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Issue No. 02 - June (2017 vol. 3)
ISSN: 2332-7790
pp: 181-193
Somwrita Sarkar , University of Sydney, Camperdown, NSW, Australia
Sanjay Chawla , Qatar Computing Research Institute, Hamad Bin Khalifa University, Ar-Rayyan, Doha, QatarQatar
Shameem Ahmad , University of Technology of Compiègne, Compiègne, France
Jaideep Srivastava , University of Minnesota, Computing Research Institute, Minneapolis, Ar-Rayyan, MN, QatarQatar
Hosam Hammady , Qatar Computing Research Institute, Ar-Rayyan, Qatar
Fethi Filali , Qatar Mobility Innovations Center, Doha, Qatar
Wasim Znaidi , Qatar Mobility Innovations Center, Doha, Qatar
Javier Borge-Holthoefer , Internet Interdisciplinary Institute (IN3-UOC), Universitat Oberta de Catalunya, Barcelona, Spain
Mobility in a city is represented as traffic flows in and out of defined urban travel or administrative zones. While the zones and the road networks connecting them are fixed in space, traffic flows between pairs of zones are dynamic through the day. Understanding these dynamics in real time is crucial for real time traffic planning in the city. In this paper, we use real time traffic flow data to generate dense functional correlation matrices between zones during different times of the day. Then, we derive optimal sparse representations of these dense functional matrices, that accurately recover not only the existing road network connectivity between zones, but also reveal new latent links between zones that do not yet exist but are suggested by traffic flow dynamics. We call this sparse representation the time-varying effective traffic connectivity of the city. A convex optimization problem is formulated and used to infer the sparse effective traffic network from time series data of traffic flow for arbitrary levels of temporal granularity. We demonstrate the results for the city of Doha, Qatar on data collected from several hundred bluetooth sensors deployed across the city to record vehicular activity through the city's traffic zones. While the static road network connectivity between zones is accurately inferred, other long range connections are also predicted that could be useful in planning future road linkages in the city. Further, the proposed model can be applied to socio-economic activity other than traffic, such as new housing, construction, or economic activity captured as functional correlations between zones, and can also be similarly used to predict new traffic linkages that are latently needed but as yet do not exist. Preliminary experiments suggest that our framework can be used by urban transportation experts and policy specialists to take a real time data-driven approach towards urban planning and real time traffic planning in the city, especially at the level of administrative zones of a city.
Urban areas, Roads, Real-time systems, Sensors, Sparse matrices, Vehicle dynamics, Planning
Somwrita Sarkar, Sanjay Chawla, Shameem Ahmad, Jaideep Srivastava, Hosam Hammady, Fethi Filali, Wasim Znaidi, Javier Borge-Holthoefer, "Effective Urban Structure Inference from Traffic Flow Dynamics", IEEE Transactions on Big Data, vol. 3, no. , pp. 181-193, June 2017, doi:10.1109/TBDATA.2016.2641003
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