Trip Booking Cancellation Reasons
This dataset provides detailed logs of transportation booking cancellations, capturing standardized and free-text reasons, financial details, and operational context. It enables organizations to analyze cancellation patterns, identify operational bottlenecks, and optimize revenue retention strategies. The dataset is ideal for root-cause analysis, customer experience improvement, and predictive modeling of cancellations.
Sample rows
preview · 8 of 65 rows · all 15 columns| cancellation_idstring | initiated_bystring | refund_amountfloat | cancellation_fee_appliedboolean | operator_idstring | booking_idstring | user_idstring | cancellation_datetimedatetime | trip_start_datetimedatetime | cancellation_reason_codestring | refund_issuedboolean | cancellation_fee_amountfloat | route_idstring | vehicle_idstring | cancellation_reason_textstring |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| CAN-10001 | customer | 22.5 | false | OP-88 | BK-20001 | USR-3001 | 2023-09-18T08:10:15 | 2023-09-18T10:30:00 | customer_request | true | 0 | RT-01 | VH-501 | Customer changed their plans and no longer requires the service. |
| CAN-10002 | operator | 15 | false | OP-89 | BK-20002 | USR-3002 | 2023-11-02T07:20:00 | 2023-11-02T08:00:00 | weather | true | 0 | RT-02 | VH-502 | Heavy snow forecast; operator canceled all morning trips. |
| CAN-10003 | system | 0 | false | OP-90 | BK-20003 | USR-3003 | 2023-10-04T18:58:00 | 2023-10-05T07:00:00 | payment_issue | false | 0 | RT-03 | VH-503 | Payment could not be processed after multiple attempts. |
| CAN-10004 | operator | 18.75 | false | OP-91 | BK-20004 | USR-3004 | 2023-08-22T13:24:11 | 2023-08-22T15:00:00 | driver_unavailable | true | 0 | RT-04 | VH-504 | Assigned driver reported sick an hour before trip. |
| CAN-10005 | customer | 0 | true | OP-92 | BK-20005 | USR-3005 | 2023-07-18T21:50:00 | 2023-07-19T06:00:00 | customer_request | true | 18 | RT-05 | VH-505 | Customer canceled due to personal emergency. |
| CAN-10006 | operator | 14 | false | OP-88 | BK-20006 | USR-3006 | 2023-12-01T06:35:00 | 2023-12-01T08:00:00 | vehicle_issue | true | 0 | RT-06 | VH-506 | Vehicle maintenance issue detected pre-trip inspection. |
| CAN-10007 | customer | 12.5 | false | OP-93 | BK-20007 | USR-3007 | 2023-09-29T09:00:00 | 2023-09-29T11:00:00 | scheduling_conflict | true | 0 | RT-07 | VH-507 | Customer realized they had a scheduling conflict with another event. |
| CAN-10008 | operator | 19 | false | OP-94 | BK-20008 | USR-3008 | 2023-10-15T15:15:00 | 2023-10-15T17:00:00 | route_change | true | 0 | RT-08 | VH-508 | Route was changed due to roadworks and customer declined new route. |
| CAN-10009 | customer | 0 | true | OP-95 | BK-20009 | USR-3009 | 2023-11-28T14:40:00 | 2023-11-28T20:00:00 | customer_request | false | 20 | RT-09 | VH-509 | Customer had to reschedule for a later date. |
| CAN-10010 | customer | 0 | true | OP-88 | BK-20010 | USR-3010 | 2023-10-03T08:25:00 | 2023-10-03T09:00:00 | other | false | 10 | RT-10 | VH-510 | No specific reason provided by the customer. |
| CAN-10011 | operator | 21 | false | OP-89 | BK-20011 | USR-3011 | 2023-09-10T15:40:00 | 2023-09-10T17:00:00 | vehicle_issue | true | 0 | RT-11 | VH-511 | Unexpected engine trouble detected before boarding. |
| CAN-10012 | system | 0 | false | OP-91 | BK-20012 | USR-3001 | 2023-12-12T11:30:00 | 2023-12-12T13:00:00 | payment_issue | false | 0 | RT-12 | VH-512 | Customer's card expired before payment was processed. |
| CAN-10013 | operator | 15 | false | OP-92 | BK-20013 | USR-3012 | 2023-08-30T16:05:00 | 2023-08-30T19:00:00 | scheduling_conflict | true | 0 | RT-13 | VH-513 | Operator double-booked driver for overlapping trips. |
| CAN-10014 | operator | 11 | false | OP-93 | BK-20014 | USR-3013 | 2023-09-12T07:42:00 | 2023-09-12T09:45:00 | weather | true | 0 | RT-14 | VH-514 | Trip canceled due to severe thunderstorm warning. |
| CAN-10015 | customer | 0 | true | OP-94 | BK-20015 | USR-3014 | 2023-07-23T14:18:00 | 2023-07-23T15:00:00 | customer_request | false | 16 | RT-15 | VH-515 | Customer decided to use an alternative transportation method. |
| CAN-10016 | driver | 13.5 | false | OP-95 | BK-20016 | USR-3011 | 2023-10-17T19:12:00 | 2023-10-17T20:00:00 | vehicle_issue | true | 0 | RT-16 | VH-516 | Brake issue detected during pre-trip inspection. |
| CAN-10017 | operator | 18 | false | OP-90 | BK-20017 | USR-3015 | 2023-08-07T11:10:00 | 2023-08-07T13:00:00 | driver_unavailable | true | 0 | RT-17 | VH-517 | Driver was unable to arrive due to personal emergency. |
| CAN-10018 | customer | 17 | false | OP-88 | BK-20018 | USR-3016 | 2023-09-05T13:00:00 | 2023-09-05T15:00:00 | route_change | true | 0 | RT-18 | VH-518 | Route detour added extra 40 minutes; customer declined. |
| CAN-10019 | system | 0 | false | OP-89 | BK-20019 | USR-3017 | 2023-10-09T08:05:00 | 2023-10-09T10:30:00 | payment_issue | false | 0 | RT-19 | VH-519 | Credit card declined. Customer could not provide another. |
| CAN-10020 | operator | 20 | false | OP-90 | BK-20020 | USR-3018 | 2023-08-15T16:40:00 | 2023-08-15T18:00:00 | weather | true | 0 | RT-20 | VH-520 | Flooding on main route led to operator cancellation. |
What the 65 rows show
from the 65-row sampleCustomer (initiated by) stands out: 19 of its 24 rows have cancellation_
- 29%cancellation_
fee_ applied = true - 16.0median refund_
amount - 8cancellation reason codes
- 0.0median cancellation_
fee_ amount
Median 16.0, from 0.0 to 60.0.
- string 9
- float 2
- datetime 2
- boolean 2
Columns
15 columns in four groups| column | type | description | example |
|---|---|---|---|
| Text 9 columns | |||
cancellation_id | string | Unique identifier for each cancellation eventunique | CAN-10001 |
booking_id | string | Unique identifier for the original trip booking | BK-20001 |
user_id | string | Unique identifier for the customer who made the booking | USR-3001 |
cancellation_reason_code | string | Standardized code representing the reason for cancellation8 values | customer_request |
cancellation_reason_text | string | Detailed free-text explanation of the cancellation reasonoptional | No explanation provided. |
initiated_by | string | Who initiated the cancellation: customer, operator, driver, or systemcustomer · operator · driver · system | customer |
route_id | string | Identifier for the route associated with the bookingoptional | RT-01 |
vehicle_id | string | Identifier for the vehicle assigned to the tripoptional | VH-501 |
operator_id | string | Identifier for the operator or company managing the bookingoptional | OP-88 |
| Numbers 2 columns | |||
refund_amount | float | Amount refunded to the customer, if applicable0 or more · optional | 22.5 |
cancellation_fee_amount | float | Amount charged as a cancellation fee, if applicable0 or more · optional | 0 |
| Dates and times 2 columns | |||
cancellation_datetime | datetime | Timestamp when the cancellation was made | 2023-09-18T08:10:15 |
trip_start_datetime | datetime | Scheduled start date and time of the trip | 2023-09-18T10:30:00 |
| True or false 2 columns | |||
refund_issued | boolean | Indicates whether a refund was issued for the cancellation | true |
cancellation_fee_applied | boolean | Indicates if a cancellation fee was charged | false |
Use it for
A transportation dashboard
The cancellation_
fee_ applied rate, refund_ amount by initiated_ by and a breakdown of operator_ id. Excel, Power BI or Tableau. Why do 19 of 65 rows have cancellation_
fee_ applied = true? A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
- Cancellations65CAN-1000122.5customerCAN-100050customerCAN-100090customer
A software demo
Believable cancellations with booking_
id, user_ id and cancellation_ datetime to fill a screen in front of a buyer.
This dataset65 rows15 columns
Yours1,000 rows15 columns
blueprint · trip-booking-cancellation-reasons
Behind this dataset
Same schema. As many rows as you need.
These 65 rows came out of a blueprint — 15 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 cancellation timestamp and reason
- Link to trip and user IDs
- Flag cancellations within 1 hour of booking
- Aggregate by reason type
- Summarize daily and weekly cancellation rates
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
- trip-booking-cancellation-reasons