Predicting Cargo Arrival Time Using Scala and Spark: Approaches and Achievements
DOI:
https://doi.org/10.37547/tajet/Volume07Issue03-09Keywords:
ETA, Scala, Apache Spark, logistics, machine learningAbstract
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.
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Copyright (c) 2025 Danylo Liakhovetskyi
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