Research Article
Data-Driven Approach for Passenger Mobility Pattern Recognition Using Spatiotemporal Embedding
Table 2
Characteristics of mobility patterns.
| ID | Proportion (%) | Spatial features | Temporal features | Possible activity | The origin station | The destination station | The start time | The day of week | Travel time |
| C1 | 13.716 | Mainly residential POIs | Mainly entertainment, working, hospital, and shopping POIs | Mainly 7–8 | Mainly weekdays | Mainly 40 min–80 min | Working (long distance) |
| C2 | 19.817 | Mainly entertainment, working, shopping, and education POIs | Mainly residential POIs | After 17 | Weekdays and Sundays | Within 40 min | Home (short distance) |
| C3 | 13.908 | Mainly residential, entertainment POIs | Mainly entertainment and shopping POIs | 9–19 | Mainly weekends | Within 60 min | Entertainment and shopping |
| C4 | 14.506 | Mainly entertainment, working, and shopping POIs | Mainly residential POIs | Mainly 17–19 | Weekdays and Sundays | Mainly 40 min– 80 min | Home (long distance) |
| C5 | 22.092 | Mainly residential POIs | Mainly entertainment, working, shopping, and POIs | Mainly 7–9 | Mainly weekdays | Mainly within 40 min | Working (short distance) |
| C6 | 15.961 | Mainly entertainment, shopping, and hospital POIs | Mainly entertainment, shopping, and residential POIs | Mainly 11–17 | Weekdays | Mainly within 40 min | Others |
|
|