## 3 Results  
### Spatial Pattern of Transportation Network Travel Speed  
The activity concept has been extended to include the speed of travel within transportation networks, thereby overcoming its previous limitations in accounting for the varying speeds of different modes of transportation and road conditions. Emphasis has been placed on integrating travel time as a critical parameter for capturing the circuitousness of transportation networks. The research case focuses on the seven major city clusters in China. The real-time navigation data for automobile and train travel modes are obtained through API, which is provided by Amap. Based on the models and data presented, the travel speed of the transportation network between the intercity nodes within each city cluster is calculated and illustrated in Figure 2.  
As the Euclidean distance between the cities of origin and destination increases, there is an upward trend in the average travel speed of the transportation network. However, the level of data dispersion also intensifies. The statement suggests that long-distance journeys typically involve fewer detours; however, significant variations exist in travel conditions across different regions. From the perspective of various modes of transportation, average of the transportation network travel speed using automobile is faster than those of train, over the same distance. Moreover, the standard deviation in its numerical distribution indicates a higher level of data concentration. This suggests that passengers face relatively minor differences in the transportation network travel speed of automobiles across varied city clusters, when traveling the same distance. Consequently, we conclude that car travel is generally more efficient than train travel. As the Euclidean distance between the origin and destination cities increases, the average of the transportation network travel speed by train increases quickly, while the efficiency gap between train and car travel diminishes. Between 0 km and 200 km, traveling by car demonstrates a clear advantage; however, beyond the 300 km mark, train transportation in certain regions starts to outperform cars. This phenomenon is likely attributable to the availability of relatively abundant high-speed rail services in these city pairs, resulting in significantly faster train travel compared to automobile journeys. To investigate the variations in travel speed within transportation networks concerning the Euclidean distance separating the origin and destination cities across various city clusters, scatterplots are generated for each city cluster (Figure 3). The horizontal axis of the scatter plot denotes the Euclidean distance between OD pairs, while the vertical axis represents the speed of travel within the transportation network. In Figure 3, the speed of travel in the transportation network by automobile exceeds that of the train, for most Euclidean distances between the origin and destination cities within each city cluster. This trend is particularly noticeable for short- and medium-distance journeys. Furthermore, when considering the standard deviation of travel speeds within transportation networks by car, as presented in Table 1, it can be inferred that the efficiency of travel within intercity highway networks across different city clusters is relatively consistent. In the context of train travel, the speed of transportation networks in the Yangtze River Delta and Beijing-Tianjin-Heibei regions exhibit higher values and a lower level of dispersion. This suggests that the majority of cities within these clusters can leverage the advantages of the \"time-space contraction effect\" facilitated using HSR networks. The Pearl River Delta region features a higher number of detours for intercity travel, leading to a slower transportation network travel speed. However, the distribution of travel speeds across the transportation network as a whole, for the same Euclidean distance between OD pairs, exhibits similarities to those observed in the three aforementioned city clusters.