# Analytical Model for Information Flow Management in Intelligent Transport Systems
## 6 Conclusions  
This study has as its main outcome the method that enables the identification of the most optimal traffic routes in the dynamically changing, multi-criterion context of commercial operations planning. Specifically, the method allows for the following:  
* Formalizing a transport system with due account of its information states, which, in turn, are determined by exposure to internal and external disturbances;
* Identifying a transport system's criteria that take into account the heterogeneity of its elements;
* Achieving the tools for managing and optimizing transport systems' performance, that build on the existing decision-making methods and allow the disadvantages of the heuristic methods used in determining the weighted coefficients of factors to be avoided. The proposed analytical method employs a zoning technique that builds on the hierarchical relationship between probabilities and allows  
* Big data in transportation systems to be processed;
* AI-based analysis of transport systems' operating environments that involves an unlimited number of criteria or performance attributes. Further, the proposed method allows the avoidance of the limitations of the subjective methods used in the decision-making theory, namely, the methods for  
* The a priori ranking of factors (methods based on expert assessments);
* The a priori distribution of probabilities;
* Ensuring guaranteed decision levels. With complex systems, the number of decisions that can fully meet all the criteria is always one. While the existing methods do yield competent decision making, they do not ensure its maximum efficiency. The method proposed herein allows for the most effective decision making within the boundaries of the system under study. The proposed method is 23.81% more effective than [PERSON]'s method, 78.57% than [PERSON]'s, 43.57% than [PERSON]'s, 59.23% than [PERSON]'s, and 59.93% than [PERSON]'s. The outcomes of this study have been implemented in the research and development project entitled \"Developing the Object-Oriented Management Models and Their Software Prototypes for Transport and Logistics Systems\". The project has produced an intelligent transport management system for a domestic logistics provider, that has been tested on a busy interchange road in one of the districts of St. Petersburg. According to the test results, the proposed method leads to a 15% increase in cost reduction. The method's high degree of reliability is assured by the use of systems analysis and systems engineering methods in creating the digital technologies implementation concept for road freight traffic, as well as by the use of vector optimization and linear programming methods in creating the analytical models for the more optimized design of road freight traffic management plans. Further, its high reliability is attested by the proprietary software product intended for automating the newly developed, centralized transportation management methods: \"The Software for Identifying Optimal Transportation Routes in Dynamically Changing Road Environments\", authored by [PERSON], [PERSON], [PERSON], Evtyukov S.A. Software Registration Certificate 2021667592, country: Russia, 2021, registration date: 1 November 2021. The principal advantages of the proposed model for assuring effective decision making in complex, big-data-based information and analysis systems and software products, consist of:  
* The absence of a formalized relationship between the weighted coefficients obtained for individual criteria and action options in transport systems;
* The resultant decision being the maximum possible under the initial values of performance indicators for the criteria under consideration;
* The resultant decision allowing not only the desired Pareto-optimal decisions to be obtained, but also the number of required computations to be substantially reduced.