Transportation Ticket Sales Patterns

This dataset provides granular records of transportation ticket sales, including route details, provider information, customer demographics, pricing, and seat capacity. It enables comprehensive analysis of sales patterns, demand forecasting, and supports strategic decisions on pricing and resource allocation for transportation providers.

  • last updated 20 Aug 2025
  • by GoMask
The brief that made it

Dynamic pricing optimization based on sales patterns and demand

Sample rows

preview · 8 of 200 rows · all 21 columns
ticket_idstringseat_classstringticket_pricefloatis_refundedbooleanprovider_namestringprovider_idstringroute_idstringroute_originstringroute_destinationstringdeparture_datetimedatetimearrival_datetimedatetimeticket_sale_datetimedatetimecurrencystringseat_numberstringcapacityintegersold_seatsintegercustomer_idstringcustomer_ageintegercustomer_genderstringpurchase_channelstringrefund_datetimedatetime
TCKT0001economy49.99falseMetroLineP001R1001New YorkBoston2024-07-12T08:30:002024-07-12T11:45:002024-06-28T13:12:00USD12A120108CUST000134femaleonlineblank
TCKT0002business299.99falseSkyHopP002R1002Los AngelesSan Francisco2024-07-19T10:00:002024-07-19T11:30:002024-07-10T15:20:00USD2C8071CUST000242malemobileblank
TCKT0003economy27.5falseBusGoP003R1003BerlinMunich2024-07-22T14:00:002024-07-22T18:30:002024-07-05T10:34:00EURblank5755CUST000323femalekioskblank
TCKT0004economy69falseRapidRailP004R1004ChicagoDetroit2024-07-15T09:15:002024-07-15T11:45:002024-07-01T17:05:00USD6B9582CUST000429maleonlineblank
TCKT0005business99.99falseSouthernRailP005R1005SydneyMelbourne2024-07-17T13:00:002024-07-17T18:00:002024-07-02T12:45:00AUD1A200135CUST000557maleagentblank
TCKT0006economy22falseIslandFerryP006R1006HonoluluMaui2024-07-14T15:00:002024-07-14T17:30:002024-06-29T11:22:00USDblank11056CUST000615otherkioskblank
TCKT0007economy18.5falseUrbanTramP007R1007LisbonPorto2024-07-23T07:45:002024-07-23T11:15:002024-07-06T08:55:00EURblank300135CUST00078unknownonlineblank
TCKT0008business120falseNightTrainP008R1008MoscowSt. Petersburg2024-07-18T20:00:002024-07-19T07:40:002024-07-03T19:30:00RUB8D400378CUST000862maleagentblank

What the 200 rows show

from the 200-row sample

First (seat class) stands out: mean ticket_price is 609.7, against 75.4 for the rest.

  • 7%is_refunded = true
  • 72.5median ticket_price
  • 4customer genders
  • 5purchase channels
  • 11currencies
  • 95median capacity
Mean ticket_price by seat_class200 rows
040080044.9economy119 rows143.9business53 rows609.7first28 rows
ticket_price200 rows, in bands of 200
085170165166801408001,400ticket_price →

Median 72.5, from 7.8 to 1,381.

provider_name200 rows · top 10 of 29 values
  1. SkyHop17
  2. MetroLine15
  3. NightTrain14
  4. BusGo13
  5. RapidRail13
  6. IslandFerry13
  7. TransAfrica13
  8. FlyJet13
  9. SouthernRail11
  10. SuburbanRail10
21 columns by typefrom the column list below
  • string 12
  • integer 3
  • float 1
  • datetime 4
  • boolean 1

Columns

21 columns in four groups
blueprint · 21 columns
columntypedescriptionexample
Text 12 columns
ticket_idstringUnique identifier for each ticket solduniqueTCKT0001
provider_idstringUnique identifier for the transportation providerP001
provider_namestringName of the transportation providerMetroLine
route_idstringUnique identifier for the routeR1001
route_originstringStarting location of the routeNew York
route_destinationstringEnding location of the routeBoston
currencystringCurrency code used for the ticket price (ISO 4217 format)11 currenciesUSD
seat_classstringClass of seat purchased (e.g., economy, business, first)economy · business · firsteconomy
seat_numberstringAssigned seat number for the ticketoptional12A
customer_idstringUnique identifier for the customer purchasing the ticketoptionalCUST0001
customer_genderstringGender of the customermale · female · other · unknown · optionalfemale
purchase_channelstringChannel through which the ticket was purchased (e.g., online, agent, kiosk)online · agent · kiosk · mobile · otheronline
Numbers 4 columns
ticket_pricefloatPrice paid for the ticket0 or more49.99
capacityintegerTotal seat capacity for the route at the time of sale1 or more120
sold_seatsintegerNumber of seats sold for the route at the time of sale0 or more108
customer_ageintegerAge of the customer at the time of purchase0 or more · optional34
Dates and times 4 columns
departure_datetimedatetimeScheduled departure date and time for the route2024-07-12T08:30:00
arrival_datetimedatetimeScheduled arrival date and time for the routeoptional2024-07-12T11:45:00
ticket_sale_datetimedatetimeDate and time when the ticket was sold2024-06-28T13:12:00
refund_datetimedatetimeDate and time of refund, if applicableoptional2024-07-15T11:00:00
True or false 1 column
is_refundedbooleanIndicates if the ticket was refundedfalse

Use it for

  • is refunded7%13 of 200 rowsmean ticket price by …44.9economy143.9busine…609.7first

    A transportation dashboard

    The is_refunded rate, ticket_price by seat_class and a breakdown of provider_name. Excel, Power BI or Tableau.

  • Why do the 28 first rows have a mean ticket_price of 609.7?

    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 tickets with provider_id, provider_name and route_id to fill a screen in front of a buyer.

Not quite right?

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blueprint · transportation-ticket-sales-patterns

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 21 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
  • Include route and station IDs
  • Track ticket sales per day
  • Segment by ticket type
  • Flag peak sales events
Rows
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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
transportation-ticket-sales-patterns

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