Abstract:

Based on truck and private car trajectory data， a trip group classification method using Clustering by fast search and find of density peaks（CFSFDP） was proposed， in which seven trip indicators—including trip start time， trip end time， and total operation duration—were employed as feature variables. The trucks and private cars were respectively divided into three trip groups. A BP neural network-based vehicle trip group identification model was subsequently established， with trip characteristic indicators serving as inputs and the density clustering algorithm's classification labels as outputs， thereby enabling rapid identification of different trip groups within the same vehicle type.The results indicated that the proposed CFSFDP-BP-based trip group classification and identification method demonstrated satisfactory prediction accuracy and reliability. An identification accuracy of 0.991 was achieved for truck trip groups， while 0.988 was obtained for private car trip groups， with superior performance observed for truck trip group identification compared to private cars. Three distinct truck trip groups were identified： flexible-low intensity， traditional-medium intensity， and traditional-high intensity. Meanwhile， three private car trip groups were classified as commute-low intensity， commute-medium intensity， and flexible-high intensity. These findings are expected to support traffic management authorities in formulating refined traffic management strategies and improving traffic operational efficiency.

Key words: road traffic, trucks and private cars, travel characteristics, clustering by fast search and find of density peaks, identification of travel groups