In order to identify the traffic operation law of urban arterial road and support basis for traffic management departments to formulate relevant traffic demand management policies, a vehicle travel group identification model of urban arterial road based on combined model was proposed. In this study, a travel characteristic indicator system was constructed from dimensions of travel intensity, travel time, travel habits for comprehensively describing the travel behavior based on the traffic bayonet data of Qingdao Jiaozhou Bay Tunnel. The redundant indicator was eliminated based on the correlation analysis to avoid the impact on identification research. For the mixed attribute travel characteristic indicator data, the improved K-prototypes algorithm was used to effectively classify the vehicle travel groups, and combined with GBDT, the identification model based on improved K-prototypes and GBDT was established. By randomly selecting 10 000 samples to conduct identification research, the result shows that there are 5 vehicle travel groups for the road in this research, including high-frequency commuter groups, low-frequency commuter groups, operating groups, frequency stable groups, and ordinary groups. For the 5 vehicle travel groups, the average identification accuracy rate exceeds 97.75%, and the highest identification accuracy rate can reach 99.47%.