MTR Ridership (Passenger Volumes)
Source
Section titled “Source”MTR Corporation
- Open Data Portal: https://opendata.mtr.com.hk/
- Monthly Statistics: https://www.mtr.com.hk/en/customer/main/index.html (Corporate → Investor Relations → Traffic Statistics)
- CKAN Search:
https://data.gov.hk/en-data/api/3/action/package_search?q=MTR+passenger+journeys
Format
Section titled “Format”CSV / XLSX Monthly aggregate. Published ~4 weeks after month end.
~5.5M
Daily Journeys (2023)
Monthly
Update Frequency
Per Station
Granularity
Schema / Fields
Section titled “Schema / Fields”| Field | Type | Example | Description |
|---|---|---|---|
YEAR | integer | 2023 | Year |
MONTH | integer | 10 | Month |
LINE | string | ISL | Line code |
STATION_CODE | string | SHW | Station code |
STATION_NAME | string | Sheung Wan | Station name |
ENTRIES | integer | 48230 | Daily average passenger entries |
EXITS | integer | 47890 | Daily average passenger exits |
TOTAL_JOURNEYS | integer | 96120 | Total daily station throughput |
Fields based on MTR monthly traffic statistics format. Exact column names may vary by file vintage.
Example API Call
Section titled “Example API Call”# CKAN search for MTR ridership datacurl "https://data.gov.hk/en-data/api/3/action/package_search?q=MTR+passenger+journeys+station"
# MTR open data portal (check for CSV downloads)curl "https://opendata.mtr.com.hk/" -IExample Response
Section titled “Example Response”YEAR | MONTH | STATION | ENTRIES | EXITS | TOTAL2023 | 10 | Sheung Wan | 48,230 | 47,890 | 96,1202023 | 10 | Central | 142,800 | 141,200 | 284,0002023 | 10 | Sai Ying P | 31,400 | 30,890 | 62,2902023 | 10 | Admiralty | 89,600 | 88,100 | 177,700Used By
Section titled “Used By”| Model | How |
|---|---|
| Gravity Model | Oi origin mass: station ridership as proxy for zone-level foot traffic potential |
| Regression Model | Accessibility variable — ridership at nearest station as site quality indicator |
Notes / Gotchas
Section titled “Notes / Gotchas”- MTR does not publish per-hour station data publicly — use TransUnifier or academic datasets for temporal patterns
- Ridership recovered to ~90% of pre-COVID levels by 2023 — use 2018/2019 as baseline for “normal” demand
- Station ridership ≠ catchment population: many residents use other transport modes