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A clustering algorithm for urban taxi carpooling based on data field energy and point spacing is proposed to solve the clustering problem of taxi carpooling on urban roads. The data field energy function is used to calculate the field energy of each data point in the passenger taxi offpoint dataset. To realize the clustering of taxis, the central point, outlier, and data points of each cluster subset are discriminated according to the threshold value determined by the product of each data point field values and point spacing. The classical algorithm and proposed algorithm are compared and analyzed by using the compactness, separation, and Dunn validity index. The clustering results of the proposed algorithm are better than those of the classical clustering algorithm. In the case of cluster numbers 25, 249, 409, and 599, the algorithm has good clustering results for the taxi trajectory dataset with certain regularity in space distribution and irregular distribution in time distribution. This algorithm is suitable for the clustering of vehicles in urban traffic roads, which can provide new ideas and methods for the cluster study of urban traffic vehicles. [ABSTRACT FROM AUTHOR]