## 6 Instance Validation
### User Choice Behavior Fitting  
Based on the utility function proposed in Section 4.1, a questionnaire survey is conducted to investigate the users' travel choice behavior according to the variable parameters contained in it. A total of 500 questionnaires were distributed in this experiment, and the respondent population was randomly selected from the Xi'an Road business district in Dalian, Liaoning Province, China. The questionnaire included the respondents' gender, age, income, purpose of travel, distance from the business district, and perceived comfort and reliability of the various modes of travel, as well as the travel choices made in the corresponding scenarios. According to the survey results, a total of 41.8% of the respondents' travel intentions were concentrated between 17:00 and 22:00 h, which was used as the peak parking lot period, with a subsequent formulation of prices for this peak period. The results of the questionnaire were analyzed to develop a nested logit model, and the parameters of the variables in the model were fitted and calibrated based on the results of 500 user business district travel surveys. The parameters for each option are listed in Table 3.  
Each of the above four attributes is a personal user attribute, an attribute that does not change with each selected branch. It can be seen that among the four user personal attributes, age and income attributes have a higher degree of influence on travel mode choice. Table 4 shows the parameters corresponding to each attribute for each travel mode. In the model, the variables' dependability, convenience, and travel time are varied with each mode of transportation, i.e., the utility attributes of each mode of transportation for the user. From the above table, it can be seen that dependability, as well as convenience, are positively correlated with the probability of choice in reserved parking, traditional parking, as well as other modes of travel, with a positive impact. And in all three ways, travel time is negatively correlated with the selection result. In the fitting results, the most influential weights are the dependability and convenience factors for choosing other modes of travel and the travel time factor for choosing parking reservations, respectively, which are in line with the actual logic. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variant** & **Coefficient** & **Standard Deviation** & \(p\)**-Value Test** \\ \hline age & 0.149 & 0.169 & 0.038 \\ income & 0.121 & 0.166 & 0.047 \\ purpose & 0.021 & 0.130 & 0.047 \\ distance & 0.085 & 0.147 & 0.036 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Personal attribute parameters. Table 5 shows the cost, preferences factor weights and the constant term for each choice branch. Among them, cost and preferences variables that vary with each choice branch are choice branch attributes. In the above table, the weights of the cost factors for each travel mode and reservation option are negative, i.e., they are negatively correlated with the probability of choice. In contrast, the weights of the preferences factors, which represent user habits, are all positive and positively correlated with the probability of selection. The cost has a slightly smaller impact weighting overall, with the largest impact being the travel option that selects a reservation time slot of 20:00-21:00. The weighting of the preferences varies considerably, with the most influential option being \"Choose to book a parking slot between 17:00 and 22:00\". The dissimilarity parameter for each choice branch in the above fitted appointment time scenario is 0.593. Based on the fitting results for this parking lot, the utility function for each travel mode is as follows.