Spatial-Temporal Congestion Identification Based on Time Series Similarity Considering Missing Data.

ABSTRACT: Traffic congestion varies spatially and temporally. The observation of the formation, propagation and dispersion of network traffic congestion can lead to insights about the network performance, the bottleneck dynamics etc. While many researchers use the traffic flow data to reconstruct the congestion profile, the data missing problem is bypassed. Current methods either omit the missing data or supplement the missing part by average etc. Great error may be introduced during these processes. Rather than simply discarding the missing data, this research regards the data missing event as a result of either the severe congestion which prevent the floating vehicle from entering the congested area, or a type of feature of the resulting traffic flow time series. Hence a new traffic flow operation

SUBMITTER: Qi H 

PROVIDER: S-EPMC5029889 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature