## 4 Discussion  
In this case study, which analyzed bike-sharing trips in Shanghai, China, using the methods of exploratory data analysis (EDA) and the continuous triangular model (CTM), we present several interesting results that led to distinctive findings. We believe these findings represent a significant contribution to the existing research on bike-sharing data analysis. This paper primarily focused on the temporal aspects of bike-sharing trip analysis, with particular attention to trip numbers and trip durations, as these are fundamental indicators of temporal patterns. We explored these aspects from various perspectives to conduct in-depth analyses. The main contributions of our research are twofold: First, some of the results align well with previous findings, validating the correctness of our study and reinforcing prior research. This alignment demonstrates the novelty and robustness of our analysis methods, which offer more perspectives and greater depth than previous studies. Second, this paper provides additional results not previously mentioned in the literature, leading to distinctive findings. While these findings may not be broad in scope, they are numerous and meaningful in depth. These detailed insights are valuable supplements to existing research, offering a more comprehensive understanding of the temporal patterns of intra-urban human mobility through bike-sharing systems. Together with previous findings, this research contributes to a more objective picture of urban mobility patterns. In addition to contributions at the trip level, this research also offers specific insights at the bike level. Relatively few studies have focused on the bike level, making our findings in this area important supplements to existing research. Our study achieved particular insights into bikes utilization, providing useful information for bike-sharing companies. Given that previous research has reported fewer related results at this level, our findings are timely additions to existing research and are expected to play an important role in future \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Total Service** & **Cumulative** & **Total Service** & **Cumulative** \\
**Duration** & **Frequency** & **Duration** & **Frequency** \\ \hline
5 & 0.10279 & 65 & 0.95684 \\
10 & 0.35604 & 70 & 0.96355 \\
15 & 0.53616 & 75 & 0.96943 \\
20 & 0.65858 & 80 & 0.97386 \\
25 & 0.74477 & 85 & 0.97743 \\
30 & 0.81089 & 90 & 0.98105 \\
35 & 0.85083 & 95 & 0.98379 \\
40 & 0.87982 & 100 & 0.98612 \\
45 & 0.90183 & 105 & 0.98812 \\
50 & 0.91993 & 110 & 0.98969 \\
55 & 0.93556 & 115 & 0.99100 \\
60 & 0.94888 & 120 & 0.99219 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The detailed information of the total service duration (every 5 min) and its corresponding cumulative frequency for all the bikes. Figure 20: The histogram between the total service durations and the number of occurrences among all the bikes. research specifically targeting bikes. By considering the temporal patterns characterized in this paper, relevant departments can develop more scientific strategies and policies to optimize current bike-sharing systems, thereby providing more convenient services to users. For example, bike-sharing companies could deploy more bikes on days or weeks with larger trip numbers to meet potential demands. We believe the findings in this study can play a crucial role in guiding actions aimed at optimizing bike-sharing systems in the future. The research presented in this paper does have certain limitations. Specifically, the dataset used in the study spans only one month. While the research findings demonstrate the effectiveness of the dataset, this short time frame may constrain the study's ability to fully understand the seasonal impacts of bike-sharing on travel. The primary limitation is the lack of comprehensive seasonal coverage, as a single month reflects only the specific conditions of that period, which may not accurately represent other seasons.