## 5 Conclusions and Future Work  
### Conclusions  
The complexity of urban road traffic means that decision makers need to assess the road traffic situation under emergencies, and the assessment results will help them in traffic management. The accuracy of the assessment results is essential to determine the management of road traffic decisions under emergencies. The paper constructs an urban road monitoring network by road geographic location relationship and studies the assessment of the urban road situation under emergencies by reviewing the correlation between emergencies and traffic data. Through these logical relationships, a collaborative monitoring model of urban road traffic dynamics under emergencies is established to comprehensively assess road traffic conditions. The following conclusions can be drawn:  
1. Previous studies have more often used individual roads where emergencies occur to build road traffic situation assessment models, and they focus on the completeness and real-time nature of the entire model process. This study proposes a collaborative monitoring method based on the traffic road network. Compared with previous studies, the data input of this method is more diversified, which is reflected in both the types of monitoring indicators and monitoring targets. The previous studies were evaluated by single indicators, so the results were more dependent on the attributes and accuracy of the monitoring indicators. In contrast, the results are more informative by using multiple indicator data fusion calculation. The monitoring target diversification refers to the data input sources from different urban road nodes. The data obtained in this way will be more reliable and comprehensive. This method starts with a single road node monitoring an emergency, goes through collaborative nodes to provide data support and finally calculates the impact of the event on the whole traffic road network. It monitors urban road traffic in a multi-node monitoring way. 2. The GT-AHP-EM method is proposed in the process of indicator assignment for situational prediction. It not only reduces the artificial factor of subjective assignment method, but also fully considers the influence of objective data. The method reconciles the contradiction between subjective weights and objective weights and improves the scientific rationality of the assignment to a certain extent. The experimental results show that the method uses traffic flow as the model input for traffic state identification in the process of traffic situational awareness, and the output results are \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & _Accuracy_ & _Precision_ & _Recall_ & _F1 Score_ \\ \hline AHP & 0.7336 & 0.7336 & 0.6487 & 0.6874 \\ EM & 0.7539 & 0.7539 & 0.7491 & 0.7510 \\ EM-VC & 0.7808 & 0.7808 & 0.8213 & 0.8005 \\ BN & 0.7654 & 0.7654 & 0.8065 & 0.7872 \\ GT-AHP-EM & 0.7831 & 0.7831 & 0.8172 & 0.7998 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Comparison of five methods of evaluation. consistent with the objective laws of road traffic. When the method is used for traffic trend prediction, the accuracy of the prediction results is improved by 2.7% and 0.7% compared with the AHP and EM methods, respectively. Therefore, the validity and rationality of the model were proven. 3. This study can be continued to dig deeper in terms of data sources, scenario conditions, etc. The experimental data sources are all collected through road sensor devices, and the monitoring data are easily collected and the data quantity is huge. We could consider this feature to carry out other studies in the traffic field for different research targets and scenarios and analyze the road, vehicles and pedestrians under different time periods for multiple roads. Meanwhile, the emergency event selected for the experimental part of this study is an illegal red light running event, which is used as a case to conduct analysis and verification. The experimental results show that this study has some feasibility.