## 4 Experimental Results
### Correlation Analysis of Indicators  
#### 4.2.1 Parameter Estimation of the Poisson Regression Model  
In this paper, the simulation analysis of the Poisson regression model is carried out for urban road traffic situation prediction indicators, and the results of the Poisson regression model parameter estimation are shown in Table 6.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **Time** & **Traffic Flow (pcu/15 min)** \\ \hline
1 & 00:00 & 90 \\
2 & 00:15 & 60 \\
3 & 00:30 & 114 \\
4 & 00:45 & 120 \\
5 & 01:00 & 180 \\ \(\dots\) & \(\dots\) & \(\dots\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Selected data from the traffic situational awareness dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Number** & **Load** & **Load** & **Load** & **Traffic** & **Traffic Flow** (pcu/15 min)** & **Traffic Flow** (pcu/15 min)** & **Traffic Flow** (pcu/15 min)** & **Traffic Flow** (pcu/15 min)** & **Traffic Flow** (pcu/15 min)** & **Traffic Flow** (pcu/15 min)** \\ \hline
1 & 0.981 & 15 & 2 & 7 & 6 & 52 & 0 & 1.1 & 8 & 400 \\
2 & 1.141 & 14 & 5 & 2 & 4 & 48 & 1 & 0.5 & 8 & 600 \\
3 & 0.766 & 15 & 2 & 2 & 2 & 72 & 0 & 0.5 & 11 & 600 \\
4 & 1.236 & 10 & 3 & 11 & 2 & 98 & 1 & 0.3 & 9 & 750 \\
5 & 1.374 & 7 & 2 & 3 & 0 & 26 & 3 & 0.8 & 8 & 400 \\
6 & 2.279 & 9 & 3 & 14 & 3 & 46 & 2 & 0.6 & 8 & 600 \\ \(\dots\) & \(\dots\) & \(\dots\) & \(\dots\) & \(\dots\) & \(\dots\) & \(\dots\) & \(\dots\) & \(\dots\) & \(\dots\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Selected data from the traffic trend prediction dataset.  
It can be seen through the results that:  
(1) The parameter estimation results of the Poisson regression model show that six indicators--road length, intersection, rainfall intensity, visibility, traffic flow and road construction--have significant effects on urban road emergencies. Among them, there are two indicators of road factors: road length (satisfying the confidence level of 0.01 significance level) and intersection (satisfying the confidence level of 0.01 significance level); there are two indicators of environmental factors: rainfall intensity (satisfying the confidence level of 0.05 significance level) and visibility (satisfying the confidence level of 0.05 significance level); there are two indicators of human factors: traffic flow (satisfying the confidence level of 0.05 significance level) and road construction (satisfying a confidence level of 0.01 significance level).  
(2) The regression coefficients of each influencing factor in the Poisson regression model are consistent with the positive and negative signs, so it can be inferred that when keeping the value of the remaining influencing factors constant, only considering the road length, one-way road width, intersections and rainfall, and one of the four influencing factors change, the likelihood of urban road emergencies will show the trend of increasing with the value of the influencing factors; when keeping the value of the remaining influencing factors constant, only considering traffic lights, temperature, visibility, law enforcement officers, traffic flow and road sections under construction, and one of the six influencing factors change, the likelihood of urban road emergencies will show the trend of increasing with the value of the influencing factors; when keeping the value of the remaining influencing factors constant, only considering traffic lights, temperature, visibility, law enforcement officers, traffic flow, and road sections under construction, and one of the six influencing factors change, the possibility of urban road emergencies will show a trend of decreasing with the increase in the value of influencing factors.  
Thus, it can be determined that the four positive indicators are road length, one-way road width, the intersections and rainfall; the six negative indicators are traffic lights, temperature, visibility, law enforcement officers, traffic flow and road sections under construction.