Machine learning in travel mode choice studies: A systematic literature review of applications, methods, and challenges

Abstract

Accurate modeling of individual travel mode choices is vital for developing sustainable, efficient, and equitable transportation systems. Traditional discrete choice models offer behavioral interpretability but struggle to capture complex and nonlinear travel behavior. Machine learning (ML) techniques provide strong predictive performance yet raise concerns regarding interpretability, transferability, and reproducibility. This study presents a systematic literature review of ML applications in travel mode choice covering publications from January 2017 to March 2025. Following the PRISMA 2020 protocol and strict inclusion and quality assessment procedures, 49 studies were selected for full analysis. A structured coding framework guided data extraction across modeling methods, feature design, optimization strategies, evaluation metrics, and transparency practices. Results indicate that ensemble learners and deep neural networks dominate recent research, improving predictive accuracy but often at the cost of reduced behavioral realism. Feature engineering increasingly integrates spatial, attitudinal, and sensor-based data, while temporal and multimodal dimensions remain underexplored. Most studies emphasize internal validation, with limited attention to behavioral plausibility, causal validity, or external transferability. This paper develops a methodological taxonomy and a unified framework for predictive and behavioral evaluation, highlighting protocols for causal identification and external benchmarking. It also outlines the potential of large language models (LLMs) for data curation, hybrid modeling, and research reproducibility. By bridging statistical learning with economic theory, future research can move toward models that are both technically robust and behaviorally coherent. Leveraging emerging AI tools such as LLMs will further enhance transparency, reproducibility, and alignment with the goals of sustainable and resilient mobility systems.

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