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This study explores the factors affecting travel mode choices upon the implementation of two innovative Traffic Management Strategies in an urban setting, namely a Transit Signal Priority system and a Congestion Pricing scheme. To gather the necessary data, a stated preferences survey was conducted in three major European cities: Athens (Greece), Lisbon (Portugal), and Manchester (United Kingdom), aimed at the elicitation of individuals’ preferences when selecting travel modes. The collected data from each city were used to develop and calibrate multiple mode choice models, including econometric Discrete Choice (DC) models such as the Multinomial Logit (MNL), as well as state-of-the-art Machine Learning models like the Random Forest (RF) classifiers. Through the evaluation and comparison of the models’ results, we present an analysis of the factors influencing mode choice and we highlight the similarities and differences in the performance and interpretation of parametric and non-parametric models. The findings from this study can inform the development of sustainable transportation systems and contribute to more efficient decision-making processes in urban mobility management.

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Comparing Random Forests and Multinomial Logit Models for Urban Travel Mode Choice Under Innovative Traffic Management Strategies

  • 会议论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年

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  • Christos Konstantinou,
  • Eleni Mantouka,
  • Marios Giouroukelis,
  • Eleni I. Vlahogianni

摘要

This study explores the factors affecting travel mode choices upon the implementation of two innovative Traffic Management Strategies in an urban setting, namely a Transit Signal Priority system and a Congestion Pricing scheme. To gather the necessary data, a stated preferences survey was conducted in three major European cities: Athens (Greece), Lisbon (Portugal), and Manchester (United Kingdom), aimed at the elicitation of individuals’ preferences when selecting travel modes. The collected data from each city were used to develop and calibrate multiple mode choice models, including econometric Discrete Choice (DC) models such as the Multinomial Logit (MNL), as well as state-of-the-art Machine Learning models like the Random Forest (RF) classifiers. Through the evaluation and comparison of the models’ results, we present an analysis of the factors influencing mode choice and we highlight the similarities and differences in the performance and interpretation of parametric and non-parametric models. The findings from this study can inform the development of sustainable transportation systems and contribute to more efficient decision-making processes in urban mobility management.