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Framework for fusing traffic information from social and physical transportation data
2018
PLoS ONE
Tremendous volumes of messages on social media platforms provide supplementary traffic information and encapsulate crowd wisdom for solving transportation problems. However, social media messages manifested in human languages are usually characterized with redundant, fuzzy and subjective features. Here, we develop a data fusion framework to identify social media messages reporting non-recurring traffic events by connecting the traffic events with traffic states inferred from taxi global
doi:10.1371/journal.pone.0201531
pmid:30071064
pmcid:PMC6072031
fatcat:5apqxd3pdvcgthgowgf7mssfya