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Embedding respresentations for discrete choice and travel demand models

By: Publication details: Kongens Lyngby : Technical University of Denmark. DTU, 2024Description: 155 sOther title:
  • Embedding representations for discrete choice and travel demand models
Subject(s): Online resources: Notes: Härtill 4 uppsatser Dissertation note: Diss. (sammanfattning) Kongens Lyngby : Danmarks Tekniske Universitet, 2024 Abstract: Including richer data in Discrete Choice Models (DCM) and Activity-based Models is crucial for promoting future transport research. However, the default method of encoding categorical variables, “one-hot encoding”, poses constrains to the number of explanatory variables that can be included. As it adds an extra variable per category, the model’s complexity is increased proportionally to the cardinality of the categorical variables considered. This can pose severe challenges in statistical modeling with an exponentially increased sample size requirement to avoid problems such as overfitting or poor parameter estimation. Given that travel data collection is the most resource-intensive of the transportation model development process, rather than increasing the sample size, a more efficient treatment would be to find alternative methods for encoding categorical variables, that would allow for more compact yet informative representations of the categorical data. In this thesis we address this need by introducing a novel, data-driven Neural Network (NN) approach for encoding categorical and discrete travel variables into continuous vector representations, called Embeddings. This approach is strongly inspired by Natural Language Processing (NLP) techniques, and allows us to obtain compact–yet meaningful representations that are able to capture semantic associations between the encoded categories, and contextual information in relation to a specific prediction task. By integrating embedding representations into Discrete Choice and Activity-Based Models we aim to develop hybrid models that not only outperform previously suggested approaches but also produce interpretable and behaviorally meaningful outputs that provide a more comprehensive understanding of the travel behavior. By achieving these aims, this thesis seeks to contribute to future transport research by providing a more efficient and interpretative framework for modeling travel behavior, ultimately leading to more informed decision-making in transportation planning and policy implementation.
Item type: Dissertation
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Härtill 4 uppsatser

Diss. (sammanfattning) Kongens Lyngby : Danmarks Tekniske Universitet, 2024

Including richer data in Discrete Choice Models (DCM) and Activity-based Models is crucial for promoting future transport research. However, the default method of encoding categorical variables, “one-hot encoding”, poses constrains to the number of explanatory variables that can be included. As it adds an extra variable per category, the model’s complexity is increased proportionally to the cardinality of the categorical variables considered. This can pose severe challenges in statistical modeling with an exponentially increased sample size requirement to avoid problems such as overfitting or poor parameter estimation. Given that travel data collection is the most resource-intensive of the transportation model development process, rather than increasing the sample size, a more efficient treatment would be to find alternative methods for encoding categorical variables, that would allow for more compact yet informative representations of the categorical data. In this thesis we address this need by introducing a novel, data-driven Neural Network (NN) approach for encoding categorical and discrete travel variables into continuous vector representations, called Embeddings. This approach is strongly inspired by Natural Language Processing (NLP) techniques, and allows us to obtain compact–yet meaningful representations that are able to capture semantic associations between the encoded categories, and contextual information in relation to a specific prediction task. By integrating embedding representations into Discrete Choice and Activity-Based Models we aim to develop hybrid models that not only outperform previously suggested approaches but also produce interpretable and behaviorally meaningful outputs that provide a more comprehensive understanding of the travel behavior. By achieving these aims, this thesis seeks to contribute to future transport research by providing a more efficient and interpretative framework for modeling travel behavior, ultimately leading to more informed decision-making in transportation planning and policy implementation.