A comparative study of machine learning, deep neural networks and random utility maximization models for travel mode choice modelling

García-García, José Carlos; García-Ródenas, Ricardo; López-Gómez, Julio Alberto; Martín-Baos, José Ángel

doi:10.1016/j.trpro.2022.02.047

Cited by 13 publications

(5 citation statements)

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“…to find the best approximation for the function describing f (x i ) = y i which maps the inputs (x) to the outputs (y), where the outputs would be the classification (Lichtner-Bajjaoui, 2020). The objective function is to minimize the expected value of incorrect classifications, which is equivalent to maximizing the utility or goal of the ML (typically back-propagation 8 ) algorithm (García-García et al, 2022). In the case of the MLP the probabilities attached to each element x to be classified belonging to a class k is a vector P (y k |x) that can be written as…”

Section: Examples In Aimentioning

confidence: 99%

Artificial Intelligence and the Economics of Decision-Making

2024

Artificial Intelligence

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Any opinions expressed in this paper are those of the author(s) and not those of IZA. Research published in this series may include views on policy, but IZA takes no institutional policy positions. The IZA research network is committed to the IZA Guiding Principles of Research Integrity. The IZA Institute of Labor Economics is an independent economic research institute that conducts research in labor economics and offers evidence-based policy advice on labor market issues. Supported by the Deutsche Post Foundation, IZA runs the world's largest network of economists, whose research aims to provide answers to the global labor market challenges of our time. Our key objective is to build bridges between academic research, policymakers and society. IZA Discussion Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. A revised version may be available directly from the author.

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Section: Examples In Aimentioning

confidence: 99%

Artificial Intelligence and the Economics of Decision-Making

2024

Artificial Intelligence

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“…In recent years, machine learning models have been proven to be a promising alternative in modeling travel mode choices [ 36 , 37 ]. Through data-driven learning, machine learning models can show complex nonlinear relationships between variables and assess the importance of variables.…”

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confidence: 99%

Exploring the Nonlinear and Threshold Effects of Travel Distance on the Travel Mode Choice across Different Groups: An Empirical Study of Guiyang, China

Shi

et al. 2022

IJERPH

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Examining how travel distance is associated with travel mode choice is essential for understanding traveler travel patterns and the potential mechanisms of behavioral changes. Although existing studies have explored the effect of travel distance on travel mode choice, most overlook their non-linear relationship and the heterogeneity between groups. In this study, the correlation between travel distance and travel mode choice is explored by applying the random forest model based on resident travel survey data in Guiyang, China. The results show that travel distance is far more important than other determinants for understanding the mechanism of travel mode choice. Travel distance contributes to 42.28% of explanation power for predicting travel mode choice and even 63.24% for walking. Significant nonlinear associations and threshold effects are found between travel distance and travel mode choice, and such nonlinear associations vary significantly across different socioeconomic groups. Policymakers are recommended to understand the group heterogeneity of travel mode choice behavior and to make targeted interventions for different groups with different travel distances. These results can provide beneficial guidance for optimizing the spatial layout of transportation infrastructure and improving the operational efficiency of low-carbon transportation systems.

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“…The objective function is to minimize the expected value of incorrect classifications, which is equivalent to maximizing the utility or goal of the ML (typically back-propagation 13 ) algorithm (García-García et al, 2022). In the case of the MLP the probabilities attached to each element x to be classified belonging to a class k is a vector P (y k |x) that can be written as…”

Section: Examples In the Field Of Aimentioning

confidence: 99%

“…The back-propagation algorithm will adjust the weights and threshold values to minimize the loss function in classification (and maximize the probability that a classification is accurate). For a more detailed discussion and examples, seeGarcía-García et al (2022).…”

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confidence: 99%

The Future Economics of Artificial Intelligence: Mythical Agents, a Singleton and the Dark Forest

Naudé

2022

SSRN Journal

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A comparative study of machine learning, deep neural networks and random utility maximization models for travel mode choice modelling

Cited by 13 publications

References 10 publications

Artificial Intelligence and the Economics of Decision-Making

Artificial Intelligence and the Economics of Decision-Making

Exploring the Nonlinear and Threshold Effects of Travel Distance on the Travel Mode Choice across Different Groups: An Empirical Study of Guiyang, China

The Future Economics of Artificial Intelligence: Mythical Agents, a Singleton and the Dark Forest

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