Deep Neural Network Design for Modeling Individual-Level Travel Mode Choice Behavior
Daisik Nam and
Jaewoo Cho
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Daisik Nam: Department of Civil and Environmental Engineering, Institute of Transportation Studies, University of California, Irvine, CA 92603, USA
Jaewoo Cho: College of Social Science, Hansung University, Seoul 02876, Korea
Sustainability, 2020, vol. 12, issue 18, 1-19
Abstract:
Individual-level modeling is an essential requirement for effective deployment of smart urban mobility applications. Mode choice behavior is also a core feature in transportation planning models, which are used for analyzing future policies and sustainable plans such as greenhouse gas emissions reduction plans. Specifically, an agent-based model requires an individual level choice behavior, mode choice being one such example. However, traditional utility-based discrete choice models, such as logit models, are limited to aggregated behavior analysis. This paper develops a model employing a deep neural network structure that is applicable to the travel mode choice problem. This paper uses deep learning algorithms to highlight an individual-level mode choice behavior model, which leads us to take into account the inherent characteristics of choice models that all individuals have different choice options, an aspect not considered in the neural network models of the past that have led to poorer performance. Comparative analysis with existing behavior models indicates that the proposed model outperforms traditional discrete choice models in terms of prediction accuracy for both individual and aggregated behavior.
Keywords: discrete choice model; deep neural network; mode choice behavior; smart urban mobility; individual-level choice prediction; agent-based model; random utility model; logit model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)
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