## 3 Results and Analysis
### The Trip Level
#### 3.1.2 The Trip Durations
Such insights could be useful for transportation departments and companies involved in managing and optimizing bike-sharing services. In addition, the distribution of trip durations can be explored using commonly used statistical measures such as maximum, minimum, average, and median. Due to space limitations, this paper focuses on the average measure, which is widely used. The average measure was employed to explore trip durations during each hour of each day from two perspectives: origin and destination. The average trip durations for each hour of each day are shown in Figure 10:0 From Figure 15a,b, we can observe that the distribution of average trip durations for the same day, from the perspectives of both origin and destination, exhibited several common temporal patterns. Two typical patterns are that (1) the average trip durations during the morning peaks were shorter than those during the evening peaks, and (2) the average trip durations during \"non-working hours\" were longer than those during \"working hours\". An interesting finding is that, even on the same day, the results for origin and destination may vary significantly. For instance, for 15 August 2016, the distribution of average trip durations for origin differs markedly from that for destination. In addition, the distribution of average trip durations exhibit a distinctive pattern: The average trip durations in the early morning and late evening are generally longer than during other periods. This indicates that the intra-urban human mobility by bikes in the early morning or late evening is typically longer, likely because public transportation systems are not operational during these periods. As a result, bike-sharing systems become  
Figure 10:1 The distribution of trip duration and corresponding trip numbers for each hour: (**a**) origin and (**b**) destination. the primary mode of intra-urban mobility. Conversely, during other periods, when public transportation is available, people have more options besides bike-sharing. This distinctive finding offers useful insights for bike-sharing companies in the context of sustainable intra-urban human mobility and the role of bike-sharing systems in reducing urban congestion and emissions, enabling them to strategically allocate bikes during specific periods to meet demands effectively. The evolutions of average trip duration at all temporal scales for each day can be explored using the CTM. For example, the CTM diagrams of the average trip durations at all temporal scales from 0 to 24 h on 1 August are shown in Figure 10:2 From Figure 16a,b, we can see that the average trip duration at the coarsest temporal scale (i.e., 24 h) is similar for both origin and destination. However, at finer temporal scales, different evolution patterns emerged between origin and destination. Several distinctive patterns include the following: (1) during the period from 3:00 to 5:00, the average trip durations for destination were obviously longer than those of origin; (2) during the period from 6:00 to 8:00, the average trip durations for destination were relatively shorter than those for origin; and (3) during the period from 21:00 to 24:00, the average trip durations for destination were distinctly longer than those for origin. These patterns may be difficult to detect using traditional analysis tools, highlighting the advantages of using the CTM to characterize the temporal patterns of intra-urban human mobility. The CTM allows one to explore and characterize the temporal patterns of any day of interest, offering valuable insights into intra-urban human mobility. Figure 10:3 The CTM diagrams of average trip durations on 1 August: (**a**) origin and (**b**) destination. Figure 10:4 The distribution of average trip duration during each hour for each day: (**a**) origin and (**b**) destination. Similar to the clear exhibition of the days with the largest trip numbers shown in Figure 9, the CTM can also effectively display the days with the longest average trip durations across all time intervals.