arXiv 20 Dec 2019 · Econometrics · publishedTransportation Research Part C Emerging Technologies (2021) · 9 citations (OpenAlex)
arXiv:1912.10058 · PDF · DOI · OpenAlex · Extracted main text
This paper presents a novel deep learning-based travel behaviour choice model.Our proposed Residual Logit (ResLogit) model formulation seamlessly integrates a Deep Neural Network (DNN) architecture into a multinomial logit model. Recently, DNN models such as the Multi-layer Perceptron (MLP) and the Recurrent Neural Network (RNN) have shown remarkable success in modelling complex and noisy behavioural data. However, econometric studies have argued that machine learning techniques are a `black-box' and difficult to interpret for use in the choice analysis.We develop a data-driven choice model that extends the systematic utility function to incorporate non-linear cross-effects using a series of residual layers and using skipped connections to handle model identifiability in estimating a large number of parameters.The model structure accounts for cross-effects and choice heterogeneity arising from substitution, interactions with non-chosen alternatives and other effects in a non-linear manner.We describe the formulation, model estimation, interpretability and examine the relative performance and econometric implications of our proposed model.We present an illustrative example of the model on a classic red/blue bus choice scenario example. For a real-world application, we use a travel mode choice dataset to analyze the model characteristics compared to traditional neural networks and Logit formulations.Our findings show that our ResLogit approach significantly outperforms MLP models while providing similar interpretability as a Multinomial Logit model.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Goodfellow, I., Bengio, Y., Courville, A (2016) Deep Learning | 0.928 | 4 | 3 | 100% |
| 2 | He, K., Zhang, X., Ren, S., Sun, J (2016) Deep residual learning for image recognition | 0.874 | 7 | 2 | 100% |
| 3 | Sifringer, B., Lurkin, V., Alahi, A (2020) Enhancing discrete choice models with representation learning | 0.874 | 5 | 2 | 100% |
| 4 | Timmermans, H., Borgers, A., van der Waerden, P (1992) Mother logit analysis of substitution effects in consumer shopping destination choice | 0.874 | 5 | 2 | 100% |
| 5 | Hess, S., Daly, A., Batley, R (2018) Revisiting consistency with random utility maximisation: theory and implications for practical work | 0.843 | 3 | 3 | 100% |
| 6 | Lee, D., Derrible, S., Pereira, F.C (2018) Comparison of four types of artificial neural network and a multinomial logit model for travel mode choice modeling | 0.737 | 3 | 2 | 100% |
| 7 | Bansal, P., Krueger, R., Bierlaire, M., Daziano, R.A., Rashidi, T.H (2019) Bayesian estimation of mixed multinomial logit models: Advances and simulation-based evaluations | 0.644 | 2 | 2 | 100% |
| 8 | Borysov, S.S., Rich, J., Pereira, F.C (2019) How to generate micro-agents? a deep generative modeling approach to population synthesis | 0.644 | 2 | 2 | 100% |
| 9 | McFadden, D., Tye, W.B., Train, K (1977) An application of diagnostic tests for the independence from irrelevant alternatives property of the multinomial logit model | 0.644 | 2 | 2 | 100% |
| 10 | Pereira, F.C (2019) Rethinking travel behavior modeling representations through embeddings | 0.644 | 2 | 2 | 100% |
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