Modelling brand choice with the multinomial logit model and artificial neural networks: A hybrid approach

1997 ◽  
Vol 14 (2) ◽  
pp. 205
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Logit Model ◽  
Hybrid Approach ◽  
Brand Choice ◽  
Multinomial Logit Model ◽  
Multinomial Logit ◽  
Artificial Neural

scholarly journals Comparison of Four Types of Artificial Neural Network and a Multinomial Logit Model for Travel Mode Choice Modeling

2018 ◽  
Vol 2672 (49) ◽  
pp. 101-112 ◽  
Author(s):  
Dongwoo Lee ◽  
Sybil Derrible ◽  
Francisco Camara Pereira
Keyword(s):  
Neural Networks ◽  
Logit Model ◽  
Travel Demand ◽  
Mode Choice ◽  
Multinomial Logit Model ◽  
Multinomial Logit ◽  
Choice Modeling ◽  
Probabilistic Neural Networks ◽  
Ann Models ◽  
Artificial Neural

Discrete choice modeling is a fundamental part of travel demand forecasting. To date, this field has been dominated by parametric approaches (e.g., logit models), but non-parametric approaches such as artificial neural networks (ANNs) possess much potential since choice problems can be assimilated to pattern recognition problems. In particular, ANN models are easily applicable with their higher capability to identify nonlinear relationships between inputs and designated outputs to predict choice behaviors. This article investigates the capability of four types of ANN model and compares their prediction performance with a conventional multinomial logit model (MNL) for mode choice problems. The four ANNs are: backpropagation neural networks (BPNNs), radial basis function networks (RBFNs), probabilistic neural networks (PNNs), and clustered probabilistic neural networks (CPNNs). To compare the modeling techniques, we present the algorithmic differences of each ANN technique, and we assess their prediction accuracy with a 10-fold cross-validation method. Furthermore, we assess the contribution of explanatory variables by conducting sensitivity analyses on significant variables. The results show that ANN models outperform MNL, with prediction accuracies around 80% compared with 70% for MNL. Moreover, PNN performs best out of all ANNs, especially to predict underrepresented modes.

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