SurvCancel

SurvCancel is an anonymized longitudinal dataset for dynamic passenger cancellation prediction in on-demand ride-sharing systems. Each JSONL record is one focal order with static trip attributes, a terminal outcome, and an ordered sequence of pre-pickup system snapshots. Snapshots are sampled every 10 seconds and include the focal order state, surrounding active orders, available vehicles, and the matched vehicle when present.

This release is intentionally flat: the four public region shards are exposed as one Hugging Face train split. Benchmark train/validation/test partitions are constructed by the accompanying code at the order level.

Release Statistics

The release contains 173,037 focal orders and 7,944,171 temporal snapshots.

Region shard	Days	Orders	Snapshots	Pre-match cancel	Matched	Post-match cancel	Completed
R1	38	60,548	2,717,147	26,511	34,037	10,987	23,050
R2	72	6,518	240,075	310	6,208	872	5,336
R3	72	42,106	2,099,571	13,404	28,702	7,667	21,035
R4	72	63,865	2,887,378	10,449	53,416	10,651	42,765
Total	--	173,037	7,944,171	50,674	122,363	30,177	92,186

Files

• train-R1.jsonl, train-R2.jsonl, train-R3.jsonl, train-R4.jsonl: public region shards
• sample/sample_records.jsonl: small sample for smoke tests
• SCHEMA.md: field-level schema

Loading With datasets

from datasets import load_dataset

dataset = load_dataset(
    "survcancel-anonymous/SurvCancel",
    split="train",
    streaming=True,
)

Load one region directly:

from datasets import load_dataset

r1 = load_dataset(
    "json",
    data_files="hf://datasets/survcancel-anonymous/SurvCancel/train-R1.jsonl",
    split="train",
    streaming=True,
)

Anonymization

Identifiers are replaced with salted HMAC-SHA256 surrogate strings:

• order_id -> ord_*
• vehicle_id and matched_vehicle_id -> veh_*
• region_id -> reg_*

The same original identifier in the same namespace maps to the same surrogate. The private salt and source-to-surrogate mappings are not released. Coordinates are standardized planar coordinates, not raw latitude/longitude.

Intended Use

This dataset is intended for academic and non-commercial research on survival analysis, dynamic event prediction, demand-responsive transportation, ride-pooling, and cancellation or pickup time-to-event modeling.

License

This dataset is released under the Creative Commons Attribution-NonCommercial- ShareAlike 4.0 International License (CC BY-NC-SA 4.0).