## 3 Experiments
### Dynamic Route Planning Experiments by SPA*  
Dynamic route planning experiments are rolling route planning from start to finish for multiple time periods or multiple days of data at a given forecast size. In general, the planning algorithm first performs a static planning from the starting point to the end point. Then it determines the starting point for the next time period based on the distance that can be traveled each day, and then performs another static planning for reaching the end point. And so on, until the above static planning is done once for the last time period. Combined with the above-mentioned parameter experiments, the number of segments \(m\) in SPA* can be taken as the corresponding value to the total forecast data size. When \(m\) takes the corresponding value, SPA* spreads the whole planning task to the corresponding time slots for simultaneous computation, i.e., parallel computation. For example, when the total forecast data size is 7 days or 28 time periods, \(m\) can be taken as 7 or 28. The start point and end point are shown in Figure 7. SPA * determines the daily driving distance according to the number of segments, and the remaining algorithms sail 10 grids per day.  
The results of the dynamic experiments are shown in Table 3. SPA* takes only about 9 s to complete a dynamic experiment when the scale of the pre-reported data is Daily. The fastest algorithm is SSA, which takes about 45 s to complete a dynamic experiment, and the slowest is Dijkstra, which takes about 900 s to complete a dynamic experiment. The time required for SPA* is even less, about 2 s, when the scale of the forecast data is \"Every 6 h\". However, at this time, the risk control capability of SPA* decreases a bit compared to the \"Daily\" case. This is in line with the results of the parametric experiments. The fastest of the other algorithms, SSA, takes 321 s to complete a planning, which is nearly 5.5 min. The R-VIMO of both SSA and BA* is greater than 0. This indicates that the planning capability of these algorithms is not sufficient to meet the corresponding constraints with the current data volume.  
Combining Figure 7, it is observed that the routes obtained by SPA* are relatively well avoiding the high-risk areas marked in red. First of all, we will examine the case where the scale of the forecast data scale is \"Daily\". When running to the first day, as shown in Figure 7b, SPA* crosses the striped risk area from the lower risk area. The SSA crosses the striped risk area closer to the middle and high-risk areas; when the run reaches the seventh day, as shown in Figure 7c, SPA* bypasses a low-risk gap at the lower left of the circular risk area to reach the end point. While all other algorithms crossed the circular risk area. The next observation is the forecast data size of \"Every 6 h\". When running to the 3 rd time period, as in Figure 7c, SPA* continues to maintain its performance. When running to the 26 th time period, SPA* also chooses to cross the circular risk zone, but at a different location than the other algorithms. Combined with the values of \(f_{1}\) and \(f_{2}\) of SPA* in Table 3, we can affirm that the planning carried out by SPA* has a better ability to control risk compared with other algorithms.  
Figure 7: Dynamic planning routes of SPA*, traditional path planning and SI-based path planning under different data scales. (**a**,**b**) are the routes planned by the algorithms on day 7 and day 1, respectively, with the forecast data scale of “Daily”; (**c**,**d**) are the routes planned by the algorithms in period 3 and period 26, respectively, with the forecast data scale of “Every 6 h”.  
Experiments show that the time of SPA* is reduced by about 4.8-1262.9 times compared with each algorithm, and the R-VIMO is always 0. At the same time, in the control of \(f_{1}\) and \(f_{2}\), the performance of SPA* is improved by 3.87%-9.47% and 7.21%-10.36%, respectively, compared with other algorithms. All in all, SPA * shows excellent and fast risk planning ability in dynamic route path planning.