Building City-Scale Walking Itineraries Using Large Geospatial Datasets

Nowadays, social networks play an important role in many aspects of people’s life and in traveling in particular. People share their experience and opinions not only on specialized sites, like TripAdvisor, but also in social networks, e.g. Instagram. Combining information from different sources we can get a manifold dataset, which covers main sights, famous buildings as well as places popular with city residents. In this paper, we propose method for generation of walking tours based on large multi-source dataset. In order to create this dataset, we developed data crawling framework, which is able to collect data from Instagram at high speed. We provide several use cases for the developed itinerary generation method and demonstrate that it can significantly enrich standard touristic paths provided by official site

Forecasting of the Urban Area State Using Convolutional Neural Networks

Active development of modern cities requires not only efficient monitoring systems but furthermore forecasting systems that can predict future state of the urban area with high accuracy. In this work we present a method for urban area prediction based on geospatial activity of users in social network. One of the most popular social networks, Instagram, was taken as a source for spatial data and two large cities with different peculiarities of online activity – New York City, USA, and Saint Petersburg, Russia – were taken as target cities. We propose three different deep learning architectures that are able to solve a target problem and show that convolutional neural network based on three-dimensional convolution layers provides the best results with accuracy of 99%

SUPERCOMPUTER SIMULATION OF CRITICAL PHENOMENA IN COMPLEX SOCIAL SYSTEMS

The paper describes a problem of computer simulation of critical phenomena in complex social systems on a petascale computing systems in frames of complex networks approach. The three-layer system of nested models of complex networks is proposed including aggregated analytical model to identify critical phenomena, detailed model of individualized network dynamics and model to adjust a topological structure of a complex network. The scalable parallel algorithm covering all layers of complex networks simulation is proposed. Performance of the algorithm is studied on different supercomputing systems. The issues of software and information infrastructure of complex networks simulation are discussed including organization of distributed calculations, crawling the data in social networks and results visualization. The applications of developed methods and technologies are considered including simulation of criminal networks disruption, fast rumors spreading in social networks, evolution of financial networks and epidemics spreading

Orienteering Problem with Functional Profits for multi-source dynamic path construction.

Orienteering problem (OP) is a routing problem, where the aim is to generate a path through set of nodes, which would maximize total score and would not exceed the budget. In this paper, we present an extension of classic OP-Orienteering Problem with Functional Profits (OPFP), where the score of a specific point depends on its characteristics, position in the route, and other points in the route. For solving OPFP, we developed an open-source framework for solving orienteering problems, which utilizes four core components of OP in its modular architecture. Fully-written in Go programming language our framework can be extended for solving different types of tasks with different algorithms; this was demonstrated by implementation of two popular algorithms for OP solving-Ant Colony Optimization and Recursive Greedy Algorithm. Computational efficiency of the framework was shown through solving four well-known OP types: classic Orienteering Problem (OP), Orienteering Problem with Compulsory Vertices (OPCV), Orienteering Problem with Time Windows (OPTW), and Time Dependent Orienteering Problem (TDOP) along with OPFP. Experiments were conducted on a large multi-source dataset for Saint Petersburg, Russia, containing data from Instagram, TripAdvisor, Foursquare and official touristic website. Our framework is able to construct touristic paths for different OP types within few seconds using dataset with thousands of points of interest

Urgent Information Spreading Multi-layer Model for Simulation in Mobile Networks

AbstractInformation spreading simulation is an important problem in scientific community and is widely studied nowadays using different techniques. Efficient users’ activity simulation for urgent scenarios is even more important, because fast and accurate reaction in such situations can save human lives. In this paper we present multi-layer agent-based network model for information spreading simulation in urgent scenarios, which allows to investigate agents’ behavior in a variety of situations. This model can be used for live city simulation in integration with other agent-based human interaction models. Experimental results demonstrate logical consistency of the proposed approach and show different cases of information spreading in the network with different social aspect