The article examines methods to predict cargo arrival times through Apache Spark and Scala. The necessity for such methods arises due to external factors such as unpredictable road conditions, weather phenomena, and specific logistical operations. Information processing employs methods such as regression, decision trees, and neural networks, which analyze data from sensors, GPS devices, and other sources to build forecasts that consider all factors directly or indirectly affecting calculation accuracy.

The methodology is based on studying the functionality of the Apache Spark platform integrated with the Scala programming language, enabling the processing of large datasets with high operational speed and solution scalability.

The use of Apache Spark combined with Scala accounts for streaming data, which improves prediction accuracy. This method optimizes logistics processes by reducing delays and allowing timely responses to changes in external conditions.

The information presented in the article will be useful for data processing professionals, logisticians, and developers.

ETA, Scala, Apache Spark, logistics, machine learning

Li N. et al. Modeling categorized truck arrivals at ports: Big data for traffic prediction //IEEE Transactions on Intelligent Transportation Systems. – 2022. – Vol. 24 (3). – pp. 2772-2788.

Khotimah H. et al. Performance Analysis of the Distributed Support Vector Machine Algorithm Using Spark for Predicting Flight Delays //E3S Web of Conferences. – EDP Sciences. - 2023. – Vol. 465, 02037. – pp.1-10

Sahoo R. et al. A hybrid ensemble learning-based prediction model to minimise delay in air cargo transport using bagging and stacking //International Journal of Production Research. – 2022. – Vol. 60 (2). – pp. 644-660.

Pérez-Chacón R. et al. Big data time series forecasting based on pattern sequence similarity and its application to the electricity demand //Information Sciences. – 2020. – Vol. 540. – pp. 160-174.

Liao V. C. C. Artificial Intelligence Technology to Predict Exact Estimated Time of Arrival for Smart Transportation Using Past Shipment Data //2024 IEEE 4th International Conference on Electronic Communications, Internet of Things and Big Data (ICEIB). – IEEE. - 2024. – pp. 275-277.