Table 1 - uploaded by Johan Wideberg
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Acquiring high quality origin destination information for the vehicle traffic in a geographic area is a tedious and costly task. Traditional methods are expensive, time-consuming and generally only present a snapshot of the traffic situation at a certain time. The technique developed in this paper exploits the use of data already at hand in a GSM n...
Citations
... Beside origin-destination matrices, multiple studies are dealing with the application of telecom big data in public transport planning [24][25][26][27], urban mobility planning [28,29], transport mode detection [27,[30][31][32], urban mobility estimation [33], traffic flow analysis [34][35][36][37] and the reconstruction of human mobility in general [38][39][40][41][42]. ...
The transport system is sensitive to external influences generated by various economic, social and environmental changes. The society and the environment are changing extremely fast, resulting in the need for rapid adjustment of the transport system. Traffic system management, especially in urban areas, is a dynamic process, which is why transport planners are in need of a proven and validated methodology for fast and efficient transport data collection, fusion and analytics that will be used in sustainable urban mobility policy creation. The paper presents a development of a methodology in data rich reality that combines traditional and novel data science approach for transport system analysis and planning. The result is overall process consisting of 150 steps from first desktop research to final solution development. It enables urban mobility stakeholders to identify transport problems, analyze the urban mobility situation and to propose dedicated measures for sustainable urban mobility strengthening. The methodology is based on a big data research and analysis on anonymized big data sets originating from mobile telecommunication network, where the extraction of mobility data from the big dataset is the most innovative part of the proposed process. The extracted mobility data were validated through a “conventional” field research. The methodology was, for additional testing, applied in a pilot study, performed in the City of Rijeka in Croatia. It resulted in a set of alternative measures for modal shift from passenger cars to sustainable mobility modes, that were validated by the local public and urban mobility stakeholders.
... Techniques and models for mobile device flow analysis [2] have mostly focused on predictive models aiming at optimizing some mobile network system parameters such as cell dimensioning, antenna distribution, and load balancing [3], [4]. ...
... On the other hand a number of projects [3], [5] try to use the cellular network traffic to estimate different road traffic and transportation related quantities [6], [7], [8], such as speed and travel times between destinations [9], [10], [11], origin/destination (O/D) matrices [12], [2], road traffic congestions [11], road traffic volume or density [13], [14], etc. ...
... Moreover some issues such as privacy and scalability are also problematic. For instance, techniques for inferring O/D matrices [2] uses information about the Location Areas (LA) over the time, where a LA is a set of cells where the mobile terminal is assumed to be located. In other words the algorithm needs to identify time, origin and destination LAs of the whole trip made by each single telephone, thus representing a remarkable privacy infringement. ...
Nearly all the members of adult population in major developed countries transport a GSM/UMTS mobile terminal which, besides its communication purpose, can be seen as a mobility sensor, i.e. an electronic individual tag. The temporal and spatial movements of these mobile tags being recorded allows their flows to be analyzed without placing costly ad hoc sensors and represents a great potential for road traffic analysis, forecasting , real time monitoring and, ultimately, for the analysis and the detection of events and processes besides the traffic domain as well. In this paper a model which integrates mobility constraints with cellular networks data flow is proposed in order to infer the flow of users in the underlying mobility infrastructure. An adaptive flow estimation technique is used to refine the flow analysis when the complexity of the mobility network increases. The inference process uses anonymized temporal series of cell handovers which meet privacy and scalability requirements. The integrated model has been successfully experimented in the domain of car accident detection.
