, doi: https://doi.org/10.1007/s10845-017-1388-1.
  • [47] X. Qiu, L. Zhang, P. Nagaratnam Suganthan, and G. A. J. Amaratunga, “Oblique Random Forest Ensemble via Least Square Estimation for Time Series Forecasting,” Information Sciences, vol. 420, pp. 249–262, Dec. 2017, doi: https://doi.org/10.1016/j.ins.2017.08.060.
  • [48] T. Xiong, Y. Bao, and Z. Hu, “Beyond One-Step-Ahead Forecasting: Evaluation of Alternative Multi-Step-Ahead Forecasting Models for Crude Oil Prices,” Energy Economics, vol. 40, pp. 405–415, Nov. 2013, doi: https://doi.org/10.1016/j.eneco.2013.07.028.
  • [49] H. I. Kazıcı, S. Kosunalp, and M. Arucu, “Its-Pro-Flow: A New Enhanced Short-Term Traffıc Flow Prediction For Intelligent Transportation Systems,” Scientific Journal of Silesian University of Technology. Series Transport, vol. 120, pp. 117–136, Sep. 2023, doi: 10.20858/sjsutst.2023.120.8.
There are 49 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Araştırma Makalesi
Authors

Bilge Kagan Dedeturk 0000-0002-8026-5003

Beyhan Adanur Dedeturk 0000-0003-4983-2417

Ayhan Akbaş 0000-0002-6425-104X

Early Pub Date March 21, 2024
Publication Date March 24, 2024
Submission Date May 3, 2023
Acceptance Date January 26, 2024
Published in Issue Year 2024

Cite

IEEE B. K. Dedeturk, B. Adanur Dedeturk, and A. Akbaş, “A Comparative Analysis of Passenger Flow Forecasting in Trams Using Machine Learning Algorithms”, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol. 13, no. 1, pp. 1–14, 2024, doi: 10.17798/bitlisfen.1292003.



Bitlis Eren Üniversitesi
Fen Bilimleri Dergisi Editörlüğü

Bitlis Eren Üniversitesi Lisansüstü Eğitim Enstitüsü        
Beş Minare Mah. Ahmet Eren Bulvarı, Merkez Kampüs, 13000 BİTLİS        
E-posta: fbe@beu.edu.tr