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AuthorStanojevic, Rade
AuthorAbbar, Sofiane
AuthorThirumuruganathan, Saravanan
AuthorChawla, Sanjay
AuthorFilali, Fethi
AuthorAleimat, Ahid
Available date2024-10-23T05:10:38Z
Publication Date2018
Publication NameSIAM International Conference on Data Mining, SDM 2018
ResourceScopus
URIhttp://dx.doi.org/10.1137/1.9781611975321.15
URIhttp://hdl.handle.net/10576/60457
AbstractIn this paper we address the challenge of inferring the road network of a city from crowd-sourced GPS traces. While the problem has been addressed before, our solution has the following unique characteristics: (i) we formulate the road network inference problem as a network alignment optimization problem where both the nodes and edges of the network have to be inferred, (ii) we propose both an offline (Kharita) and an online (Kharita) algorithm which are intuitive and capture the key aspects of the optimization formulation but are scalable and accurate. The Kharita in particular is, to the best of our knowledge, the first known online algorithm for map inference, (iii) we test our approach on two real data sets and both our code and data sets have been made available for research reproducibility.
Languageen
PublisherSociety for Industrial and Applied Mathematics Publications
SubjectData mining
Inference engines
Roads and streets
Traffic control
GPS traces
MAP inferences
Network alignments
On-line algorithms
Optimization formulations
Real data sets
Reproducibilities
Road network
Space division multiple access
TitleRobust road map inference through network alignment of trajectories
TypeConference
Pagination135-143
dc.accessType Open Access


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