Development of vehicle speed forecasting method for intelligent highway transport system

Abstract

Interaction of vehicles on an intercity highway is considered. The vehicle control model here is idealized, close to the 4th generation automated intelligent transport system. Each vehicle has the desired motion program, independent of the driver's motives, which is justified by minimum resource consumption and compliance with the desired schedule. The diversity of programs affects their unwanted change. The aim was to identify the dependence of the actual vehicle speed on traffic flow parameters. The main task was to reveal a direct parameter for changing the motion program. The use of simulation models based on cellular automata is substantiated. A new cellular automaton, which is a sliding window with a reference point, which is the observer vehicle is developed. The number of objects in the field increases periodically and is constant. All cells on the left and right of the reference point of the automaton form the information field, or the total length of the automaton. The automaton height, which depends on the type of highway, is modeled. The rules of objects movement in the automaton grid at each iteration are finite, established and similar to the Schreckenberg automaton, except for randomization, which is minimized in this model. Such an automaton reflects relative speeds of vehicles relative to the observer vehicle, as well as the ability to reproduce accelerations. At each iteration, the change in vehicle speeds is calculated. The simulation algorithm is programmed in the Delphi language. Simulation of the vehicle movement on the E-471 international highway is performed. On the 20 km section of this route, traffic flows with different density and speed distribution are modeled. The quadratic correlation dependences of the forced change in the desired speed of the observer vehicle on changes in the average speed of the flow vehicles are revealed. The degree of agreement between the theoretical dependence and empirical data is very high. On the basis of the dependencies obtained, the choice of the direct diagnostic parameter of the traffic flow is justified.