An Explainable Machine Learning Framework for Green Maritime Logistics Using Dynamic Spatial Congestion Prediction and Relative Fuel Index Modeling

A Comparative Analysis of Machine Learning Regression Models for Urban Traffic Congestion Prediction

A feature-centric machine learning framework for forecasting urban air pollution in arid regions

Machine Learning Model for Shipment Delay Prediction

The fast pace of development of e-commerce has elevated the timely delivery as a characteristic element of customer satisfaction and logistics performance. However, there are still delays in shipment because of uncontrollable factors like traffic, weather, operational bottlenecks and network inefficiencies. To overcome this issue, this paper derives a Machine Learning Model of Shipment Delay Prediction to combine refined shipment data, operational time and contextual logistics data to predict the probability of delay at an early phase. Based on the previous studies of real-time delay prediction, proactive risk assessment, as well as ML-based logistics optimization, the suggested framework will integrate feature engineering, supervised learning models (Random Forest, XGBoost, CatBoost, Logistic Regression), and a multi-stage prediction process. This methodology is focusing on interpretability, prediction on each shipment processing step, and scalability to the logistic operations. The experimental findings indicate that the gradient-boosting models are rather consistent in terms of their performance (high ROC-AUC scores and higher recall in the delay class). This study adds a useful and empirical methodology, which can be adopted by logistics teams to predict disruptions, make sound-informed routing, and enhance service reliability.

LOGISTICS AND TRANSPORTATION: FREIGHT DEMAND FORECASTING USING SARIMA AND XGBOOST

Accurate freight demand forecasting is essential for improving logistics planning and supporting decision-making in e-commerce supply chains. This study compares the performance of two forecasting approaches—Seasonal Autoregressive Integrated Moving Average (SARIMA) and Extreme Gradient Boosting (XGBoost)—for predicting daily freight demand measured by transported weight. The research followed the CRISP-DM methodology using the Brazilian Olist public e-commerce dataset. After data preprocessing, exploratory analysis, stationarity testing, and feature engineering, multiple SARIMA and XGBoost models were developed and evaluated using chronological train-test splitting, cross-validation, and Mean Absolute Percentage Error (MAPE). The SARIMA models incorporated seasonal differencing and Box-Cox transformations, whereas the XGBoost models included calendar-based variables, moving averages, and moving standard deviations. The results demonstrate that feature engineering substantially improved predictive performance. The best XGBoost model achieved a MAPE of 3%, considerably outperforming the best SARIMA model, whose predictive accuracy remained limited despite data transformations. These findings indicate that machine learning techniques combined with temporal feature engineering provide superior freight demand forecasts for e-commerce logistics. The proposed approach offers a practical decision-support tool for transportation planning, resource allocation, and operational efficiency while providing a reproducible computational workflow through publicly available source code and processed data.

A MACHINE LEARNING APPROACH BASED ON SYNTHETIC DATA FOR ESTIMATING CARBON FOOTPRINT IN LOGISTICS PROCESSES

This study aims to estimate the carbon footprint arising from logistics processes using machine learning methods based on synthetic data and to identify the most suitable model for this purpose. Due to data access limitations, a multidimensional synthetic dataset was generated using variables like transport distance, cargo weight, transport mode, fuel type, traffic density, weather, and vehicle characteristics. Carbon emissions were calculated via an activity-based approach. The study comparatively evaluated six algorithms: Linear Regression, Random Forest, Extra Trees, Gradient Boosting, Support Vector Regression, and XGBoost. Findings indicate that tree-based ensemble learning models outperformed classical methods in predicting emissions. Among all models, XGBoost delivered the highest performance with an R² value of 0.9945 and minimal error rates. The results demonstrate that synthetic data provides an effective alternative for estimating carbon footprints in logistics process where access to real-world data is limited, and that it can contribute to sustainable logistics decision-making processes.

OPTIMIZATION MODEL FOR URBAN PUBLIC TRANSPORT OPERATIONS USING MACHINE LEARNING-BASED DEMAND PREDICTION

Angkot, a route-based urban paratransit or public minibus service, remains an important component of daily mobility in Manado City. However, its predominantly supply-based operating pattern does not systematically adjust vehicle deployment to hourly passenger demand, which can produce low occupancy during off-peak periods and insufficient capacity during demand peaks. This study integrates machine-learning-based hourly passenger-demand prediction with an integer-constrained fleet-sizing model for the Paal Dua-Pasar 45 route. Direct observations were collected for 21 days, from 25 May to 14 June 2026, through an on-board survey of one angkot and a separate vehicle-headway survey; 1,538 passenger boardings were directly recorded. Random Forest and XGBoost were compared using a chronological 80:20 holdout and Leave-One-Day-Out Cross-Validation (LODO). Random Forest remained the better-performing model after tuning (LODO R² = 0.462; MAE = 0.76) compared with XGBoost (R² = 0.421; MAE = 0.83). The selected predictions were transformed from vehicle-level boardings to estimated corridor demand using observed hourly vehicle frequency and were then entered into the fleet-sizing model. The model-derived corridor demand averaged 57 passengers per hour, and the resulting fleet requirement averaged six vehicles, ranging from five to nine vehicles. Because corridor demand was inferred from one directly surveyed vehicle and predictive performance was moderate, these fleet values should be interpreted as route-specific planning estimates rather than externally validated operating prescriptions. The study demonstrates a practical framework for linking demand prediction with adaptive fleet allocation while highlighting the need for multi-vehicle validation and uncertainty-aware deployment.