## 4 Empirical Research
### Chongqing's Transportation Infrastructure Planning Based on the Coordinate Planning Method
#### 4.3.2 Compilation of the Computation Program  
The program is written in Python 3.11 and Mysql 8.0, and the libraries include OSMN, Network X, Chap-gpt4o API, and Mysql. The program implements the computational process introduced in Section 3 of this paper. Firstly, the basic transportation network model is constructed using Formula (1), and the travel agents with different identity attributes are generated using Chat-GPT. Secondly, the starting and ending points of the agent are set with the help of Chat-GPT based on the data for Group V and Group E. And based on the data of the PoF group and the ToF group, combined with Formulas (11) and (12), the means of transportation is selected for the agent. Then, the shortest path is calculated through Formulas (2)-(4), the travel time and the information about the path nodes are recorded, and the results are stored in the database. Finally, whether the TIG matrix has been completely enumerated is checked (Formulas (7)-(10)). If completed, the path record is read from the database. The key evaluation indicators, such as network efficiency and time stability, are calculated by using Formulas (5) and (6). The detailed design concept is shown in Figure 4. \begin{table}
\begin{tabular}{c c c c c c} \hline  & **Highway** & **Expressway** & **Railway** & **High-Speed** & **Subway** \\ \hline Comfort (1 h \(<\) Time \(<\) 5 h) & Neutral & Neutral & Comfortable & Comfortable & Unbearable \\ Comfort (Time \(>\) 5 h) & Unbearable & Uncomfortable & Neutral & Neutral & N/A \\ Safety (Time \(<\) 1 h) & Safe & Safe & Very Safe & Very Safe & Very Safe \\ Safety (1 h \(<\) Time \(<\) 5 h) & Dangerous & Risky & Very Safe & Very Safe & Very Safe \\ Safety (Time \(>\) 5 h) & Very Dangerous & Dangerous & Very Safe & Very Safe & N/A \\ Price (Yuan/km) & 0.77 & 1.15 & 0.158 & 0.555 & 0.3 \\ \hline \end{tabular} All the above data are required for evaluation.  
\end{table} 
Table 2: _Cont._  
Figure 3: The initial network model. The greater the number of simulations, the higher the confidence of the results. Meanwhile, the greater the number of simulations, the longer the time to obtain the results of the operation. Determining a number of simulations that can reflect the reality and minimize the computation time will improve the efficiency of the study. In this study, 15 candidate values ranging from 1 \(\times\) 10\({}^{7}\)-9 \(\times\) 10\({}^{9}\) were selected to represent the number of simulation rounds. Utilizing the transportation infrastructure network model of Chongqing, the big data simulation program generated 40 sets of average travel time data for each candidate value. For these datasets, the 99.5% confidence intervals (CIs) were calculated, along with the range (difference between upper and lower limits) of each CI. The results are summarized in Table 3. When the number of simulations exceeded 7 \(\times\) 10\({}^{8}\), the 99.5% CI narrowed to less than 1, indicating a 99.5% probability that the true average travel time falls within the range of 159.9-169.9 min under these conditions. Figure 4: Program design ideas. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multirow{2}{*}{**Time(s)**} & **99.5\% Lower Limit** & **99.5\% Upper Limit** & **Difference Between Upper** \\  & & **(min)** & **(min)** & **and Lower Limits** \\ \hline
1 \(\times\) 10\({}^{7}\) & 9.7 & 167.62 & 172.88 & 5.26 \\
3 \(\times\) 10\({}^{7}\) & 18.89 & 168.86 & 172.94 & 4.08 \\
5 \(\times\) 10\({}^{7}\) & 28.06 & 169.31 & 172.59 & 3.28 \\
7 \(\times\) 10\({}^{7}\) & 37.12 & 169.73 & 171.82 & 2.09 \\
9 \(\times\) 10\({}^{7}\) & 46.37 & 168.99 & 171.16 & 2.17 \\
1 \(\times\) 10\({}^{8}\) & 94.39 & 169.46 & 171.24 & 1.78 \\
3 \(\times\) 10\({}^{8}\) & 195.02 & 169.33 & 171.07 & 1.74 \\
5 \(\times\) 10\({}^{8}\) & 281.61 & 169.84 & 170.71 & 1.65 \\
7 \(\times\) 10\({}^{8}\) & 382.73 & 169.9 & 170.9 & 1 \\
9 \(\times\) 10\({}^{8}\) & 477.27 & 170.14 & 170.96 & 0.82 \\
1 \(\times\) 10\({}^{9}\) & 932.1 & 169.82 & 170.53 & 0.71 \\ \hline \hline \end{tabular}
\end{table}