This study develops and evaluates a data-driven forecasting framework for freight railway traffic along Morocco's Tanger–Casablanca corridor, with the objective of assessing whether advanced machine learning models can enhance the accuracy and robustness of medium-term demand forecasting compared to conventional statistical time-series methods, thereby improving both operational railway planning and strategic logistics decision-making.

A quantitative research design is employed using sixteen weeks of aggregated data derived from 2,159 operational records collected along the Tanger-Casablanca railway corridor. The dataset integrates multiple sources, including railway operational indicators, port throughput measures, industrial production indices, and macroeconomic variables. The study implements classical statistical models (ARIMA and SARIMA) alongside machine learning approaches, namely Random Forest, Gradient Boosting, Long Short-Term Memory (LSTM), and Prophet. Rigorous preprocessing, feature engineering, rolling-window validation, and multi-metric evaluation (MAE, RMSE, MAPE, and R²) are applied to ensure robust and unbiased model comparison.

The results indicate that machine learning approaches consistently outperform traditional statistical models in forecasting freight railway demand. Among all models, LSTM delivers the highest predictive accuracy, followed by Gradient Boosting and Random Forest, while ARIMA and SARIMA serve as baseline benchmarks but show reduced performance during periods of peak demand. The analysis further reveals that port throughput and wagon allocation are the most significant predictors of freight volume, confirming the presence of nonlinear and temporally dependent demand structures that are more effectively captured by advanced learning models.

This study contributes to the limited empirical literature on railway freight forecasting in developing countries by applying and systematically comparing advanced machine learning techniques within the Moroccan railway context. It introduces an integrated forecasting framework that combines heterogeneous data sources with rigorous validation procedures, thereby bridging the gap between predictive analytics and operational railway decision-making. The proposed approach offers both methodological innovation and practical insights to support the modernization of rail freight systems in emerging logistics environments.

The purpose of this study is to examine the determinants of modal shift intention from road freight transport to rail freight in Morocco, focusing on the perceived economic, energy and environmental performance of rail freight.

A quantitative survey was conducted among key freight transport stakeholders, including road carriers, industrial shippers, logistics operators and experts. A total of 483 valid questionnaires were collected. Measurement scales were derived from the literature and adapted to the Moroccan context. Data were analyzed using SPSS through descriptive statistics, reliability analysis, correlation tests and multiple linear regression.

The results indicate generally positive perceptions of rail freight, particularly regarding energy efficiency and environmental performance. All three perceived performance dimensions have a positive and significant effect on modal shift intention. Perceived energy performance emerges as the strongest predictor, followed by environmental impact, while economic performance shows a significant but more moderate influence. The model demonstrates strong explanatory power.

The study relies on perceptual data and a non-probabilistic sampling approach, which may limit the generalizability of the findings. Future research could integrate objective cost, energy and emission data; apply longitudinal designs or extend the model by incorporating institutional, infrastructural and policy-related variables to further explain rail freight adoption.

The findings provide valuable insights for policymakers, rail operators and logistics managers by highlighting the key levers for promoting rail freight development. Strengthening rail energy efficiency, improving service reliability and enhancing intermodal integration can significantly increase stakeholders’ willingness to shift freight from road to rail.

By encouraging modal shift toward rail freight, the study supports broader societal objectives related to environmental protection, energy security and sustainable development. Increased use of rail freight can contribute to reduced greenhouse gas emissions, lower road congestion and improved quality of life in urban and industrial areas.

This study provides one of the first empirical investigations of rail freight modal shift determinants in Morocco, offering an integrated analysis of economic, energy and environmental factors within a single conceptual framework.