To address the limitations of dynamic time warping and its conventional weighted variants—namely, insufficient sensitivity to critical time periods and suboptimal weight allocation mechanisms—in the context of urban traffic state time-series measurement, this paper proposes an enhanced DTW method based on Gaussian function weighting, termed Gaussianweighted DTW, to improve the discriminative accuracy of trajectory similarity measurement. The proposed approach constructs a time-dependent multi-peak Gaussian weighting function that integrates the quasi-Gaussian distribution characteristics of urban traffic flow and prior knowledge of peak-hour patterns into the sequence alignment process, thereby nonlinearly amplifying the contribution of peak traffic periods in distance computation. Experiments are conducted using taxi GPS trajectory data from Chengdu, from which hourly origin-count time series are constructed on a spatial grid basis. Coupled with the K-Medoids clustering algorithm, the performance of GS-WDTW is quantitatively evaluated against DTW and WDTW using the silhouette score and Calinski-Harabasz index. Results show that, at the optimal cluster number K=4, GS-WDTW achieves a 39.9% and 13.1% improvement over DTW, and a 41.1% and 13.0% improvement over WDTW, in terms of SS and CHI, respectively, demonstrating significantly enhanced capability in identifying spatiotemporal characteristics of travel patterns. Spatial analysis further confirms a high degree of consistency between the clustering results and the actual urban functional zone distribution. The GS-WDTW method thus enables more precise capture of critical nonlinear features in time-series data, offering a valuable reference for state perception, resource optimization, and functional zone identification in intelligent transportation systems.