APPLYING FUZZY NEURAL NETWORK TO PREDICT BUS LINE PASSENGER FLOW

This paper presents a new model for predicting short term public transit passenger flow based on the theory of an adaptive neural fuzzy inference system (ANFIS). Test results from the model reveal better adaptability than auto regression (AR) and auto regression moving average (AR) techniques.

• Supplemental Notes:
  • Publication Date: 2000 Remarks: Text in Chinese with summary in English
• Authors:
  • Yang, Xin-miao
  • Wang, Wei
  • Gu, Wei-ping
  • Zhou, Min-bao
• Publication Date: 2000

Language

• Chinese

Media Info

• Pagination: p. 38-40
• Serial:
  • Kung lu chiao tung ko chi = journal of highway and transportation research and development. Vol. 17, no. 4

Subject/Index Terms

• TRT Terms: Fuzzy logic; Fuzzy systems; Neural networks; Public transit; Traffic estimation
• Subject Areas: Data and Information Technology; Public Transportation;

Filing Info

• Accession Number: 00803109
• Record Type: Publication
• Source Agency: UC Berkeley Transportation Library
• Files: PATH
• Created Date: Dec 26 2000 12:00AM