Traffic congestions in urban road traffic networks originate from some crowded road segments with crucial locations, and diffuse towards other parts of the urban road network creating further congestions. This behavior of road networks motivates the need to understand the influence of individual road segments on others in terms of congestion. In this paper, we investigate the problems of global influence ranking and local influence ranking of road segments. We propose an algorithm called RoadRank to compute the global influence scores of each road segment from their traffic measures, and rank them based on their overall influence. To identify the locally influential road segments, we also propose an extension called distributed RoadRank, based on road network partitions. We perform extensive experiments on real SCATS datasets of Melbourne. We found that the segments of Batman Avenue, Footscray Road, Punt Road, La Trobe Street, and Victoria Street, are highly influential in the early morning times, which are well known as congestion hotspots for both the network operators and the commuters. Our promising results and detailed insights demonstrate the efficacy of our method.