Travel Mode Choice Analysis Using Support Vector Machines

Abstract

Travel mode choice forecasts receive wide attention in travel behavior analysis. Most traditional mode choice models are based on the principle of random utility maximization. Alternatively, mode choice is a pattern recognition problem, where different human behavior determines the choices among travel mode alternatives. In this study a new artificial intelligence model, support vector machine, is applied to travel mode choice modeling. The support vector machine model is tested and compared with a nested logit model and a multilayer feed forward neural networks model in terms of both fitting and testing results. The analysis of actual investigation data shows that the model has fast convergence and high precision, which is of great importance for travel mode choice prediction.

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 — 23 citations exist upstream.

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