抄録
The sources of errors in travel demand model output are not only from a lack of information related to the parameters that the model tries to estimate but also due to the absence of sharply defined criteria of class membership that can play important roles in human thinking, for which qualitative variables may be better representations. Fuzzy Set Theory (FST) is suggested as an approach to tackle the computation of such variables. Combined with other approaches, in this case Artificial Neural Network (NN) and Doubly Constrained Gravity (DCG), the FST is used to model intra city work trip distribution with trip length addressed as a fuzzy attribute. However, the fuzzy model tends to perform with the same level of accuracy as un-combined models. In some cases, the hybrid models have a slightly lower performance than NN and DCG. Findings from this study suggest that FST may be suitable for inter city trip, but not short trip distribution model.
