Critical intersection identification and macroscopic fundamental diagram estimation based on trajectory data

Effective management and planning of traffic congestion is crucial to improving urban quality of life and ensuring sustainable development. First, the road network of Guiyang City is divided into regions, and a fuzzy temporal network model, namely the Fuzzy Supra-adjacency Matrix (FSAM) model, is introduced to identify critical intersections. Additionally, an improved trip segmentation method is proposed to segment the trip trajectory data. Secondly, two temporal-spatial related penetration rates are proposed to be applied to taxi GPS trajectory data for estimating regional Macroscopic Mundamental Diagram (MFD). Finally, the average flow and density of each region are calculated using the temporal-spatial related penetration rate, and the single-day MFD of each region is estimated. In summary, based on taxi GPS trajectory data, first, the application of temporal-spatial related penetration rates to taxi GPS trajectory data to estimate MFD can provide a theoretical basis for intelligent ride-sharing systems. Secondly, the proposed trip segmentation method enables the segmentation of GPS trip data in non-passenger carrying states. Finally, the proposed identification of critical intersections before calculating macro traffic variables not only saves time but also further improves the estimation accuracy of MFD.