Bus passenger classification method based on spatial and temporal behavior regularity mining

Using the advanced public transportation system (APTS) to extract individual passenger travel information, the bus trip-chain was constructed, and the method of bus passenger classification based on the spatial and temporal behavior regularity mining (STBRM) was examined. Time series were used to characterize the travel temporal characteristics of passengers, and the cross-correlation distance (CCD) algorithm was used to calculate the temporal regularity of individual passengers. The density-based spatial clustering of applications with noise (DBSCAN) algorithm was used to mine the travel spatial regularity of individual passengers. According to the travel intensity and spatial-temporal regularity, bus passengers were divided into five groups, including rare travel, regular time, regular space, regular space-time, and irregular. Taking the numbers of travel days, similar boarding times, and similar boarding bus stops as the clustering indexes, the K-means++ algorithm was applied to classify passengers into three categories, namely high regularity, medium regularity, and low regularity. The classification result of the proposed method was compared with the K-means++ clustering method, and the relationship between the two methods was revealed. Research results show that when the time division length is 5 min and the temporal regularity judgment threshold is 3.0, the CCD algorithm has the best identification effect of passengers with temporal shifted patterns. Compared with the DBSCAN algorithm, the recognition rate improves by 14.64%. Increasing the time window length can improve the stability of travel spatial and temporal regularity judgment. When the time window length reaches three weeks, the proportion of passengers in a spatial pattern decreases slowly and becomes stable after six weeks. When the time window length reaches two weeks, the proportion of passengers in a temporal pattern increases slowly and becomes stable after four weeks. The number of passengers for regular time, regular space, and regular space-time accounts for only 30.4% of the total number of passengers, but their number of trips accounts for 84.7% of the total number of trips, therefore, the bus dependence is very high, which should be taken as the key service object of public transport institutions. The classification results of the proposed method and K-means++ clustering method have a strong correlation, and the groups with very high or very low regularity for the two methods have a high degree of overlap. 13 tabs, 9 figs, 31 refs.