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The paper presents an approach to the automatic grouping algorithms development based on parametric optimization models for processing high-volume data in the agrarian and industrial complex. Combined search algorithms with alternating randomized neighborhoods show much more stable results (give a smaller minimum value, and also have a low standard deviation of the target function) and hence better performance compared to known (so-called classical) algorithms, such as j-means and k-means.

The article deals with the problem of the formation of a traffic jam on the one of the street Krasnoyarsk cities. According to statistics, the largest set of accidents occur when turning the vehicle. For convenience and traffic priority, traffic lights are set up and additional signals or sections of traffic lights are introduced. In Europe, additional lanes are added, designed separately for turning, where cars can continue to wait for their turn, but if there are a lot of cars, then the additional lane ceases to cope with the flow of cars and begins to load the lane. Another option, when all left turns are prohibited, you need to turn right, where there is one-way traffic, look for a place to turn around and return from another street in order to drive straight to the opposite side of the road where the turn was prohibited. These manipulations can affect the ecology near residential areas and overload small streets, where there is already 1 lane for traffic. The paper proposes the option of transferring the turn to the supermarket to the next intersection in order to increase throughput at large traffic flows, and also uses analytical optimization methods to minimize travel time and maximize passing cars per hour.