Recent advances in information and communication technologies enable us to collect large spatio-temporal data of road traffic and to provide continuous, specific, historical, and wide-area datasets. Probe vehicle data are one of the most widely used passive datasets that can potentially observe wide ranges in the network, as they do not require fixed sensors. Such datasets potentially observe various phenomena which are caused by various reasons. However, it is not easy to detect significant incidents from such phenomenon due to the multi-dimensional form of the dataset and the large size required to scan the entire dataset. Incident detection is one of the significant approaches for monitoring of traffic flow and data cleansing to acquire “typical traffic situation” for analysis of measures and calibrations of models. Several previous studies have attempted to determine the local changes in the traffic flow. However, a few studies investigated a method to explore the significant incidents influencing wide-ranges of the spatio-temporal dimension which are possibly caused by the changes on either or both the service and demand side. This study proposes a data mining method to detect unusual situations in large-scale road network from a large volume of probe vehicle dataset. The method employs a nonparametric probabilistic function and the Markov random field to consider the spatio-temporal correlations in the probe vehicle dataset. The method is applied to an actual dataset provided by 40000 commercial vehicles all over Japan.
Detection method of wide-area incident with massive probe vehicle data
2017-10-01
918918 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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