OD matrices in transport tasks

The paper considers the analysis of data in so-called origin-destination matrices. Also in relation to them often use the term matrix of correspondence. As practical examples, we are using data from Moscow metro and Moscow suburb railways. The correspondence matrix shows the number of trips between two points (stations) for a selected time interval. Theoretically, such data being calculated for the given time intervals describe all the characteristics of the passenger load. But practically, it depends on the existing models for traffic. Currently, such data used, mostly, just to calculate simple statistics. But such a simplified approach completely ignores the temporal characteristics of the traffic. In this paper, we discuss data mining models and approaches for origin-destination matrices analysis. Also, we describe the results of applying such approaches to analyzing data from the Moscow region.

from our new paper: On Processing of Correspondence Matrices in Transport Systems

On Physical Web for Social Networks

The article discusses the use of Physical Web approaches for the expansion of social networks. This implies the presentation of data from social networks in a real (physical) context, as well as the inverse task of using information about a real physical context in querying and analyzing data from social networks. First of all, mobile phones of social network users are considered as real objects that will be used both in data dissemination and in gathering information about the context. In this case, the purpose of consideration is to build a “natural” extension, when the implementation does not require the creation of a special type of social network entries. The general scheme or model of implementation is based on the minimization (or even complete absence) of requesting additional rights to access the social network, the absence of marks in the social network, and the use of basic functionality and standard protocols for mobile devices.

From our new paper: Namiot D., Sneps-Sneppe M. (2019) On Physical Web for Social Networks. In: Vishnevskiy V., Samouylov K., Kozyrev D. (eds) Distributed Computer and Communication Networks. DCCN 2019. Communications in Computer and Information Science, vol 1141. Springer, Cham

On mobility patterns in Smart City

The article is devoted to the analysis of transport data in the Moscow region, which was used in the design of new urban rail lines. Mobility (smart mobility) is one of the main characteristics of a smart city. Understanding how residents in a city actually move is necessary for the reliable provision of transport services, the construction of new transport lines, etc. Understanding patterns of displacement allows you to identify anomalies in urban displacements, which are evidence of some changes in the urban environment. The basis for the analysis is the data of mobile operators and information on the use of travel documents. - from our new paper