Розробка модуля отримання демографічних та клінічних даних про пацієнта для експертної системи оцінювання ризику серцево – судинних захворювань у хворих на артеріальну гіпертензію

Signaling data from the cellular networks can provide a means of analyzing the efficiency of a deployed transportation system and assisting in the formulation of transport models to predict its future use. An approach based on this type of data can be especially appealing for transportation systems that need massive expansions, since it has the added benefit that no specialized equipment or installations are required, hence it can be very cost efficient. Within this context in this paper we describe how such obtained data can be processed and used in order to act as enablers for traditional transportation analysis models. We outline a layered, modular architectural framework that encompasses the entire process and present results from initial analysis of mobile phone call data in the context of mobility, transport and transport infrastructure. We finally introduce the Mobility Analytics Platform, developed by Ericsson Research, tailored for mobility analysis, and discuss techniques for analyzing transport supply and demand, and give indication on how cell phone use data can be used directly to analyze the status and use of the current transport infrastructure

Mobilitetanalys baserad på mobildata

The thesis evaluates mobility based on mobile phone positions. The aim is to develop and assess different methods for travel demand estimation based on CDR data. Besides this estimation location data in cellular data is explained in more detail and a previous work based on mobile phone data and travel demand estimation is reviewed. The different methods of travel time estimation include both static and dynamic estimation. The static travel demand estimation evaluates movements in the city based on predefined time periods, whereas the dynamic estimations are based on different definitions of a trip. A trip can be defined as movements between important places, or just simply count a trip between each position, or a filtering of active states to create more accurate origin-destination matrices. The second part of the thesis includes evaluation of travel time based on CDR data before the final conclusions are drawn. The main finding of the thesis is that it is possible to assess mobility in a city based on CDR data, even if there are no validation data available

Mobilitetanalys baserad på mobildata

The thesis evaluates mobility based on mobile phone positions. The aim is to develop and assess different methods for travel demand estimation based on CDR data. Besides this estimation location data in cellular data is explained in more detail and a previous work based on mobile phone data and travel demand estimation is reviewed. The different methods of travel time estimation include both static and dynamic estimation. The static travel demand estimation evaluates movements in the city based on predefined time periods, whereas the dynamic estimations are based on different definitions of a trip. A trip can be defined as movements between important places, or just simply count a trip between each position, or a filtering of active states to create more accurate origin-destination matrices. The second part of the thesis includes evaluation of travel time based on CDR data before the final conclusions are drawn. The main finding of the thesis is that it is possible to assess mobility in a city based on CDR data, even if there are no validation data available

Travel demand estimation and network assignment based on cellular network data

Cellular networks signaling data provide means for analyzing the efficiency of an underlying transportation system and assisting the formulation of models to predict its future use. This paper describes how signaling data can be processed and used in order to act as means for generating input for traditional transportation analysis models. Specifically, we propose a tailored set of mobility metrics and a computational pipeline including trip extraction, travel demand estimation as well as route and link travel flow estimation based on Call Detail Records (CDR) from mobile phones. The results are based on the analysis of data from the Data for development "D4D" challenge and include data from Cote dlvoire and Senegal. (C) 2016 Elsevier B.V. All rights reserved.Funding Agencies|Swedish Governmental Agency for Innovation Systems (VINNOVA)</p

Mobility modeling for transport efficiency : Analysis of travel characteristics based on mobile phone data

Signaling data from the cellular networks can provide a means of analyzing the efficiency of a deployed transportation system and assisting in the formulation of transport models to predict its future use. An approach based on this type of data can be especially appealing for transportation systems that need massive expansions, since it has the added benefit that no specialized equipment or installations are required, hence it can be very cost efficient. Within this context in this paper we describe how such obtained data can be processed and used in order to act as enablers for traditional transportation analysis models. We outline a layered, modular architectural framework that encompasses the entire process and present results from initial analysis of mobile phone call data in the context of mobility, transport and transport infrastructure. We finally introduce the Mobility Analytics Platform, developed by Ericsson Research, tailored for mobility analysis, and discuss techniques for analyzing transport supply and demand, and give indication on how cell phone use data can be used directly to analyze the status and use of the current transport infrastructure