Mining spatio-temporal co-location fuzzy congestion patterns from traffic datasets

Traffic congestion occurs when the total traffic volume on the road network exceeds the road capacity which disrupts normal traffic flow. The congestion patterns in traffic datasets need to be mined to effectively address urban traffic congestion problems. However, existing research work has failed to reasonably and accurately define "traffic congestion" and ignores the spatio-temporal attributes of the traffic flow data and the fuzziness of the traffic congestion concept. This paper presents the concept of spatio-temporal co-location fuzzy congestion patterns by introducing fuzzy set theory into the definition of traffic congestion to measure the degree of traffic congestion. The algorithm also adds the time attribute to the traditional spatial co-location pattern mining. Two algorithms are then presented for mining spatio-temporal co-location fuzzy congestion patterns. The methods are evaluated using real traffic datasets with the results showing that the methods provide better mining results than the existing methods. © 2020, Tsinghua University Press. All right reserved.