Smart Management and Power Consumption Forecasting in Passenger Transport

Abstract

Modern approaches to development and management of passenger transportation include aspects of efficient electricity consumption in line with “open sky” concept, and intelligent logistics. Both scientific community and empiricists agree upon the importance of including intelligent models in management of studied systems and their elements as well as in forecasting their operational characteristics. Intelligence of these systems is defined by the existence of technical and technological solutions in the sphere of artificial intellect. Communication systems and networks provoke the dynamic evolution of transport and logistics practically in all directions which allows effectively managing power flows functioning in them. Models designed to calculate electricity consumption of trams and trolleys are based on the assumptions about passenger traffic being constant, which, in our view, needs correction and clarification. Rolling-stock control systems allow getting information about characteristic quantity in the regime of real-time and consequently form an adaptive transportation strategy for electric passenger transport. Academic and research approach to tackling this problem means to design forecast models of passenger traffic flows using artificial neural networks and based on them adaptive selection and adjustment of passenger capacity according to the respective dynamics. In the process of developing route descriptor, this gives a possibility of justified changes in interstation distance in case competition terms of providing interstation intervals of rolling-stock passenger service quality are observed.