Transportation Demand Forecast Dataset
This dataset provides detailed, time-stamped records of public transportation ridership, including route, vehicle, stop information, passenger counts, and demand forecasts. It supports peak demand analysis, resource planning, and service scheduling, making it invaluable for transportation planners and data scientists seeking to optimize transit operations and predict future needs.
Sample rows
preview · 8 of 200 rows · all 20 columns| record_idstring | vehicle_typestring | passenger_countinteger | event_indicatorboolean | weather_conditionstring | service_datedate | service_start_timedatetime | service_end_timedatetime | route_idstring | route_namestring | vehicle_idstring | origin_stop_idstring | origin_stop_namestring | destination_stop_idstring | destination_stop_namestring | capacityinteger | service_typestring | event_namestring | forecasted_demandinteger | actual_demandinteger |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| RID-0001 | bus | 38 | false | clear | 2024-06-03 | 2024-06-03T07:10:00 | 2024-06-03T08:05:00 | R101 | Central City Loop | B001 | S001 | Main Terminal | S002 | Market Square | 40 | regular | blank | 39 | 38 |
| RID-0002 | train | 145 | false | clear | 2024-06-03 | 2024-06-03T17:25:00 | 2024-06-03T18:05:00 | R102 | Riverside Express | T015 | S003 | River Junction | S004 | East End | 150 | express | blank | 148 | 145 |
| RID-0003 | ferry | 92 | false | rain | 2024-06-04 | 2024-06-04T05:40:00 | 2024-06-04T06:15:00 | R103 | Harbor Shuttle | F020 | S005 | Harbor Pier | S006 | Island Point | 120 | regular | blank | 100 | 92 |
| RID-0004 | bus | 2 | false | fog | 2024-06-04 | 2024-06-04T23:45:00 | 2024-06-05T00:25:00 | R104 | Midnight Express | B002 | S007 | Night Plaza | S008 | Downtown | 35 | night | blank | 4 | 2 |
| RID-0005 | subway | 298 | true | clear | 2024-06-05 | 2024-06-05T18:00:00 | 2024-06-05T19:05:00 | R105 | Metro Line Blue | S021 | S009 | University Center | S010 | Tech Park | 300 | express | Tech Expo Conference | 300 | 298 |
| RID-0006 | bus | 40 | false | rain | 2024-06-05 | 2024-06-05T06:35:00 | 2024-06-05T07:15:00 | R106 | Old Town Circular | B003 | S011 | Old Town | S012 | Central Library | 40 | regular | blank | 40 | 40 |
| RID-0007 | tram | 59 | false | clear | 2024-06-06 | 2024-06-06T20:00:00 | 2024-06-06T20:45:00 | R107 | Tram South Loop | TR14 | S013 | South Station | S014 | City Museum | 60 | regular | blank | 60 | 59 |
| RID-0008 | ferry | 3 | false | storm | 2024-06-06 | 2024-06-06T22:10:00 | 2024-06-06T22:45:00 | R108 | Night Ferry | F021 | S015 | Marina South | S016 | Harbor North | 120 | night | blank | 5 | 3 |
| RID-0009 | train | 121 | false | rain | 2024-06-07 | 2024-06-07T08:25:00 | 2024-06-07T09:10:00 | R109 | Express Eastbound | T016 | S017 | East Station | S018 | West Station | 130 | express | blank | 125 | 121 |
| RID-0010 | tram | 49 | false | clear | 2024-06-07 | 2024-06-07T13:55:00 | 2024-06-07T14:35:00 | R110 | Central Tramway | TR15 | S019 | Central Park | S020 | City Hall | 60 | regular | blank | 50 | 49 |
| RID-0011 | ferry | 0 | false | fog | 2024-06-08 | 2024-06-08T04:50:00 | 2024-06-08T05:25:00 | R111 | Harbor Morning Run | F022 | S021 | Harbor Pier | S022 | Island Point | 110 | night | blank | 2 | 0 |
| RID-0012 | subway | 222 | false | clear | 2024-06-08 | 2024-06-08T10:05:00 | 2024-06-08T10:55:00 | R112 | Metro Line Green | S022 | S023 | Green Park | S024 | Museum District | 300 | regular | blank | 230 | 222 |
| RID-0013 | bus | 36 | false | rain | 2024-06-09 | 2024-06-09T06:20:00 | 2024-06-09T07:05:00 | R113 | North Busway | B004 | S025 | North Station | S026 | Maple Grove | 38 | regular | blank | 37 | 36 |
| RID-0014 | tram | 60 | true | clear | 2024-06-09 | 2024-06-09T18:35:00 | 2024-06-09T19:20:00 | R114 | Sunset Tram | TR16 | S027 | Sunset Station | S028 | City Mall | 60 | regular | Mall Summer Festival | 60 | 60 |
| RID-0015 | train | 101 | false | clear | 2024-06-10 | 2024-06-10T08:45:00 | 2024-06-10T09:25:00 | R115 | Express West | T017 | S029 | West Terminal | S030 | Grand Avenue | 120 | express | blank | 110 | 101 |
| RID-0016 | ferry | 75 | false | clear | 2024-06-10 | 2024-06-10T11:50:00 | 2024-06-10T12:35:00 | R116 | Central Ferry | F023 | S031 | Central Dock | S032 | Bay View | 120 | regular | blank | 80 | 75 |
| RID-0017 | bus | 1 | false | fog | 2024-06-11 | 2024-06-11T22:40:00 | 2024-06-12T00:05:00 | R117 | Nightline North | B005 | S033 | Northern Depot | S034 | Central Terminal | 35 | night | blank | 2 | 1 |
| RID-0018 | tram | 53 | false | rain | 2024-06-11 | 2024-06-11T13:20:00 | 2024-06-11T14:00:00 | R118 | Heritage Tramway | TR17 | S035 | Heritage Stop | S036 | Museum Row | 60 | regular | blank | 55 | 53 |
| RID-0019 | bus | 35 | false | clear | 2024-06-12 | 2024-06-12T07:00:00 | 2024-06-12T07:45:00 | R119 | Morning Bus | B006 | S037 | Sunrise Station | S038 | Central Office | 38 | regular | blank | 36 | 35 |
| RID-0020 | train | 130 | false | rain | 2024-06-12 | 2024-06-12T18:15:00 | 2024-06-12T19:10:00 | R120 | Downtown Express | T018 | S039 | Downtown Station | S040 | Suburbia | 150 | express | blank | 140 | 130 |
What the 200 rows show
from the 200-row sampleSubway (vehicle type) stands out: mean passenger_
- 15%event_
indicator = true - 40median passenger_
count - 4service types
- 60median capacity
- 41median forecasted_
demand - 40median actual_
demand
Median 40, from 0 to 301.
- string 12
- integer 4
- date 1
- datetime 2
- boolean 1
Columns
20 columns in four groups| column | type | description | example |
|---|---|---|---|
| Text 12 columns | |||
record_id | string | Unique identifier for each ridership recordunique | RID-0001 |
route_id | string | Unique identifier for the transportation route | R101 |
route_name | string | Name or description of the routeoptional | Central City Loop |
vehicle_id | string | Unique identifier for the vehicle usedoptional | B001 |
vehicle_type | string | Type of vehicle (e.g., bus, train, tram)6 values · optional | bus |
origin_stop_id | string | Unique identifier for the origin stop/station | S001 |
origin_stop_name | string | Name of the origin stop/stationoptional | Main Terminal |
destination_stop_id | string | Unique identifier for the destination stop/station | S002 |
destination_stop_name | string | Name of the destination stop/stationoptional | Market Square |
service_type | string | Type of service (e.g., regular, express, night)regular · express · night · special · optional | regular |
weather_condition | string | Weather conditions during the service (e.g., clear, rain, snow)6 values · optional | clear |
event_name | string | Name of the event if applicableoptional | Tech Expo Conference |
| Numbers 4 columns | |||
passenger_count | integer | Number of passengers for this record0 or more | 38 |
capacity | integer | Maximum passenger capacity of the vehicle1 or more · optional | 40 |
forecasted_demand | integer | Predicted demand for this service instance0 or more · optional | 39 |
actual_demand | integer | Actual observed demand for this service instance0 or more · optional | 38 |
| Dates and times 3 columns | |||
service_date | date | Date of the transportation service | 2024-06-03 |
service_start_time | datetime | Start time of the transportation service | 2024-06-03T07:10:00 |
service_end_time | datetime | End time of the transportation serviceoptional | 2024-06-03T08:05:00 |
| True or false 1 column | |||
event_indicator | boolean | Indicates if a special event affected ridership (true/false)optional | false |
Use it for
A transportation dashboard
The event_
indicator rate, passenger_ count by vehicle_ type and a breakdown of weather_ condition. Excel, Power BI or Tableau. Why do the 25 subway rows have a mean passenger_
count of 282.9? A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
- Records200RID-000138busRID-0005298subwayRID-001460tram
A software demo
Believable records with service_
date, service_ start_ time and service_ end_ time to fill a screen in front of a buyer.
This dataset200 rows20 columns
Yours1,000 rows20 columns
blueprint · transportation-demand-forecast-dataset
Behind this dataset
Same schema. As many rows as you need.
These 200 rows came out of a blueprint — 20 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.
Rules it was built with
- Time-stamped demand counts
- Route and location attributes
- Peak/off-peak flag annotated
- External event impact recorded
Open the blueprint in Data Factory
1 credit per row. Preview 20 rows free. Buy rows for the full run.
- Exports
- CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
- Licence
- yours to use, including commercially
- API slug
- transportation-demand-forecast-dataset