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Title: Towards Estimating Urban Macroscopic Fundamental Diagrams From Mobile Phone Signaling Data: A Simulation Study
Accession Number: 01628794
Record Type: Component
Abstract: The authors propose a novel way of estimating Macroscopic Fundamental Diagrams (MFD), or often also called Network Fundamental Diagrams, from mobile phone signaling data under the condition that vehicles can be identified from the data stream. The authors run a preliminary simulation study to identify whether MFDs can be constructed from this type of data. The authors co-simulate the road traffic in Luxembourg City and one mobile operator’s LTE network user plane, and show that mobile phone base station clusters cover road network partitions of coherent behavior. The results indicate that the relationships between handovers (flow) and attached phones (density) in these base station clusters constitute MFDs. The authors validate the results by comparing estimated velocities in parts of the network to the ground-truth simulated velocities. The results indicate a promising direction for research, as the ubiquity of mobile network infrastructure enables it as a promising, distributed data source for traffic estimation.
Supplemental Notes: This paper was sponsored by TRB committee AHB45 Standing Committee on Traffic Flow Theory and Characteristics.
Monograph Title: Monograph Accession #: 01618707
Report/Paper Numbers: 17-05246
Language: English
Corporate Authors: Transportation Research Board 500 Fifth Street, NW Authors: Derrmann, ThierryFrank, RaphaëlViti, FrancescoPagination: 13p
Publication Date: 2017
Conference:
Transportation Research Board 96th Annual Meeting
Location:
Washington DC, United States Media Type: Digital/other
Features: Figures; Maps; References; Tables
TRT Terms: Geographic Terms: Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Planning and Forecasting
Source Data: Transportation Research Board Annual Meeting 2017 Paper #17-05246
Files: TRIS, TRB, ATRI
Created Date: Dec 8 2016 12:03PM
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