Koushik, ANP and Manoj, M and Nezamuddin, N and Prathosh, AP (2022) Testing and enhancing spatial transferability of artificial neural networks based travel behavior models. In: Transportation Letters .
Full text not available from this repository.Abstract
Artificial Neural Networks (ANNs) are emerging classes of AI algorithms, and have seen numerous applications in travel behavior research recently. However, the transferability of ANN-based travel behavior models is seldom tested. A few studies that test transferability, merely use vanilla Feedforward Neural Networks. This paper evaluates the spatial transferability of two ANN-based models: first, a Feedforward ANN-based mode choice model, and next, a Long Short Term Memory (LSTM)-based activity generation and activity-timing model, and enhances their transferability using transfer learning (TL). Both the models were found to exhibit poor transferability in case of naïve transfer. Transfer learning resulted in significant improvements with the TL-enhanced models that utilizeonly 50 of local data achieving results similar to a locally developed model. Further, ANNs performed poorer when compared with nested logit (NL) models during naïve transfer. However, the TL-enhanced ANN-based models showed significant improvement compared to transfer scaling enhanced NL models.
| Item Type: | Journal Article |
|---|---|
| Publication: | Transportation Letters |
| Publisher: | Taylor and Francis Ltd. |
| Additional Information: | The copyright for this article belongs to Taylor and Francis Ltd. |
| Keywords: | Behavioral research; Brain; Feedforward neural networks; Learning systems; Time delay; Timing circuits, Activity generation and timing model; Artificial neural-network based modeling; Long short term memory network; Machine-learning; Memory network; Network-based; Spatial transferability; Timing modeling; Transfer learning; Travel behavior modeling, Long short-term memory |
| Department/Centre: | Division of Electrical Sciences > Electrical Communication Engineering |
| Date Deposited: | 31 Oct 2022 09:10 |
| Last Modified: | 31 Oct 2022 09:10 |
| URI: | https://eprints.iisc.ac.in/id/eprint/77667 |
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