This study aims to investigate how artificial intelligence can enhance the resilience and efficiency of railway timetables in disruption-prone commuter corridors. Specifically, it focuses on Moroccan railway networks connecting Casablanca, Mohammedia, and Rabat, where recurrent delays and congestion compromise service reliability. The research seeks to determine how integrating predictive delay modeling with adaptive passenger behavior can reduce secondary delays, alleviate congestion, and maintain timetable stability under operational disturbances.

A unified, simulation-based framework was developed, combining 3 interlinked modules: (1) machine learning-based predictive delay forecasting, (2) agent-based modeling of passenger adaptive behavior, and (3) dynamic timetable reoptimization using a rolling-horizon heuristic approach. The framework operates as a closed-loop system, where predicted delays and simulated passenger responses continuously inform real-time timetable adjustments. Empirical validation was conducted using operational data from Moroccan commuter trains, with scenario-based analysis comparing baseline, prediction-only, and fully integrated interventions.

Results show that the fully integrated framework significantly improves operational performance. Average train delays were reduced by 46%, total passenger waiting time decreased by 43%, and congestion intensity was nearly halved, while timetable stability remained high at 95%. The study also demonstrates that passenger behavior plays a critical role in delay propagation, and that combining predictive forecasting with adaptive control strategies prevents the nonlinear amplification of secondary delays that traditional train-centric models fail to address.

This research advances the field of railway operations by presenting a passenger-centered, AI-driven timetable reoptimization framework that integrates predictive analytics and behavioral simulation within a dynamic feedback loop. Unlike conventional models, it captures emergent congestion patterns, anticipates disruptions proactively, and provides actionable operational strategies without requiring major infrastructure expansion. The study offers a novel methodological contribution with practical implications for enhancing commuter railway resilience in high-density, disruption-prone contexts.

Rail freight is widely recognized for its economic and environmental advantages, yet it remains weakly integrated into firms’ supply chains, particularly in emerging economies. This study aims to investigate the conditions under which rail freight can be effectively integrated into multi-actor supply chains, with specific attention to the role of organizational coordination, information quality and artificial intelligence (AI) in shaping logistics integration outcomes.

The study draws on a quantitative survey of 3,185 stakeholders involved in rail-based and multimodal supply chains in Morocco. The data are analyzed using a combination of machine learning, deep learning and artificial neural network models. These methods are used not only to identify the main determinants of rail freight integration but also to capture non-linear relationships, interaction effects and potential integration trajectories that cannot be addressed through conventional linear models.

The results show that rail freight integration depends primarily on organizational and informational mechanisms rather than on infrastructure alone. Inter-organizational coordination and logistics information quality emerge as the most influential factors. AI contributes positively to rail freight integration, but its effect is conditional: AI tools significantly enhance integration only when adequate levels of coordination and information sharing are already in place. Scenario simulations further reveal that the strongest integration gains arise from the combined improvement of organizational practices and AI adoption.

This research contributes to the literature by shifting the focus from infrastructure-centered explanations toward a systemic understanding of rail freight integration. It is among the first studies to empirically combine machine learning, deep learning and artificial neural networks to analyze logistics integration in an emerging-economy context and to show that AI functions as a complementary and amplifying mechanism rather than a standalone solution.