Abstract
ABSTRACT Artificial Neural Networks (ANNs) are emerging classes of AI algorithms, and have seen numerousapplications in travel behavior research recently. However, thetransferability of ANN-based travel behavior models is seldom tested. A fewstudies that test transferability, merely use vanilla Feedforward NeuralNetworks. This paper evaluates the spatial transferability of two ANN-basedmodels: first, a Feedforward ANN-basedmode choice model, and next, a Long Short Term Memory (LSTM)-based activitygeneration and activity-timing model, and enhances their transferability usingtransfer learning (TL). Both the modelswere found to exhibit poor transferability in case of naïve transfer. Transferlearning resulted in significant improvements with the TL-enhanced modelsthat utilizeonly 50% of local data achieving results similar to a locallydeveloped model. Further, ANNs performed poorer when compared with nested logit(NL) models during naïve transfer. However, the TL-enhanced ANN-based modelsshowed significant improvement compared to transfer scaling enhanced NL models.
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