## 3 Materials and Methods
### Statistical Description of the Raw Data  
The data set comes from DiDi Chuxing Company (a ride-souring service company), Shenzhen branch. In terms of online car-hailing market share, DiDi Chuxing is the world's largest one-stop diversified travel platform, with nearly 300 million users in more than 400 cities in China at the end of 2017. According to various data, DiDi has more than 87% of China's private car market and more than 99% of the online taxi market. Therefore, to study the problem of ride-sourcing travel mode from a macro perspective of the city, the data of the DiDi travel platform used in this research is highly representative. This research has obtained a total of 253,370 DiDi Travel data records in Shenzhen during the five working days (Monday-Friday) from 4 July 2016 to 8 July 2016. The DiDi Travel platform has defined travel time as three peak types, where \"1\" represents the morning peak and is defined as 7:00-10:00, \"3\" represents the evening peak and is defined as 17:00-20:00, and \"2\" is defined as 10:00-17:00. This research focuses on commuting travel of urban villages, so it extracts travel records with origin type as \"urban villages\" and arrival type as \"commercial building\" in morning peak hours, and travel records with departure type as \"commercial building\" and arrival area as \"urban villages\" in evening peak hours. After data cleaning to eliminate the abnormal latitude and longitude of the departure and arrival locations, the effective record of morning peak commute trips was 23,622, and the effective record of evening peak commute trips was 13,083. The bellowing Table 1 shows an example of DiDi travel record. Since departure and arrival types have been limited to \"urban village\" and \"commercial building\" during the data preprocessing stage, DiDi travel during the commuting period can reflect the occurrence and attraction of commuting trips and the distribution of employment and residence; the departure during the morning peak (morning origin, MO) and evening peak arrival (evening destination, ED) locations can reflect the distribution of residences (urban villages); and morning peak arrival (morning destination, MD) and evening peak departure (evening origin, EO) locations can reflect the location of the workplace. The distribution of DiDi commuting trips occurring across urban villages during the morning and evening peak hours on the scale of 490 TAZs in Shenzhen is shown in Table 2 and Figures 2-5. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Data** & **ID** & **Origin** & **Origin** & **Destination** & **Destination** & **Destination** & **Departure** & **Peak** & **Origin** & **Destination** & \\  & **Longitude** & **Latitude** & **Longitude** & **Latitude** & **Time** & **Type** & **Type** & **Type** & **Type** & **Type** \\ \hline
1 July 2016 & 2798439385 & 113.9832 & 22.75048 & 114.0681 & 22.62849 & 09:19 & 1 & Urban & Commercial & \\  & 1 July 2016 & 2949340043 & 113.9351 & 22.54093 & 113.9609 & 22.56938 & 19:43 & 3 & Commercial & Urban & \\  & & & & & & & & buildings & village & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of the raw data from DiDi ride-sourcing company. Figure 4: Distribution of DiDi trips during the EO. Figure 3: Distribution of DiDi trips during the MD. Figure 2: Distribution of DiDi trips during the MO. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Travel Frequency** & **Max.** & **Min.** & **Mean** & **S.D.** \\ \hline MO & 1752 & 1 & 285.1 & 264.7 \\ MD & 2836 & 1 & 271.1 & 329.0 \\ EO & 2338 & 3 & 365.9 & 364.5 \\ ED & 2294 & 3 & 365.7 & 365.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Description of DiDi commuting trips. From Figures 2-5, DiDi's travel demand shows a certain degree of agglomeration in space, most of which is concentrated in the central and southwestern central areas, which also reflects the agglomeration and distribution of job-housing in Shenzhen. Figures 2-5 and Table 2 also reflect the uneven distribution of DiDi commuting trips within each community.