## 2 Literature Review  
### Analysis of Spatiotemporal Operation Characteristics of the Traffic State  
Much research has been conducted on typical traffic state classification parameters and methods, as shown in Table 1. [PERSON] et al. [1] analyzed the spatiotemporal distribution characteristics of chronic traffic congestion by simplifying a complex traffic network into the most frequently congested parts (clusters) based on real-time collected data and introducing clustering methods. [PERSON] et al. [2] proposed a method based on frequent pattern mining in information science for analyzing traffic sensor data to understand traffic congestion patterns in cities. The feasibility and effectiveness of the proposed method were evaluated through an analysis of traffic sensor data in Okinawa (Japan). [PERSON] [3] created a model for mining traffic period patterns that accounted for the impact of monsoons on road traffic, presenting an overall traffic scenario analysis and more accurately analyzing the traffic patterns of developing cities with a high population density such as Mumbai. Based on the work by Qingdao, [PERSON] [4] combined traffic performance index (TPI) data with a hierarchical clustering algorithm to extensively study traffic congestion in urban commercial districts. In a study in Beijing, [PERSON] et al. [5; 6] constructed a traffic state classification system based on the flow, speed, time occupancy, and road network redundancy and analyzed the system using an improved fuzzy clustering algorithm. Clustering of actual traffic parameters detected in some areas of the Beijing expressway network allowed the states of traffic flow data to be determined. [PERSON] et al. [7] used floating vehicle data and real-time traffic data to analyze the traffic status of schools around roads through spatiotemporal clustering, revealing the distribution characteristics of traffic status around schools in Beijing. [PERSON] et al. [8] estimated traffic speeds from GPS traces and proposed algorithms based on artificial neural networks and pattern matching for short-term travel speed predictions in Italy. [PERSON] et al. [9] considered the impact of road grades on congestion indicators, selected the INRIX road congestion index system, used taxi trajectory data to calculate the road congestion index, and proposed an abnormal judgment method for the road congestion index and a congestion index correction method based on regional continuity. [PERSON] et al. [10] introduced the spatiotemporal cumulative index of traffic congestion to identify and quantitatively analyze the operational status of regional transportation; established a functional relationship between congestion sources and congestion evaluation points to construct a visual model; used gradient direction histograms and principal component analysis to extract features from traffic operation status data; and used the Gaussian mixture clustering method to cluster the feature data and classify the spatial distribution pattern of regional traffic congestion. [PERSON] et al. [11] presented an expert system for detecting traffic congestion and incidents from real-time GPS data collected from GPS trackers or drivers' smartphones. [PERSON] et al. [12] proposed an approach for detecting traffic congestion from taxis' GPS trajectories at the turn level by analyzing features of GPS trajectories and identifying valid trajectory segments; they also detected congested trajectory segments of three different intensities. In addition, many scholars have explored traffic congestion from different perspectives. For example, [PERSON] et al. [13] explored and analyzed the spatiotemporal distribution characteristics, laws, and causes of traffic congestion in Xuchang city based on the real-time traffic data over one week.Sun et al.