Title
Transportation mode inference from anonymized and aggregated mobile phone call detail records
Abstract
Transportation mode inference is an important research direction and has many applications. Existing methods are usually based on fine-grained sampling - collecting position data from mobile devices at high frequency. These methods can achieve high accuracy, but also incur cost and complexity in terms of the computational resource and system implementation. Finally, fine-grained sampling is not always available, especially for large-scale deployment. This paper proposes a novel method to infer transportation mode based on coarse-grained call detail records. The method allows estimating the transportation mode share from a given origin to a given destination, looking also at how the share changes over time. The method can achieve acceptable accuracy with trivial cost and complexity. It is suitable for the statistical analysis on transportation modes of a large population. The method can also be used as a complementary tool in situations where fine-grained sampling is unavailable or the balance between accuracy and complexity is critical. A case study using real call detail records data for the city of Boston shows the performance of the proposed method.
Year
DOI
Venue
2010
10.1109/ITSC.2010.5625188
ITSC
Keywords
Field
DocType
mobile computing,mobile handsets,statistical analysis,traffic information systems,complementary tool,computational resource,fine-grained sampling,large-scale deployment,mobile devices,mobile phone call detail records,system implementation,transportation mode inference,transportation mode share,data collection,global positioning system,mobile device,high frequency,accuracy,transportation,data mining
Mobile computing,Data mining,Population,Data collection,Inference,Simulation,Mobile device,Sampling (statistics),Mobile phone,Engineering,Computational resource
Conference
ISSN
ISBN
Citations 
2153-0009
978-1-4244-7657-2
36
PageRank 
References 
Authors
2.09
14
4
Name
Order
Citations
PageRank
Huayong Wang1362.09
Calabrese, F.2362.77
Di Lorenzo, G.3644.32
Carlo Ratti41211113.38