Chapter
Apr 26, 2012
Applying Fuzzy Neural Networks to Predict Bus Line Patronage
Authors: Xinmiao Yang and Wei WangAuthor Affiliations
Publication: Traffic And Transportation Studies (2002)
Abstract
It is of great importance for public transit enterprise to correctly predict short-term bus line patronage. A new model for short-term bus line patronage predicting based on the theory of adaptive neural-fuzzy inference system (ANFIS) has been raised in this paper. An initial TS fuzzy forecasting model has been produced by means of adaptive learning from historical data, and a hybrid method combining BP algorithm and least square algorithm has been used to train and adjust parameters of model. Test results from this fuzzy neural model have showed better adaptability and fitness than results from AR, ARMA and ARIMA, which best satisfies practical operation needs from public transit enterprise.
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Information
Published In
Traffic And Transportation Studies (2002)
Pages: 644 - 649
Copyright
© 2002 American Society of Civil Engineers.
History
Published online: Apr 26, 2012
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ASCE Technical Topics:
- Adaptive systems
- Algorithms
- Analysis (by type)
- Artificial intelligence and machine learning
- Buses
- Computer programming
- Computing in civil engineering
- Engineering fundamentals
- Fuzzy logic
- Highway transportation
- Hybrid methods
- Infrastructure
- Least squares method
- Mathematics
- Methodology (by type)
- Neural networks
- Public transportation
- Regression analysis
- Statistical analysis (by type)
- Systems engineering
- Systems management
- Transportation engineering
- Vehicles
Authors
Affiliations
Xinmiao Yang
Doctor, Institute of Transportation Engineering, Tsinghua University, Beijing, 100084, China
Wei Wang
Professor, Transportation College, Southeast University, Nanjing, 210096, China
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Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.