One can use applications that consider only the present congestion situations on suggested roads to navigate within cities. However, few of these applications reroute users according to real-time traffic congestion as the trip progresses [4; 5]. Hence, systems and algorithms must be designed in such a way that people can avoid traffic congestion in real time. In addition, other recent studies have attempted to predict traffic congestion in cities. A more specific approach for tackling the problem of linking traffic jams is the partitioning of the links according to traffic congestion in order to devise control strategies for solving this problem. Partitioning a network of roads into homogeneous groups can be extremely useful for traffic control considering that the congestion is spatially correlated in adjacent roads and that it propagates with different speeds [6]. In [7], the authors designed a network partition method by using the normalized cut. This method was further adapted and improved, where the authors developed a method based on the definition of the snake similarity matrix and application of the normalized cut algorithm in order to detect directional congestion within clusters and improve the performance of low connectivity networks [8]. Previous work on trajectory data clustering has mainly focused on the case where objects move freely in Euclidean space [9; 10]. However, these approaches failed to account for an underlying network that constrains movement. However, network constraints play a crucial role in determining the similarity measure between the trajectories to be clustered. Another method was proposed in which the authors elaborated an approach based on discovering the clusters of trajectories by grouping similar trajectories that visited the same links of the network [11]. Then, they extended their work to the case of links, where they grouped the common links that were visited by a large number of trajectories [12]. Therefore, the community-detection algorithm used in the clustering step of their proposed framework can be sensitive to the presence of noise, which can degrade the quality of clusters. However, these methods cannot be directly applied to dynamic frameworks. In order to study the feasibility of the control strategy to improve the transportation networks' performance, in this paper, we focus on the clustering of transportation networks into homogeneous traffic partitions that evolve over time [13]. Data are essential to the planning and management of issues related to transportation networks. Instead of relying on conventional models, transportation research is increasingly data-driven. With the growing quantity and quality of data being collected from intelligent transportation systems, data-driven transportation research relies on new generation techniques to analyze these data. Recently, data-driven innovation in transportation science follows two main approaches: the technology-oriented approach to enhance the data resources available to the platform [14] and the methodology-oriented approach to improve the software part of the platform [15]. Exploratory data analysis is a heuristic search technique for finding significant relationships between variables in large data sets. Its efficiency is key to deriving insights from big data. It is the first technique when approaching the data. Exploratory data analysis has the objective of identifying attributes in a data set, using univariate data analysis to characterize the data, detecting and minimizing the impact of missing and aberrant values, detecting errors and, finally, combining features to generate new features [16]. In our case, to perform network partitioning, both the network topology and the link speeds for all time periods are needed. Therefore, data preparation is needed to create a validated data set for evolutionary spectral clustering. Data preparation aims to remove travel time outliers and coarsen the large-scale network in order to improve the computation time and to estimate link speeds.