## 5 Conclusions  
A well-organized and well-operated transportation system plays an important role in supporting communication and motivating the sustainable socioeconomic development of an urban agglomeration [71]. Intercity travel is an integral part of the entire urban agglomeration transportation system [72]. Analyzing the personalities of intercity travelers and the spatial-temporal characteristics of intercity travel behavior in urban agglomerations can provide support for the planning and management of transportation systems in urban agglomerations. In this study, a combined method of variable selection, determination of clustering number, and improved K-prototypes was applied to analyze the personalities of intercity travelers and the spatial-temporal characteristics of intercity travel behavior. Firstly, to deal with unordered categorical and numerical variables simultaneously, a combined method based on Cramer's V and the Spearman correlation coefficient was applied to eliminate highly correlated variables while retaining those that significantly impacted the clustering results. This study conducted a variable selection process on 20 variables containing 161,797 trip chains. Based on the calculated correlation coefficient results, 12 variables that had a significant impact on the travel characteristics of intercity behavior were selected, and 8 variables were filtered out. Secondly, the silhouette coefficient method was applied to determine the optimal number of clusters for each trip chain dataset. A total of 12 trip chain datasets had their optimal number of clusters identified. Thirdly, the K-prototypes algorithm was applied to cluster trip chain datasets, which contained both numerical and categorical data. Finally, a detailed comparative analysis of trip chains from temporal and spatial perspectives was conducted to identify the personality and behavior of intercity travelers. We found that the majority of intercity travelers were male, with an age range from 19 to 35, in the context of different travel directions across various transportation corridors in 2019 and 2021. People were most likely to travel between two cities on weekdays. The average travel distance within the city increased with the increase in \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Attributes** & **Beijing–** & **Tianjin–** & **Beijing–** & **Shijiazhuang–** & **Shijiazhuang–** & **Shijiazhuang–** \\  & **Tianjin** & **Beijing** & **Shijiazhuang** & **Beijing** & **Shijiazhuang** & **Tianjin** \\ \hline Weekday & 3 & 3 & 3 & 3 & 3 & 3 \\ Gender & 1 & 1 & 1 & 1 & 1 & 1 \\ Type-O & 1 & 1 & 1 & 1 & 1 & 1 \\ Mode-O & 4 & 1 & 4 & 1 & 1 & 1 \\ Mode-D & 1 & 1 & 1 & 1 & 1 & 1 \\ Type-D & 1 & 1 & 1 & 1 & 1 & 1 \\ Age & 5 & 7 & 5 & 5 & 5 & 5 \\ MOT-O & 7 & 12 & 8 & 7 & 7 & 11 \\ Ds-O & 2 & 1 & 2 & 1 & 1 & 1 \\ Wt-O & 7 & 6 & 9 & 6 & 7 & 11 \\ Wt-D & 6 & 6 & 5 & 7 & 8 & 6 \\ Ds-D & 1 & 2 & 1 & 2 & 1 & 1 \\ Percentage & 54.99\% & 49.50\% & 41.39\% & 58.76\% & 60.98\% & 57.14\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Characteristics of the trip chain with the largest proportion of different travel directions in 2021.  
subway mileage. Compared with 2019, travelers spent more time at the departure hub in 2021. Overall, this study's primary contribution is to propose an excellent applicable method for analyzing the characteristics and behaviors of intercity travelers within urban agglomerations from the perspective of an entire trip chain based on smartphone data. The research results can provide a reference for the government to optimize the transportation system and for enterprises to promote the level of transportation management. There are also some limitations in this study. Firstly, although the complete intercity trip chain for urban agglomerations includes personal attributes, travel time, travel mode, travel distance, and other information of travelers, the classification of travel modes was not precise enough, and only smartphone data was used.