Processes and events in the center: a taxi trajectory-based approach to detecting traffic congestion and analyzing its causes
Yufeng He, Barbara Hofer, Yehua Sheng, Yue Yin, Hui Lin
Abstract
A novel approach is introduced for the detection of the location and direction of traffic congestion using GPS data from taxis. This approach uses a dynamic model that conceptualizes events, processes, and states. The states are the locations of the taxis, the processes are the motion of taxis, and the events are congestion. The model is implemented as a graph database, which represents the relationships between states, events, processes, and things (such as points of interest and road grid). Algorithms for constructing and updating the relationships and taxi behaviors dynamic retrieval method in Neo4j are presented and are used to demonstrate the capabilities in dynamic expression and reasoning. An implementation of Shanghai in 2015 finally demonstrated the ability of congestion direction detection and the cause searching of traffic congestion.