Tracking the Evolution of Congestion in Dynamic Urban Road Networks
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Liu, Chengfei
Vu, Hai L
Islam, Md Saiful
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Kavita Ganesan, Chase Geigle, Xia Ning
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IUPUI, Indianapolis, IN
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Abstract
The congestion scenario on a road network is often represented by a set of differently congested partitions having homogeneous level of congestion inside. Due to the changing traffic, these partitions evolve with time. In this paper, we propose a two-layer method to incrementally update the differently congested partitions from those at the previous time point in an efficient manner, and thus track their evolution. The physical layer performs low-level computations to incrementally update a set of small-sized road network building blocks, and the logical layer provides an interface to query the physical layer about the congested partitions. At each time point, the unstable road segments are identified and moved to their most suitable building blocks. Our experimental results on different datasets show that the proposed method is much efficient than the existing re-partitioning methods without significant sacrifice in accuracy.
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CIKM'16: PROCEEDINGS OF THE 2016 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT
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24-28-October-2016
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Data management and data science not elsewhere classified
Data mining and knowledge discovery
Spatial data and applications