## 5 The Model of Traffic Allocation
### Model Description
The traffic flow results obtained by using the flow assignment algorithm are shown in Figure 8. It can be seen that the traffic flow of each link is almost the highest in the time period of 7:10-7:45, followed by 7:45-7:55. In addition, links 9 and 12 are maintained in the low traffic flow range. According to the flow and energy supplement of the four stations, the energy supplement flow of the four nodes in six time periods is summarized and compared, as shown in Figure 9a. From the perspective of the time dimension, the number of energy-replenishing vehicles is the largest in the same period of 7:10-7:45, while there are fewer vehicles in the earliest and noon periods. The comparison trend with the overall driving traffic flow is roughly the same, and the traffic at node 6 is the smallest, and the volume of node 7 is similar to that of node 10, which is determined by the vehicle flow of energy supplement demand. At present, the market share of fuel vehicles still accounts for the absolute counterpart. The same is true for the charging station. Node 11 has a higher flow than node 6. Node 6 is located on the expressway and in the Yangjiaping business district; accordingly, the flow is greater. The reason why node 7 has more traffic than node 10 is determined by the link level and the distance from the end point [25]. In addition, with the gradual increase of electric vehicle market penetration, the proportion of the traffic flow is higher. \begin{table}
\begin{tabular}{c c c c c} \hline
**Periods of Time** & **1\(\rightarrow\)2** & **1\(\rightarrow\)3** & **4\(\rightarrow\)2** & **4\(\rightarrow\)3** \\ \hline
6:00–7:10 & 1800 & 4400 & 2500 & 800 \\
7:10–7:45 & 1100 & 5900 & 2100 & 600 \\
7:45–7:55 & 350 & 1200 & 500 & 150 \\
7:55–8:20 & 1100 & 2000 & 1000 & 500 \\
8:20–9:15 & 1700 & 2300 & 2600 & 1200 \\
9:15–12:00 & 4100 & 4600 & 6600 & 1900 \\ \hline \end{tabular}
\end{table} 
Table 6: Total demand in the corresponding time period. \begin{table}
\begin{tabular}{c c c c} \hline
**Link** & **Length (m)** & **Link** & **Length (m)** \\ \hline
1 & 362 & 11 & 1106 \\
2 & 778 & 12 & 1811 \\
3 & 548 & 13 & 675 \\
4 & 801 & 14 & 1923 \\
5 & 1612 & 15 & 1517 \\
6 & 1109 & 16 & 1825 \\
7 & 1301 & 17 & 385 \\
8 & 1210 & 18 & 408 \\
9 & 2605 & 19 & 388 \\
1 of charging demand and energy-replenishing vehicles in the subsequent network will gradually increase. Therefore, the station traffic with different energy-replenishing vehicle types and interest rates is compared. As shown in Figure 9b-d, the traffic to the charging station node is basically the same when the market share is 30%. When the market share is 50%, the traffic flow at the charging station node begins to exceed that at the gas filling station node and completely exceeds that at 80%, even greater than the current gap between the two types of traffic flow. Figure 8: Results of balanced distribution of traffic flow in different time periods of each path link. Figure 9: Equalization distribution results of energy replenished vehicle flow in each time period. **(a)** 10% of the electric vehicle market. **(b)** 30% of the electric vehicle market. **(c)** 50% of the electric vehicle market. **(d)** 80% of the electric vehicle market. After obtaining the flow of energy replenishing vehicles, the negative effect cost of each energy replenishing station and the corresponding path can be calculated, as shown in Figure 10a. The cost of the negative effect becomes higher in the two time periods of 7:45-7:55 and 7:55-8:20. The total negative effect cost in the total measurement time is 16,821.52 V, including 4,464.75 V for station 67,471.88 V for station 77,659.88 V for station 10 and 5,408.92 V for station 11. As can be seen from Figure 10, with the increase in electric vehicle market share, the overall negative effect level is also gradually decreasing.