We propose a methodology to identify and analyze similarities in long-term trends of road links using travel speed data. The methodology employs seasonal trend decomposition by LOESS (STL) to extract trend curves from travel speed time series, clearly representing underlying long-term patterns and behavior. These trend curves are then analyzed using the k-means clustering algorithm to group road links based on long-term trends. The resulting clusters offer valuable insights for long-term planning in traffic management, infrastructure development, and identifying potential bottlenecks within the road network. To demonstrate the proposed methodology, we applied it to travel speed data from the European road E4, focusing on the route between Södertälje and Stockholm. The analysis reveals distinct trend characteristics and behaviors, highlighting the diverse nature of traffic patterns in different road links.