Dynamic identification of logistics critical roads based on BeiDou trajectory data

Abstract

AbstractThe convergency and diffusion behaviours of truck information derived from BeiDou trajectory data are extremely useful in improving the capacity of logistics critical roads (LCR) in industrial parks. In this paper, a novel dynamic identification method of LCR is proposed for traffic refined organization in industrial parks. The structural importance method is built in the first step to assess the theoretical importance of road network structure. The logistics attractiveness of the road is then measured using a business circle model with built environment and truck behaviour factors. In the third step, the real‐time road selectivity is calculated using the K‐Average Nearest Neighbour Density‐Based Spatial Clustering of Applications with Noise (KANN—DBSCAN) algorithm to assess road vulnerability. Finally, LCR are identified by solving the road criticality based on the aforementioned parameters. The method is validated by comparing the predicted LCR with the factual LCR in a case study of Hongqi town and relative BeiDou data. The conformity of the proposed method with reality is > 85%. The authors reveal some rules at various times: Logistics factors are more important in LCR in industrial parks. LCR with heavy passenger traffic have a significant tide, and those with heavy freight traffic are strongly correlated with production plans.