## 3 Results and Analysis
### The Trip Level  
3.1.1. The Trip Numbers  
The distribution of trip numbers on daily and weekly scales is visualized in Figure 4. In this figure, the horizontal axis represents individual days, while the vertical axis indicates the number of trips on those days. Additionally, the four weeks are marked with different-colored polylines: red for the first week, green for the second week, blue for the third week, and black for the fourth week. Moreover, the figure includes statistical confidence information, with the mean value indicated by a red dotted line and the 95% confidence interval represented by green dotted lines. Note that weekdays and weekends are highlighted using red and yellow rectangles, respectively. According to Figure 4, we can observe that, generally, the number of trips increases as the weeks progress. This interesting finding suggests that the number of trips may not exhibit strong periodic patterns throughout a month. Specifically, the number of trips in the latter weeks of a month might be larger than that in the initial weeks. For each week, the number of trips on weekends in three of the four weeks (i.e., week 1, week 2, and week 4) is larger than that on weekdays. Among the weekdays, Friday had the largest number of trips in three of the four weeks (i.e., week 2, week 3, and week 4). Additionally, in two of the four weeks (i.e., week 1 and week 2), Wednesday had the largest number of trips. This demonstrates that Wednesday and Friday are peak days for bike-sharing demand on weekdays, while weekends generally have higher demand than weekdays. Therefore, relevant departments can implement more effective measures to manage the high bike-sharing volumes on these specific days, ensuring smooth operation of bike-sharing traffic in intra-urban regions. To further explore the distribution of trip numbers, we adopted the four commonly used statistical measures: maximum, average, median, and minimum. The distribution of trip numbers under these measures is shown in Figure 5. From Figure 5, we can observe that several patterns emerge. (1) Maximum measure: the maximum number of trips on Friday was the largest among weekdays, while Monday had the smallest maximum trip numbers. Additionally, maximum trip numbers on weekends were generally larger than those on weekdays. (2) Average measure: Friday had the largest average trip numbers on weekdays, followed by Wednesday and Thursday. Monday and Tuesday had the smallest average trip numbers. Average trip numbers on weekends were relatively larger than those on weekdays. (3) Median measure: Friday and Wednesday were the two days with the largest median trip numbers on weekdays, while weekends had generally smaller median trip numbers compared to weekdays. (4) Minimum measure: Wednesday had the largest minimum trip numbers. The minimum trip numbers on weekends were larger than those on weekdays, likely due to the overall larger trip numbers on these days. Overall, the findings based on the four statistical measures align well with the observations in Figure 4.  
Figure 4: The distribution of trip numbers on the daily and weekly scales. The distribution of hourly trip numbers is explored from two perspectives: the origin and the destination. This analysis is important because trips often span two neighboring hours. A trip is considered an origin if it starts within the corresponding hour and a destination if it ends within the corresponding hour. For example, if a trip starts from 8:05 and ends at 8:10, it is considered both an origin and a destination within the hour 8-9. If a trip starts from 8:55 and ends at 9:10, it is considered an origin within the hour 8-9 and a destination within the hour 9-10. Therefore, analyzing trips from both the origin and the destination perspectives provides a more objective and accurate representation of the actual situation. This approach helps us to characterize more reliable temporal patterns of intra-urban human mobilities.