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This research aims to enhance public transport reliability by predicting delay severity using AI.

June 19, 2026Smart CitiesOpen Access

Optimizing Public Transport Infrastructure Through AI-Driven Reliability Prediction: A Data-Driven Approach

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Randomized trial applies AI to predict delays in urban bus routes, suggesting targeted infrastructure upgrades.

This research aims to enhance public transport reliability by predicting delay severity using AI.
Developed a data-driven framework using the XGBoost machine learning algorithm.
Categorized delay severity into minor, moderate, and severe based on various variables.
Analyzed spatial and temporal data to identify reliability hotspots for infrastructure prioritization.
Achieved classification accuracies of 91.5% for outbound and 89.7% for inbound bus routes.
Identified critical factors influencing delays as seasonal and meteorological variables.
Determined specific bus stops with high delay probabilities to inform targeted infrastructure upgrades.

Andreadis et al. (2026) studied this question.

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