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<p>Leveraging travel time data is essential for various applications such as ride sharing, traffic management, and traffic accident detection. Despite significant advancements in travel time forecasting, selecting the most appropriate prediction method remains challenging due to the constantly changing nature of road traffic. Previous research have investigated several machine learning models for predicting travel time; however, the opaque nature of these models often limits the ability to explain their predictions, creating a notable research gap. Recognizing the potential of eXplainable AI (XAI) techniques to enhance decision-making, our study aims to address this gap by incorporating XAI methods. Our work introduces a taxonomy of XAI techniques and applies explainable travel time prediction methods from key XAI categories, specifically ante-hoc techniques (such as QLattice) and post-hoc techniques (including SHAP, LIME, and ELI5). We conduct a thorough evaluation using two real-world datasets, NYC taxi and California PeMS, demonstrating that XAI techniques can improve predictive accuracy and provide insights into the factors affecting travel times, while also showcasing the comparative performance of different XAI-based approaches for travel time prediction.</p>

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Travel time prediction: unveiling the impact of spatio-temporal features on trip duration with eXplainable AI

  • 期刊论文

  • 发表日期：

    2025年12月1日

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  • Nishtha Srivastava,
  • Bhavesh N. Gohil,
  • Suprio Ray

摘要

Leveraging travel time data is essential for various applications such as ride sharing, traffic management, and traffic accident detection. Despite significant advancements in travel time forecasting, selecting the most appropriate prediction method remains challenging due to the constantly changing nature of road traffic. Previous research have investigated several machine learning models for predicting travel time; however, the opaque nature of these models often limits the ability to explain their predictions, creating a notable research gap. Recognizing the potential of eXplainable AI (XAI) techniques to enhance decision-making, our study aims to address this gap by incorporating XAI methods. Our work introduces a taxonomy of XAI techniques and applies explainable travel time prediction methods from key XAI categories, specifically ante-hoc techniques (such as QLattice) and post-hoc techniques (including SHAP, LIME, and ELI5). We conduct a thorough evaluation using two real-world datasets, NYC taxi and California PeMS, demonstrating that XAI techniques can improve predictive accuracy and provide insights into the factors affecting travel times, while also showcasing the comparative performance of different XAI-based approaches for travel time prediction.