Short-Time Bus Route Passenger Flow Prediction Based on a Secondary Decomposition Integration Method
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 2
Abstract
Bus passenger flow is one of the decisive factors for the development of public transportation. Therefore, accurate prediction of real-time passenger flow on bus routes not only helps bus companies to make reasonable scheduling plans to meet the travel needs of passengers but also promotes the sound development of urban public transportation and reduces pollution. In this paper, we propose a secondary decomposition integration method that combines empirical modal decomposition (EMD), sample entropy (SE), and kernel extreme learning machine (KELM) to achieve a short-time prediction of bus route passenger flow. The EMD decomposes the original passenger flow data into several intrinsic mode functions, measures the complexity of the decomposed intrinsic mode functions using SE, and performs a secondary decomposition of the intrinsic mode functions with the highest complexity using EMD, followed by the prediction of the two decomposition results using KELM. The final predicted result is the sum of the two results. The model is verified by the real card-swiping data of two bus lines per minute. Each group of data has 300 data, with 80% of the data as the training set and the remaining 20% as the test set, which can predict the passenger flow per minute. The experimental results show that the short-term bus passenger flow forecasting method proposed in this paper has high accuracy and good robustness.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some data that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This research was funded by the National Natural Science Foundation of China Grant Nos. 52062027 and 71861023, the Program of Humanities and Social Science of Education Ministry of China Grant No. 18YJC630118, and Foundation of A Hundred Youth Talents Training Program of Lanzhou Jiaotong University. The “Double-First Class” Major Research Programs, Educational Department of Gansu Province (No. GSSYLXM-04).
References
Anvari, S., S. Tuna, M. Canci, and M. Turkay. 2016. “Automated Box–Jenkins forecasting tool with an application for passenger demand in urban rail systems.” J. Adv. Transp. 50 (1): 25–49. https://doi.org/10.1002/atr.1332.
Bai, Y., Z. Sun, B. Zeng, J. Deng, and C. Li. 2017. “A multi-pattern deep fusion model for short-term bus passenger flow forecasting.” Appl. Soft Comput. 58 (Sep): 669–680. https://doi.org/10.1016/j.asoc.2017.05.011.
Banerjee, N., A. Morton, and K. Akartunalı. 2020. “Passenger demand forecasting in scheduled transportation.” Eur. J. Oper. Res. 286 (3): 797–810. https://doi.org/10.1016/j.ejor.2019.10.032.
Chen, Q., D. Wen, X. Li, D. Chen, H. Lv, J. Zhang, and P. Gao. 2019. “Empirical mode decomposition based long short-term memory neural network forecasting model for the short-term metro passenger flow.” PLoS One 14 (9): e0222365. https://doi.org/10.1371/journal.pone.0222365.
Chen, W., Y. Chen, and W. Liu. 2016. “Ground roll attenuation using improved complete ensemble empirical mode decomposition.” J. Seismic Explor. 25 (5): 485–495.
Clark, J. 1991. “Neural network modeling.” Phys. Med. Biol. 36 (10): 1259. https://doi.org/10.1088/0031-9155/36/10/001.
Gan, M., Y. Cheng, K. Liu, and G.-L. Zhang. 2014. “Seasonal and trend time series forecasting based on a quasi-linear autoregressive model.” Appl. Soft Comput. 24 (Nov): 13–18. https://doi.org/10.1016/j.asoc.2014.06.047.
Hochreiter, S., and J. Schmidhuber. 1997. “Long short-term memory.” Neural Comput. 9 (8): 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735.
Huachun, W., Z. Jian, X. Chunhu, Z. Jiyang, and H. Yiming. 2021. “Two-dimensional time series sample entropy algorithm: Applications to rotor axis orbit feature identification.” Mech. Syst. Sig. Process. 147 (Jan): 107123. https://doi.org/10.1016/j.ymssp.2020.107123.
Huang, N. E., Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N.-C. Yen, C. C. Tung, and H. H. Liu. 1998. “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis.” Proc. R. Soc. London, Ser. A 454 (1971): 903–995. https://doi.org/10.1098/rspa.1998.0193.
Hush, D. R., and B. G. Horne. 1993. “Progress in supervised neural networks.” IEEE Signal Process Mag. 10 (1): 8–39. https://doi.org/10.1109/79.180705.
Kordanuli, B., L. Barjaktarović, L. Jeremić, and M. Alizamir. 2017. “Appraisal of artificial neural network for forecasting of economic parameters.” Physica A 465 (Jan): 515–519. https://doi.org/10.1016/j.physa.2016.08.062.
Li, C., X. Wang, Z. Cheng, and Y. Bai. 2020. “Forecasting bus passenger flows by using a clustering-based support vector regression approach.” IEEE Access 8 (Jan): 19717–19725. https://doi.org/10.1109/ACCESS.2020.2967867.
Li, H., J. Bai, and Y. Li. 2019. “A novel secondary decomposition learning paradigm with kernel extreme learning machine for multi-step forecasting of container throughput.” Physica A 534 (Nov): 122025. https://doi.org/10.1016/j.physa.2019.122025.
Mousavi, A. A., C. Zhang, S. F. Masri, and G. Gholipour. 2021. “Structural damage detection method based on the complete ensemble empirical mode decomposition with adaptive noise: A model steel truss bridge case study.” Struct. Health Monit. 21 (3): 887–912. https://doi.org/10.1177/14759217211013535.
Niu, M., Y. Hu, S. Sun, and Y. Liu. 2018. “A novel hybrid decomposition-ensemble model based on VMD and HGWO for container throughput forecasting.” Appl. Math. Modell. 57 (May): 163–178. https://doi.org/10.1016/j.apm.2018.01.014.
Nobrega, J. P., and A. L. Oliveira. 2019. “A sequential learning method with Kalman filter and extreme learning machine for regression and time series forecasting.” Neurocomputing 337 (Apr): 235–250. https://doi.org/10.1016/j.neucom.2019.01.070.
Richhariya, B., and M. Tanveer. 2018. “Eeg signal classification using universum support vector machine.” Expert Syst. Appl. 106 (Sep): 169–182. https://doi.org/10.1016/j.eswa.2018.03.053.
Sun, S., Y. Wei, K.-L. Tsui, and S. Wang. 2019. “Forecasting tourist arrivals with machine learning and internet search index.” Tourism Manage. 70 (Feb): 1–10. https://doi.org/10.1016/j.tourman.2018.07.010.
Tang, T., A. Fonzone, R. Liu, and C. Choudhury. 2021. “Multi-stage deep learning approaches to predict boarding behaviour of bus passengers.” Sustainable Cities Soc. 73 (Oct): 103111. https://doi.org/10.1016/j.scs.2021.103111.
Wang, L., Z. Wang, H. Qu, and S. Liu. 2018. “Optimal forecast combination based on neural networks for time series forecasting.” Appl. Soft Comput. 66 (May): 1–17. https://doi.org/10.1016/j.asoc.2018.02.004.
Wang, X., L. Huang, H. Huang, B. Li, Z. Xia, and J. Li. 2020. “An ensemble learning model for short-term passenger flow prediction.” Complexity 2020 (Dec): 1–13. https://doi.org/10.1155/2020/6694186.
Yoshida, M., Y. Kuroe, and T. Mori. 2005. “Models of Hopfield-type quaternion neural networks and their energy functions.” Int. J. Neural Syst. 15 (01n02): 129–135. https://doi.org/10.1142/S012906570500013X.
Zhang, H., and S.-S. He. 2018. “Analysis and comparison of permutation entropy, approximate entropy and sample entropy.” In Proc., 2018 Int. Symp. on Computer, Consumer and Control (IS3C), 209–212. New York: IEEE.
Zheng, J., M. Su, W. Ying, J. Tong, and Z. Pan. 2021. “Improved uniform phase empirical mode decomposition and its application in machinery fault diagnosis.” Measurement 179 (Jul): 109425. https://doi.org/10.1016/j.measurement.2021.109425.
Zhou, C., P. Dai, and Z. Zhang. 2015. “Passenger demand prediction on bus services.” In Proc., 2015 Int. Conf. on Green Computing and Internet of Things (ICGCIoT), 1430–1435. New York: IEEE.
Information & Authors
Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 149 • Issue 2 • February 2023
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Apr 29, 2022
Accepted: Sep 14, 2022
Published online: Nov 17, 2022
Published in print: Feb 1, 2023
Discussion open until: Apr 17, 2023
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Biological processes
- Buses
- Computer programming
- Computing in civil engineering
- Decomposition
- Engineering fundamentals
- Engineering mechanics
- Entropy methods
- Environmental engineering
- Flow measurement
- Highway transportation
- Infrastructure
- Measurement (by type)
- Passengers
- Public transportation
- Routing (transportation)
- Thermodynamics
- Traffic engineering
- Transportation engineering
- Vehicles
- Waste management
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Bilge Kagan Dedeturk, Beyhan Adanur Dedeturk, Ayhan Akbaş, A Comparative Analysis of Passenger Flow Forecasting in Trams Using Machine Learning Algorithms, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 10.17798/bitlisfen.1292003, 13, 1, (1-14), (2024).
- Taizhou Wang, Jinhua Xu, Jianghui Chen, A short term passenger flow prediction method for urban rail transit considering station classification, International Conference on Smart Transportation and City Engineering (STCE 2023), 10.1117/12.3024092, (117), (2024).
- Yulong Pei, Songmin Ran, Wanjiao Wang, Chuntong Dong, Bus-Passenger-Flow Prediction Model Based on WPD, Attention Mechanism, and Bi-LSTM, Sustainability, 10.3390/su152014889, 15, 20, (14889), (2023).
- Xianwang Li, Zhongxiang Huang, Saihu Liu, Jinxin Wu, Yuxiang Zhang, Short-Term Subway Passenger Flow Prediction Based on Time Series Adaptive Decomposition and Multi-Model Combination (IVMD-SE-MSSA), Sustainability, 10.3390/su15107949, 15, 10, (7949), (2023).
- Xubin Zhai, Yu Shen, Short-Term Bus Passenger Flow Prediction Based on Graph Diffusion Convolutional Recurrent Neural Network, Applied Sciences, 10.3390/app13084910, 13, 8, (4910), (2023).
- Changxi Ma, Yuanping Li, Wei Meng, A Review of Vehicle Speed Control Strategies, Journal of Intelligent and Connected Vehicles, 10.26599/JICV.2023.9210010, 6, 4, (190-201), (2023).
- Jaroslav Mašek, Adriana Pálková, Peter Blaho, Štefánia Halajová, Simona Jursová, Denis Šipuš, Proposal for Using IT Solutions in Public Passenger Transport in Slovak Republic to Reduce the Spread of COVID-19, LOGI – Scientific Journal on Transport and Logistics, 10.2478/logi-2023-0017, 14, 1, (181-191), (2023).
- Guojing Hu, Robert W. Whalin, Tor A. Kwembe, Weike Lu, Short-term traffic flow prediction based on secondary hybrid decomposition and deep echo state networks, Physica A: Statistical Mechanics and its Applications, 10.1016/j.physa.2023.129313, 632, (129313), (2023).
- Jinxin Wu, Deqiang He, Xianwang Li, Suiqiu He, Qin Li, Chonghui Ren, A Time Series Decomposition and Reinforcement Learning Ensemble Method for Short-Term Passenger Flow Prediction in Urban Rail Transit, Urban Rail Transit, 10.1007/s40864-023-00205-1, 9, 4, (323-351), (2023).