Alleviating public traffic congestion is an efficient and effective way to improve the travel time reliability and quality of public transport services. The existing public network optimization models usually ignored the essential impact of public traffic congestion on the performance of public transport service. To address this problem, this study proposes a data-based methodology to estimate the traffic congestion of road segments between bus stops (RSBs). The proposed methodology involves two steps: (1) Extracting three traffic indicators of the RSBs from smart card data and bus trajectory data; (2) The self-organizing map (SOM) is used to cluster and effectively recognize traffic patterns embedded in the RSBs. Furthermore, a congestion index for ranking the SOM clusters is developed to determine the congested RSBs. A case study using real-world datasets from a public transport system validates the proposed methodology. Based on the congested RSBs, an exploratory example of public transport network optimization is discussed and evaluated using a genetic algorithm. The clustering results showed that the SOM could suitably reflect the traffic characteristics and estimate traffic congestion of the RSBs. The results obtained in this study are expected to demonstrate the usefulness of the proposed methodology in sustainable public transport improvements. these network optimization models developed in the above-mentioned articles had been usually applied in uncongested road segments.Public traffic condition refers to the traffic volume of the road network and its dynamic spatial-temporal distribution, which can reflect the degree of congestion [8]. Recently, the traffic congestion impacts of bus operations on a road segment or a corridor have been investigated by researchers [9,10]. These congestion effects mainly include the effects of bus stop design, bus travel time, and bus priority options such as exclusive bus lanes or priority signals for buses [9]. Understanding these congestion impacts can help operators to identify the effectiveness of transport network optimization in relieving congested areas or congested routes. For example, under congested traffic conditions, it is difficult for buses to return to the driving lane, which leads to a longer travel time after picking-up/dropping off passengers at stops [11]. Furthermore, the bus travel time variation dominated by traffic congestion often results in unreliable service, which has negative impacts on both the operators and passengers [12]. Previous studies have pointed out that well-located stops have the potential to alleviate the impact of traffic congestion [3]. Therefore, it is critical to optimize PTN by considering the impact of traffic congestion in order to achieve a high level of public transport service and improve travel time reliability.In this study, we proposed a data-based methodology to estimate the traffic congestion of road segments between bus stops (RSBs) using a self-organizing feature map (SOM). The SOM was used to cluster and effective...