## 5 Experiment  
### Data Analysis  
The data set used for the experiment was obtained from the Nanjing public bicycle company (PBC). It contains data from 20 September to 26 September 2016. We obtained the user trajectories, including origin and destination stations and spatiotemporal information. After deleting the data inconsistent with the time and the blank site, there were 980,036 valid data pieces and 1089 actual stations. Each travel record included the user ID, travel start (end) time, travel start (end) station, station name, station classification, station longitude and latitude, and other information. The bicycle system has 34,729 bicycles (about 35,000), 227,835 users, 41,672 pile positions, and the average loading rate of 0.83. In Figure 4, we show the spatial location of bicycle stations. Stations are divided into four types according to their colors: small margin type (SD), dispatching demand type (DB), dynamic balance type (SM), and key protection type (KG). The operating company determines the division standard according to the frequency and flow difference of vehicle borrowing and returning at the station. As seen in the figure, the collection of different types of stations is overlapping and cannot be completely separated according to geographical location. In the ellipse, the distribution of stations near Zhongshan Road is enlarged. It can be seen from the ellipse that there are different sizes of bubbles. The bubble size indicates the maximum number of bikes stored at the station. This information implies that nodes in the bicycle network have strong heterogeneity. In addition, bicycle stations are mostly distributed along the traffic trunk road, and the bicycle journey trajectory (such as the black dotted line in the ellipse) is closely related to the traffic route. Using the time distance between bicycle stations is more reasonable than the straight distance between stations. Since the cycling speed of bicycles is relatively stable, we use the average cycling time between stations to represent the distance between stations to avoid obtaining a large number of calculations of track length between stations. Next, we look at the temporal characteristics of the bike's borrowing or returning behavior data in Figure 5. Figure 5a,b are the borrowing frequency diagrams of bicycle stations (not shown because the return frequency diagrams are similar). It can be seen from the figure that there are two peaks in the morning (7:00-9:00) and evening (17:00-19:00) on weekdays except Thursday. This phenomenon is called the tide phenomenon. We check the weather conditions on Thursday and find that it rained in the afternoon, reducing people's bicycle travel behavior. On weekends, the tide phenomenon is not obvious. This case shows that people's travel demand is greatly affected by commuting. The direct reason for the BRP is the imbalance between user demand and supply of bicycle stations. The changing trend of the net flow of bicycle stations over time is shown in Figure 5c, and the changing trend of the difference between inflow and outflow is shown in Figure 5d. The peak period in the morning and evening is still the period with the largest fluctuation in the net flow. In addition, the imbalance of working days is much more serious than that of weekends. Figure 5a-d shows the overall performance of all sites, meaning that most nodes have this characteristic performance. Figures 4 and 5 show the spatial and temporal characteristics of bicycle networks, respectively. Processing spatiotemporal data is one of the important features of our model, which is also its original intention. Our model is based on long-term interests, so we can prepare in advance in the face of _tidal phenomena_ to alleviate the imbalance between supply and demand in the bicycle environment. Figure 4: The scatter diagram of bicycle stations in Nanjing. This figure is the distribution map of four types of stations with Xuanwuhu as the center of the map.