Name
Abstract | ||
|---|---|---|
Travel mode choice forecast has received wide attention in travel behavior analysis. Mode choice is a pattern recognition problem, where different human behavior patterns determine the choices among alternative travel modes. Based on the functional similarity between artificial neural networks (ANN) and decision tree, the method of knowledge-based neural networks (KBNN) combines the rule induction of decision tree (DT) and the accurate approximation of ANN. One appeal of KBNN is the use of pattern association and error correction to represent a problem. This contrasts considerably with the random utility maximization framework in discrete choice modeling. So a network built by this method and a nested logit (NL) model are specified, estimated and comparatively evaluated. The prediction results show that KBNN model demonstrates the highest performance. The analysis of actual investigation data shows that the proposed KBNN model has fast convergence and high precision, which is of great importance for travel mode choice prediction. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/NCM.2008.54 | NCM (1) |
Keywords | Field | DocType |
rule induction,functional similarity,knowledge based systems,pattern recognition,travel industry,mode choice,travel behavior analysis,knowledge-based neural networks,discrete choice modeling,pattern association,kbnn,alternative travel mode,random utility maximization,travel mode choice prediction,discrete choice analysis,ann,dt,artificial neural network,kbnn model,travel mode choice,proposed kbnn model,nl,decision tree,nested logit model,decision trees,knowledge-based neural network,neural nets,error correction,travel mode choice forecast,travel behavior,estimation,neural network,discrete choice model,artificial neural networks,predictive models,human behavior,knowledge base,transportation | Travel behavior,Convergence (routing),Decision tree,Computer science,Mode choice,Knowledge-based systems,Discrete choice,Artificial intelligence,Rule induction,Artificial neural network,Machine learning | Conference |
Volume | ISBN | Citations |
1 | 978-0-7695-3322-3 | 1 |
PageRank | References | Authors |
0.35 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianchuan Xianyu | 1 | 1 | 0.35 |
| Zhicai Juan | 2 | 4 | 1.07 |
| Linjie Gao | 3 | 4 | 0.73 |