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.
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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).
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© 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
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