Neuro-fuzzy modelling of workers trip production

This paper attempts to introduce the application of neuro-fuzzy techniques for full time worker trip generation estimation in the Adelaide metropolitan area using household/person characteristics such as age, vehicle ownership and distance from CBD. In the last 30 years, several linear regression models have been developed for this purpose. These models’ linear structure does not seem suitable to predict highly nonlinear behaviour of urban transport systems. Consequently, intelligent modelling methods, as powerful nonlinear tools, have attracted much attention in the prediction of trip generation. In 1993, fuzzy logic and artificial neural networks were combined and neuro-fuzzy techniques emerged to model engineering systems. Since then this technique has been improved drastically and utilized to model a wide variety of complicated engineering systems. In this research, the aforementioned method is employed for modelling person/worker trip generation. After subtractive clustering, a meaningful relation between distance of residence from CBD area and workers’ trip generation was not observed in this research. The modelling was accomplished with and without this factor and this view was justified. Finally a fuzzy inference system was achieved which explains people’s behaviour with a reasonable error range. (a) For the covering record of the conference, please refer to ITRD no. E218380.


  • English

Media Info

  • Pagination: 10P (SESSION TUES 3B)
  • Monograph Title: ATRF 2009: 32nd Australasian Transport Research Forum: the growth engine: interconnecting transport performance, the economy and the environment: 29 September-1 October 2009, Auckland, New Zealand

Subject/Index Terms

Filing Info

  • Accession Number: 01153094
  • Record Type: Publication
  • Source Agency: ARRB
  • Files: ITRD, ATRI
  • Created Date: Mar 23 2010 9:15AM