# Risk Causal Analysis of Traffic-Intensive Waters Based on Infectious Disease Dynamics
## 1 Introduction  
With the rapid development of the shipping industry, the navigation safety of ships has drawn much attention from both academia and industry. The research of navigation safety in the Yangtze River Basin has increased year by year [1]. At the same time, due to the fact that water transportation safety, as part of public safety, is closely related to social stability, groups of experts at home and abroad have conducted certain research in water transportation safety management, education, and technology [2]. At present, with the application and development of new technologies, such as artificial intelligence, ship modernization has been continuously improved, especially in the domains of ship design, ship stability, communication, and navigation equipment [3]. However, the issue of water traffic safety is still a hot topic. Major traffic accidents occur occasionally, which have become the main contributor restricting the long-term development of shipping industries. Therefore, studying the risks of ship navigation in traffic-intensive waters could be helpful to reduce the occurrence of water traffic accidents, improve the level of water traffic supervision, and provide intellectual support for emergency response. The likelihood of accidents in traffic-intensive waters is relatively high, which deserves more attention. Thus, numerous studies have been conducted in this field. [PERSON] et al. [4] combined theformal safety assessment (FSA) and Bayesian network (BN) to evaluate the risks of ship navigation in the Yangtze River area. [PERSON] et al. [5] used data envelopment analysis (DEA) to evaluate the safety of water traffic in the state of a dynamic navigation environment. Through the example verification, the conclusion that the safety level and the traffic flow have an inevitable relationship is obtained. In view of the safety hazard caused by the increase of the number of navigable ships, [PERSON] et al. [6] constructed a traffic unit complexity model to evaluate the water traffic conditions, quantified the complexity of the ship traffic flow by simulating the traffic flow risk characteristics, described the congestion degree of navigable ships, and identified potential collision risk factors. In traffic-intensive waters, the risk of congestion is more frequent, which will lead to secondary disasters. In order to control such risks, [PERSON] et al. [7] identified the key factors affecting the saturation of traffic flow based on the historical data, and used the Bayesian network to predict the probability of congestion in the Yangtze River. In their study, the multi-factor coupling relationships are considered [8; 9]. With respect to the analysis of ship navigational risks, [PERSON] et al. [10] constructed a risk assessment model of ship navigation based on the Bayesian theory, and studied the degree of ship congestion. [PERSON] et al. [11] constructed a ship queuing model based on the characteristics of the Bosphorus and verified the simulation results of traffic flow based on multiple sets of historical data. [PERSON] et al. [12] used the analytic hierarchy process to quantify the shipping risk based on the safety characteristics of ship behaviors in port waters. [PERSON] et al. [13] applied Markov chain Monte Carlo methods to simulate and describe the dynamic risk of ship navigation systems. With the application of new artificial intelligence algorithms, it has become a trend to study risk mechanisms by using a combination of quantitative data and multidisciplinary approaches. At present, one of the mostly used methods in the mutual conversion between quantitative data and qualitative concepts is cloud models, which can better solve the problem of randomness and fuzzy recognition [14]. [PERSON] et al. [15] used a clustering algorithm to evaluate risk [16], which had the advantages of fast processing speed and high intelligence.