Urban Micro Mobility Usage Patterns

This dataset provides detailed trip-level records for shared bikes and scooters in urban environments, including device, user, geospatial, temporal, and IoT metrics. It is designed to enable sustainable transit planning, operational optimization, and in-depth analysis of micro mobility usage patterns, supporting both city planners and mobility providers.

  • last updated 5 Sept 2025
  • by GoMask
The brief that made it

Urban transit planning and optimization

Sample rows

preview · 8 of 200 rows · all 28 columns
trip_idstringweather_conditionstringdevice_battery_start_percentfloatstart_countrystringdevice_idstringdevice_typestringuser_typestringtrip_start_timedatetimetrip_end_timedatetimetrip_duration_minutesfloatstart_latitudefloatstart_longitudefloatend_latitudefloatend_longitudefloatdistance_kmfloatstart_street_addressstringstart_citystringstart_statestringstart_postal_codestringend_street_addressstringend_citystringend_statestringend_postal_codestringend_countrystringtrip_costfloatpayment_methodstringdevice_battery_end_percentfloattrip_statusstring
TRP0001clear91USABK-2041bikeregistered2024-04-01T07:15:23Z2024-04-01T08:12:55Z57.540.757978-73.98554240.765237-73.976556.2350 W 42nd StNew YorkNY1003610 Columbus CircleNew YorkNY10019USA9.25credit_card83completed
TRP0002rain66UKSC-8132scooterguest2024-04-01T12:44:09Z2024-04-01T13:09:18Z25.251.507481-0.12763251.509331-0.1347122.1Piccadilly CircusLondonEnglandW1D 7ETCovent GardenLondonEnglandWC2E 9DDUK3.5mobile_wallet60completed
TRP0003cloudy100JapanBK-6769bikeanonymous2024-04-02T17:03:44Z2024-04-02T17:07:11Z3.535.673343139.71038835.670312139.7044980.25blankTokyoTokyoblankblankTokyoTokyoblankJapan0.75cash100completed
TRP0004rain58RussiaSC-4923scooterguest2024-04-03T09:25:51Z2024-04-03T09:42:37Z16.855.75582537.61729855.76082237.6201521.7Tverskaya St 12MoscowMoscow101000Arbat St 23MoscowMoscow119019Russia2.5debit_card56completed
TRP0005fog98FranceBK-3017bikeregistered2024-04-03T18:11:32Z2024-04-03T19:42:46Z91.248.8566132.35222248.8614562.33415612.2Boulevard Saint-GermainParisÎle-de-France75006Rue de RivoliParisÎle-de-France75001France11.5credit_card86completed
TRP0006cloudy0IcelandSC-1078scooteranonymous2024-04-04T15:02:11Z2024-04-04T15:03:13Z164.126518-21.81743964.127221-21.8253890.07blankReykjavikCapital RegionblankblankReykjavikCapital RegionblankIceland0.5blank0completed
TRP0007clear76IndiaBK-9142bikeregistered2024-04-05T06:41:19Z2024-04-05T07:12:55Z31.628.61393977.20902128.62914477.2141164.9Connaught PlaceDelhiDelhi110001Karol BaghDelhiDelhi110005India7.3debit_card67completed
TRP0008clear60MexicoSC-2187scooterguest2024-04-05T13:53:40Z2024-04-05T14:11:22Z17.719.432608-99.13320919.439998-99.1510111.8Avenida ReformaMexico CityCDMX06600Calle MaderoMexico CityCDMX06000Mexico2.2cash59completed

What the 200 rows show

from the 200-row sample

Other (weather condition) stands out: mean device_battery_start_percent is 10.5, against 71.6 for the rest.

  • 71.5median device_battery_start_percent
  • 2device types
  • 3user types
  • 5payment methods
  • 34end countries
  • 49start states
Mean device_battery_start_percent by weather_condition200 rows
05010077.0clear72 rows69.3rain33 rows63.9snow14 rows51.8fog19 rows75.4cloudy43 rows10.5other19 rows
device_battery_start_percent200 rows, in bands of 10
025502600053632184043050100device_battery_start_percent →

Median 71.5, from 0 to 100.

start_country189 rows with a value · 11 left blank
  1. Japan20
  2. USA14
  3. UK11
  4. Italy11
  5. Sweden11
  6. Russia10
  7. France10
  8. Norway10
  9. Germany9
  10. India8
28 columns by typefrom the column list below
  • string 17
  • float 9
  • datetime 2

Columns

28 columns in three groups
blueprint · 28 columns
columntypedescriptionexample
Text 17 columns
trip_idstringUnique identifier for each trip recorduniqueTRP0001
device_idstringUnique identifier for the shared bike or scooter used in the tripBK-2041
device_typestringType of micro mobility device (e.g., bike, scooter)bike · scooterbike
user_typestringType of user (e.g., registered, guest, anonymous)registered · guest · anonymousregistered
start_street_addressstringStreet address where the trip startedoptional350 W 42nd St
start_citystringCity where the trip startedNew York
start_statestringState or region where the trip startedNY
start_postal_codestringPostal code of the trip starting locationoptional10036
start_countrystringCountry where the trip startedUSA
end_street_addressstringStreet address where the trip endedoptional10 Columbus Circle
end_citystringCity where the trip endedNew York
end_statestringState or region where the trip endedNY
end_postal_codestringPostal code of the trip ending locationoptional10019
end_countrystringCountry where the trip endedUSA
payment_methodstringPayment method used for the tripcredit_card · debit_card · mobile_wallet · cash · other · optionalcredit_card
weather_conditionstringWeather condition during the trip (e.g., clear, rain, snow, fog)6 values · optionalclear
trip_statusstringStatus of the trip (e.g., completed, cancelled, interrupted)completed · cancelled · interruptedcompleted
Numbers 9 columns
trip_duration_minutesfloatDuration of the trip in minutes0 or more57.5
start_latitudefloatLatitude of the trip starting location-90 to 9040.757978
start_longitudefloatLongitude of the trip starting location-180 to 180-73.985542
end_latitudefloatLatitude of the trip ending location-90 to 9040.765237
end_longitudefloatLongitude of the trip ending location-180 to 180-73.97655
distance_kmfloatDistance covered during the trip in kilometers0 or more6.2
trip_costfloatCost of the trip in local currency0 or more · optional9.25
device_battery_start_percentfloatBattery percentage of the device at trip start0 to 100 · optional91
device_battery_end_percentfloatBattery percentage of the device at trip end0 to 100 · optional83
Dates and times 2 columns
trip_start_timedatetimeTimestamp when the trip started2024-04-01T07:15:23Z
trip_end_timedatetimeTimestamp when the trip ended2024-04-01T08:12:55Z

Use it for

  • median device …71.5200 rowsmean device battery s…77.0clear69.3rain63.9snow51.8fog

    A transportation dashboard

    Device_battery_start_percent by weather_condition and a breakdown of start_country. Excel, Power BI or Tableau.

  • Why do the 19 other rows have a mean device_battery_start_percent of 10.5?

    A root-cause class exercise

    Hand out the rows and one question. The answer is in the data, not in the brief.

  • A software demo

    Believable trips with device_id, device_type and user_type to fill a screen in front of a buyer.

Not quite right?

Make it yours.

Same 28 columns, your size and your rules. See 20 rows before you pay.

Preview 20 rows free

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This dataset200 rows28 columns
Yours1,000 rows28 columnsstart_street_address: UK only

blueprint · urban-micro-mobility-usage-patterns

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 28 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
  • Trip start/end location and times required.
  • Vehicle type: bike or scooter.
  • User type: resident or visitor.
  • Payment method optional.
  • Trip duration must be >1 min.
Rows
Open the blueprint in Data Factory

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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
urban-micro-mobility-usage-patterns

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