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The development and application of a global positioning system – based monitoring system for soil stabilization vehicles

Baráth Balázs, Simon Norbert, Háry András

Journal of Ecological Engineering

2025

Vol. 26, nr 3

1--7

The accuracy and efficiency of soil stabilization works are key to ensuring the durability of roads. During the conducted research, a GPS-based (global positioning system) tracking system was developed that can monitor the movement of soil stabilization vehicles in real time, recording the exact location and working width of the stabilized road sections. The system’s software solutions enable the conversion of location coordinates from the WGS84 (World Geodetic System) system to EOV (EOV as Uniform National Projection system) format and visualization of the results in AutoCAD. The developed tool can significantly contribute to the improvement of the quality control of soil stabilization works, as the development of road defects resulting from stabilization errors can be reduced with the help of documentation and visualization. During the testing of this system, the development proved to be successful and provides an opportunity to perform soil stabilization processes more efficiently and reliably, thereby improving the service life of road surfaces and traffic safety.

Improving unloading time prediction for vehicle routing problem based on GPS data

Žunić Emir, Kuric Amar, Delalić Sead

Annals of Computer Science and Information Systems

2020

Vol. 22

45--49

The problem of transport optimization is of great importance for the successful operation of distribution companies. To successfully find routes, it is necessary to provide accurate input data on orders, customer location, vehicle fleet, depots, and delivery restrictions. Most of the input data can be provided through the order creation process or the use of various online services. One of the most important inputs is an estimate of the unloading time of the goods for each customer. The number of customers that the vehicle serves during the day directly depends on the time of unloading. This estimate depends on the number of items, weight and volume of orders, but also on the specifics of customers, such as the proximity of parking or crowds at the unloading location. Customers repeat over time, and unloading time can be calculated from GPS data history. The paper describes the innovative application of machine learning techniques and delivery history obtained through a GPS vehicle tracking system for a more accurate estimate of unloading time. The application of techniques gave quality results and significantly improved the accuracy of unloading time data by 83.27% compared to previously used methods. The proposed method has been implemented for some of the largest distribution companies in Bosnia and Herzegovina.