Evaluation of transport system configuration by efficiency indicators

The study is devoted to the process of evaluating the efficiency of the transport system in terms of urban mobility. The approach is based on the use of a system of performance indicators using neurocomputer technologies. Generalized models for obtaining a vector of performance indicators and an integral performance indicator in the form of computer neural networks are proposed. It is shown that to record the fact that the indicator values fall to the threshold and below, it is enough to use a neural network built on perceptron neurons. The multi-layered model for determining the integral indicator allows assessing the importance of individual indicators in the system of monitoring the efficiency of a given configuration of the transport system. An experimental study of twenty-five states of the transport system of various configurations in the cities of Poland and Ukraine was carried out. The key indicators of the system's efficiency are determined, namely, the energy efficiency indicator of the vehicle as a system element, the environmental indicator and the traffic safety indicator. Based on the results of the experimental study, a neural network structure is proposed for evaluating the energy efficiency of given configurations of the transport system. For the purpose of training and testing the obtained network, the procedure of adjusting the threshold value of the activation function and normalizing the values of the input parameters array of the transport system was used. The constructed network was implemented using Visual Studio 2019 using the C++ language. The network was adjusted to determine the energy efficiency estimate with a given accuracy by replacing the perceptron neuron with a regular one with a sigmoidal activation function. The random nature of the choice of the configuration and the initial values of the weighting factors made it possible to obtain a model with an accuracy of implementation on the control sample in the range from 90 to 98.7% at a learning rate of 0.1.