Predicting Fuel Consumption by Artificial Neural Network (ANN) Based on the Regular City Bus Lines

This article discusses the application of an ANN model for forecasting the fuel consumption of vehicles on the regular city bus lines. In the context of rising fuel costs and their impact on transportation companies, the developed system supports the optimization of fuel consumption standards and fleet management. The model accounts for prediction factors such as route length [km], number of bus stops, probability of traffic jams [from 1—low to 3—high], ambient temperature [°C], from external database, technical state of the vehicle [from 1—good to 5—bad], type of petrol [1—ON; 2—E95], filling of the vehicle/number of passengers [from 1—empty to 5—full]. Based on this these data, the presented model was developed. The system analyzes input, generates reports, and identifies potential issues, including excessive fuel consumption or fuel theft. Its modular design allows for further development and adaptation to user needs. Implementing this solution enhances operational efficiency, reduces costs, and optimizes transportation management.