## 3 Case Application and Analysis
The nodes with medium betweenness value (the standard value of the betweenness is greater than and equals 0.19, but less than 0.49) were distributed around the \"L-type\" shape of high-betweenness nodes, accounting for 35% of all the nodes. This means that the spatial distribution of road network nodes with high betweenness value presents \"L-type\" shape characteristics. The nodes with low betweenness (the standard value of the betweenness is less than 0.19) were mostly distributed on the road network periphery, accounting for 56% of all the nodes. It can be seen that the betweenness of nodes shows a certain structural characteristic of the \"hierarchical circle layer\" in the spatial distribution. If the key nodes are identified according to the node intermediate number, most of the key nodes are located on the trunk road in the central area of the road network. Figure 5: Spatial distribution of node degree. For the traffic flow characteristics, the four-grade running state divided on the Baidu map was used to assign weights to represent the traffic load of road segments. The traffic load value of the node was calculated according to Equation (3). The traffic flow during the rush hour in the five days during weekdays was considered. Because this period of time is the most congested, this can best reflect the actual situation of the traffic load of the road network. To accurately reflect the characteristics of the traffic flow, the traffic operation state of the Baidu map during the traffic peak period from 7: 00 to 9: 00 in 5 days was collected in this paper, and the average traffic load of the nodes in the road network was calculated. The scatter plot of the calculated data after standardized processing was shown in Figure 1:0 All the nodes were divided into three grades according to the classification of natural breakpoints, and the spatial distribution of the average traffic load was shown in Figure 1:1 The nodes with high traffic load (the standard value of average traffic load is greater than OR equals 0.56) were mainly distributed in expressways and individual trunk roads, accounting for 12% of all the nodes. The nodes with low traffic load (the standard value of average traffic load is less than 0.33) were generally distributed on the northwest-southeast link of the road network, accounting for 31% of all the nodes. The  
Figure 1:2 Normalized values of node betweenness. Figure 1:3 Spatial distribution of node betweenness. nodes with middle traffic load (the standard value of average traffic load is greater than OR equals 0.33, but less than 0.56) were distributed in the northeast and southwest region, accounting for 58% of all the nodes. It can be seen that during the morning rush hour, most of the nodes carrying large traffic volume in the whole road network were the intersections between expressways on the outer edge of the road network and the trunk roads in a certain range on both sides of the northwest-southeast line. The above analysis shows that the results identified according to the three indexes respectively are inconsistent, and they can even turn out to be completely different. It can  
Figure 1:4 Normalized values of traffic loads of nodes. Figure 1:5 Spatial distribution of traffic loads of nodes. be seen that it is unreasonable and limited to use a single index to identify the key nodes of the road network. In addition, SPSS software was used to analyze the correlation about the three indexes, namely weighting connection degree (\(x_{1}\)), betweenness (\(x_{2}\)) and average traffic load (\(x_{3}\)), and the correlation coefficient \(R\) and significant parameter \(P\) were obtained as follows respectively: \(R_{12}=0.066\), \(P=0.490\); \(R_{13}=0.725\), \(P=0.000\); \(R_{23}=0.1490\), \(P=0.115\). It can be seen that there was a high correlation between weighted connection degree and the average traffic load of nodes.