Transportation
22 columns
200 rows
8 formats

Shared Mobility Usage Patterns

This dataset provides granular records of shared mobility trips, including user demographics, vehicle types, trip routes, payment details, and trip purposes. It enables transportation startups and urban planners to analyze mobility patterns, optimize services, and identify opportunities for expansion based on real usage data.

last updated 20 Aug 2025
by GoMask

The brief that made it

Service optimization and route planning for shared mobility providers

Sample rows

preview · 8 of 200 rows · all 22 columns

trip_idstring	vehicle_typestring	trip_duration_minutesfloat	is_first_tripboolean	payment_methodstring	user_idstring	vehicle_idstring	start_datetimedatetime	end_datetimedatetime	start_latitudefloat	start_longitudefloat	end_latitudefloat	end_longitudefloat	trip_distance_kmfloat	fare_amountfloat	user_ageinteger	user_genderstring	start_location_namestring	end_location_namestring	membership_typestring	trip_purposestring	promotion_appliedboolean
TRIP0001	bike	20	false	wallet	USR0001	VEH0001	2024-04-01T07:15:00Z	2024-04-01T07:35:00Z	40.748817	-73.985428	40.752726	-73.977229	4.2	2.8	29	female	Empire State Building NYC	Grand Central Terminal	monthly	commute	false
TRIP0002	e-scooter	14	false	credit_card	USR0002	VEH0002	2024-04-02T18:05:00Z	2024-04-02T18:19:00Z	51.507351	-0.127758	51.515617	-0.091998	3.6	3.2	35	male	Westminster London	Liverpool Street Station	annual	commute	false
TRIP0003	car	72	false	debit_card	USR0003	VEH0003	2024-04-06T11:00:00Z	2024-04-06T12:12:00Z	35.689487	139.691706	35.693825	139.703356	9.1	19.5	60	female	Shinjuku Tokyo	Shibuya Crossing	annual	leisure	false
TRIP0004	scooter	13	true	wallet	USR0004	VEH0004	2024-04-07T14:32:00Z	2024-04-07T14:45:00Z	52.520006	13.404954	52.526422	13.41557	2.6	1.7	22	male	Alexanderplatz Berlin	Prenzlauer Berg Station	pay_as_you_go	leisure	true
TRIP0005	e-bike	17	false	credit_card	USR0005	VEH0005	2024-04-10T09:05:00Z	2024-04-10T09:22:00Z	19.075984	72.877656	19.098599	72.836597	6	3.5	31	female	Gateway of India Mumbai	Bandra Station Mumbai	monthly	commute	false
TRIP0006	car	90	false	debit_card	USR0006	VEH0006	2024-04-12T19:25:00Z	2024-04-12T20:55:00Z	55.755825	37.617298	55.765409	37.605682	11.3	24.7	68	male	Red Square Moscow	Gorky Park Moscow	annual	leisure	false
TRIP0007	e-scooter	12	false	wallet	USR0007	VEH0007	2024-04-13T15:50:00Z	2024-04-13T16:02:00Z	48.856613	2.352222	48.85837	2.294481	3.2	2.6	27	female	Le Marais Paris	Eiffel Tower Paris	monthly	errand	false
TRIP0008	bike	20	false	wallet	USR0008	VEH0008	2024-04-15T08:05:00Z	2024-04-15T08:25:00Z	37.774929	-122.419416	37.804363	-122.271111	5.7	3.1	34	male	Market Street San Francisco	Oakland Ferry Terminal	monthly	commute	false
TRIP0009	e-bike	19	false	credit_card	USR0009	VEH0009	2024-04-16T17:40:00Z	2024-04-16T17:59:00Z	28.613939	77.209021	28.62836	77.218671	4.8	2.9	25	male	Connaught Place Delhi	India Gate Delhi	monthly	errand	false
TRIP0010	scooter	13	true	wallet	USR0010	VEH0010	2024-04-18T10:15:00Z	2024-04-18T10:28:00Z	41.902782	12.496366	41.89021	12.492231	2.3	1.5	19	female	Piazza Navona Rome	Colosseum Rome	pay_as_you_go	errand	true
TRIP0011	car	80	false	debit_card	USR0011	VEH0011	2024-04-20T13:30:00Z	2024-04-20T14:50:00Z	-33.86882	151.209296	-33.873651	151.206889	8.7	15.8	55	male	Sydney Opera House	Town Hall Station Sydney	annual	leisure	false
TRIP0012	other	60	true	other	USR0012	VEH0012	2024-04-21T03:09:00Z	2024-04-21T04:09:00Z	-90	0	-90	0	0	0	blank	prefer_not_to_say	South Pole Station	South Pole Station	other	other	true
TRIP0013	bike	14	false	wallet	USR0013	VEH0013	2024-04-22T16:40:00Z	2024-04-22T16:54:00Z	48.208174	16.373819	48.210033	16.360035	2.1	1.3	32	male	Stephansplatz Vienna	Belvedere Palace Vienna	monthly	commute	false
TRIP0014	e-bike	25	false	credit_card	USR0014	VEH0014	2024-04-23T07:10:00Z	2024-04-23T07:35:00Z	40.416775	-3.70379	40.41922	-3.686614	3.5	2.6	28	female	Puerta del Sol Madrid	Retiro Park Madrid	monthly	commute	false
TRIP0015	scooter	10	true	wallet	USR0015	VEH0015	2024-04-25T20:20:00Z	2024-04-25T20:30:00Z	34.052235	-118.243683	34.056219	-118.236502	1.6	1.1	16	male	Union Station Los Angeles	Grand Park LA	pay_as_you_go	leisure	true
TRIP0016	car	55	false	debit_card	USR0016	VEH0016	2024-04-26T21:55:00Z	2024-04-26T22:50:00Z	1.352083	103.819836	1.295547	103.858441	7.2	12.2	72	female	Orchard Road Singapore	Marina Bay Sands	annual	commute	false
TRIP0017	e-scooter	16	false	wallet	USR0017	VEH0017	2024-04-28T16:45:00Z	2024-04-28T17:01:00Z	59.329323	18.068581	59.334591	18.06324	2.9	2.4	23	male	Stockholm City Hall	Kungsträdgården Park	pay_as_you_go	errand	false
TRIP0018	e-bike	18	false	credit_card	USR0018	VEH0018	2024-04-29T12:30:00Z	2024-04-29T12:48:00Z	35.676192	139.650311	35.689634	139.692101	5.4	3.3	40	female	Tokyo Station	Meiji Shrine Tokyo	monthly	commute	false
TRIP0019	bike	17	false	wallet	USR0019	VEH0019	2024-04-30T08:10:00Z	2024-04-30T08:27:00Z	37.566535	126.977969	37.570377	126.992378	3.2	2.2	36	male	Gwanghwamun Seoul	Dongdaemun Seoul	monthly	errand	false
TRIP0020	scooter	11	true	wallet	USR0020	VEH0020	2024-05-02T11:55:00Z	2024-05-02T12:06:00Z	45.464211	9.191383	45.478147	9.227972	3.5	1.9	26	female	Duomo di Milano	Milano Centrale Station	pay_as_you_go	errand	true

What the 200 rows show

from the 200-row sample

Car (vehicle type) stands out: mean trip_duration_minutes is 77.1, against 21.1 for the rest.

20%is_first_trip = true
21.0median trip_duration_minutes
4user genders
4membership types
4trip purposes
3.6median trip_distance_km

Mean trip_duration_minutes by vehicle_type200 rows

trip_duration_minutes200 rows, in bands of 20

Median 21.0, from 0.05 to 120.0.

payment_method200 rows · 6 values

wallet94
debit_card40
credit_card39
cash15
other7
promo_code5

22 columns by typefrom the column list below

string 10
integer 1
float 7
datetime 2
boolean 2

Columns

22 columns in four groups

blueprint · 22 columns
column	type	description	example
Text 10 columns
`trip_id`	string	Unique identifier for each shared mobility tripunique	TRIP0001
`user_id`	string	Unique identifier for the user taking the trip	USR0001
`vehicle_id`	string	Unique identifier for the shared mobility vehicle used	VEH0001
`vehicle_type`	string	Type of shared mobility vehicle (e.g., bike, scooter, car)6 values	bike
`payment_method`	string	Payment method used for the trip6 values	wallet
`user_gender`	string	Gender of the usermale · female · other · prefer_not_to_say · optional	female
`start_location_name`	string	Name or description of the trip's starting locationoptional	Westminster London
`end_location_name`	string	Name or description of the trip's ending locationoptional	Grand Central Terminal
`membership_type`	string	Type of user membership (e.g., pay-as-you-go, monthly, annual)pay_as_you_go · monthly · annual · other · optional	monthly
`trip_purpose`	string	Purpose of the trip (e.g., commute, leisure, errand, other)commute · leisure · errand · other · optional	commute
Numbers 8 columns
`start_latitude`	float	Latitude coordinate where the trip started-90 to 90	40.748817
`start_longitude`	float	Longitude coordinate where the trip started-180 to 180	-73.985428
`end_latitude`	float	Latitude coordinate where the trip ended-90 to 90	40.752726
`end_longitude`	float	Longitude coordinate where the trip ended-180 to 180	-73.977229
`trip_distance_km`	float	Total distance traveled during the trip in kilometers0 or more	4.2
`trip_duration_minutes`	float	Total duration of the trip in minutes0 or more	20
`fare_amount`	float	Total fare charged for the trip0 or more	2.8
`user_age`	integer	Age of the user at the time of the trip12 to 120 · optional	29
Dates and times 2 columns
`start_datetime`	datetime	Date and time when the trip started	2024-04-01T07:15:00Z
`end_datetime`	datetime	Date and time when the trip ended	2024-04-01T07:35:00Z
True or false 2 columns
`is_first_trip`	boolean	Indicates if this is the user's first tripoptional	false
`promotion_applied`	boolean	Indicates if a promotion or discount was applied to the fareoptional	false

Use it for

A transportation dashboard
The is_first_trip rate, trip_duration_minutes by vehicle_type and a breakdown of payment_method. Excel, Power BI or Tableau.
Why do the 49 car rows have a mean trip_duration_minutes of 77.1?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Trips200
TRIP000120bike
TRIP000413scooter
TRIP001013scooter
A software demo
Believable trips with user_id, vehicle_id and vehicle_type to fill a screen in front of a buyer.

This dataset200 rows22 columns

Yours1,000 rows22 columnsstart_location_name: UK only

blueprint · shared-mobility-usage-patterns

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 22 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

Record ride start and end points
Include vehicle type (bike, scooter, car)
Track trip duration and frequency
Exclude long-term rentals

Rows1,000 rows10,000 rows100,000 rows1,000,000 rows

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: shared-mobility-usage-patterns

What should your data show?

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Sample rows

What the 200 rows show

Columns

Use it for

A transportation dashboard

A root-cause class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Transportation

What should your data show?