School trip attraction modeling using neural & amp; fuzzy-neural approaches

Abstract

Trip attraction has long been considered as a major element in trip demand estimation. Many models have been presented for this purpose. Models use socio-economic variables in order to predict trip attraction. Neural networks and neuro-fuzzy systems are suitable approaches to establish proper models. This paper develops neural and fuzzy-neural models to predict school trip attraction. Neural networks are organized in different architectures and the results have been compared in order to determine the best fitting one. Then an adaptive neural fuzzy inference system (ANFIS) is used to estimate number of school trip attraction. Different models were trained, validated and tested with a real database obtained from Shiraz, a large city in Iran, and then compared with regression model made for school trip attraction in Shiraz Comprehensive Transportation Study (SCTS). The results indicate that the neural networks and fuzzy-neural systems performed more accurate than regression models.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 5 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.