## 1 Introduction  
With the complexity of urban road traffic environments, road emergencies directly threaten the regular operation of urban road traffic. The first step in many articles of research [1; 2; 3; 4; 5] on the global analysis of urban road traffic is to build a proper network of urban road traffic systems based on road characteristics such as road traffic facility, geographical information, traffic volume, etc., and then analyze the study subject. Researchers can add or subtract information to be displayed in this network according to their needs. It helps visualize the road traffic operation. Road nodes are an essential part of the road network. They cooperate to monitor the traffic conditions in the target area to maintain the stable operation of the urban road traffic system. After a single road node detects a road emergency, once the monitoring target leaves the monitoring range, it cannot continuously track the target and analyze the subsequent impact of the event. Therefore, a collaborative monitoring method that can link other road nodes in the road network is needed to assess the road traffic situation in order to facilitate subsequent road protection and emergency treatment. Reducing the damage and loss of urban roads caused by emergencies is conducive to urban road traffic monitoring and early warning. There are mainly two solutions to the problem of multi-node collaborative monitoring [6]: one is that all nodes send monitoring data to the master node, and the master node conducts data fusion, analysis and judgment; the other is that each node in the network monitors and identifies the target and then sends the results of its own assessment to the master node, which completes the evaluation and decision by fusion. At present, most research on multi-node collaborative monitoring is carried out around the second idea. From the perspective of the fusion method, it mainly realizes decision and evaluation through indicators and analysis methods. In traffic situation assessment of roads in complex environments, indicators can be understood as factors to evaluate node performance, and the analysis method is mainly based on the corresponding algorithm combined with factor value and weight. Due to the particularity of risk assessment, it is impossible to obtain accurate evaluation results through only parameters; we also need to refer to experts' opinions for subjective evaluation, and reducing the emotional factors in the process of data processing is the focus of risk assessment research. Many domestic and foreign researchers have built evaluation models based on the spectral clustering K-nearest neighbor algorithm [7], multi-objective particle swarm [8; 9; 10], continuous time dynamic Bayesian network [11; 12; 13], multi-attribute decision making [14; 15] and other research methods. These methods have achieved excellent efforts in their respective fields, thus promoting the research progress of monitoring methods. Multi-attribute decision-making is still the main direction of traffic situation assessment [16; 17; 18; 19; 20; 21; 22]. In the field of road traffic, the core of the multi-attribute decision-making method is to calculate reasonable weights for different traffic attributes to assess road safety effectively. The method of combining a subjective and objective combination of assignment weights is mainly used in determining weights to determine the total weights of indicators. The subjective assignment is usually based on the Analytic Hierarchy Process (AHP) method for optimizing and improving the weight assignment [23; 24; 25; 26]. The objective assignment is generally based on the entropy method (EM) for innovation and combination optimization [27; 28; 29; 30]. In the process of calculating the weights, there are two points to improve. First, the subjective weight in the weighting is artificial judgment, which has intense subjectivity, so the determination of the combination coefficient is fuzzy.