TY - GEN
T1 - Developing fuzzy route choice models using neural nets
AU - Hawas, Y. E.
N1 - Publisher Copyright:
© 2003 IEEE.
PY - 2003
Y1 - 2003
N2 - The paper discusses the calibration methodology of a neuro-fuzzy logic for route choice behaviour modelling. Neuro-fuzzy refers to the trend of logics that couple the traditional fuzzy logic structure with neural nets training capabilities for knowledge base and parameters settings. The fuzzy logic accounts for the various factors of potential effect on the route choice utility perceived by the traveller. The structure of the fuzzy logic, the calibration of the membership functions, and the composition of the knowledge base are discussed in detail. Logic training is based on data extracted from a factorial experimental design model.
AB - The paper discusses the calibration methodology of a neuro-fuzzy logic for route choice behaviour modelling. Neuro-fuzzy refers to the trend of logics that couple the traditional fuzzy logic structure with neural nets training capabilities for knowledge base and parameters settings. The fuzzy logic accounts for the various factors of potential effect on the route choice utility perceived by the traveller. The structure of the fuzzy logic, the calibration of the membership functions, and the composition of the knowledge base are discussed in detail. Logic training is based on data extracted from a factorial experimental design model.
UR - http://www.scopus.com/inward/record.url?scp=84943194668&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84943194668&partnerID=8YFLogxK
U2 - 10.1109/IVS.2002.1187930
DO - 10.1109/IVS.2002.1187930
M3 - Conference contribution
AN - SCOPUS:84943194668
T3 - IEEE Intelligent Vehicles Symposium, Proceedings
SP - 71
EP - 76
BT - IEEE Intelligent Vehicles Symposium, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2002 IEEE Intelligent Vehicle Symposium, IV 2002
Y2 - 17 June 2002 through 21 June 2002
ER -