This study aims to investigate the relationship between the Multiple Centrality Assessment index and the level of traffic congestion using the data from the urban traffic networks in Harbin, China. By utilizing the standard 'primal' format in the traffic networks, the street centrality can be measured by three types of indices, the Closeness, Straightness and Betweenness centralities. These centrality indices were calculated by Python program based on Arcgis10.1. The correlations between congestion level and the street centralities were analyzed based on the collected traffic data on the top ten congested traffic links on weekdays in urban areas. The results indicated that the street centrality indices and congestion level are positively related. Among three centrality indices, the global betweenness exhibited a higher correlation with the congestion time delay index than the global closeness did. The study demonstrated a new aspect to investigate traffic congestion and provided useful information for transportation planning.