Architecture of managing big data of mixed transportation of passengers in aglomerations

Abstract

The aim of this study is to develop a big data architecture that can provide the formation and management of competitive mixed passenger traffic in agglomerations in real time, taking into account the optimization of their cost, speed and new services. The work is based on studies of development trends of transport systems in agglomerations (SmartCitiesWorld); analysis of the methodology and use of systems for managing relational databases of structured data, as well as non-relational databases, information processing experience AIIM. For the analysis of passenger flows and decision-making on optimal routes, spatial databases OGS that implement standards have been used. The study substantiates the conclusion that the Big Data technology, implemented according to the cascade principle of information support for the transportation process, ensures the growth of monetization of all its components (transport infrastructure, vehicles and their management system). On this basis, a big data architecture was built, which implies the sharing of structured and unstructured data in the management of passenger traffic in agglomeration. This architecture made it possible to take into account the influence of the most important changes in the agglomeration on the mobility of its population and to optimize the financial performance of transport organizations due to the competitiveness of the allocated mixed routes.