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neural-transit-v1 | train-network-0019-case-09 | train | {
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neural-transit-v1 | train-network-0014-case-08 | train | {
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neural-transit-v1 | train-network-0004-case-07 | train | {
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neural-transit-v1 | train-network-0009-case-04 | train | {
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neural-transit-v1 | train-network-0006-case-07 | train | {
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} | 08:42:55μ λ리μμμ 보λμκΉμ§ μ΅λ 1ν νμΉμΌλ‘ κ°μ₯ 빨리 λμ°©νλ κ²½λ‘λ₯Ό κ³μ°ν΄μ€. | {
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neural-transit-v1 | train-network-0014-case-03 | train | {
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"name": "κ°λμ"
},
{
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},
{
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},
{
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"origin": "S7b4e",
"destination": "Sec1b",
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} | 08:38:28μ μ¨μμμμ 보λμκΉμ§ μ΅λ 3ν νμΉμΌλ‘ κ°μ₯ 빨리 λμ°©νλ κ²½λ‘λ₯Ό κ³μ°ν΄μ€. | {
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{
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neural-transit-v1 | train-network-0017-case-04 | train | {
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"name": "λ리μ"
},
{
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},
{
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"name": "λ€μ¨μ"
},
{
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"name": "νΈλ₯Έμ"... | {
"origin": "Sbfa4",
"destination": "S2eb5",
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} | 08:42:55μ λ리μμμ λ€μ¨μκΉμ§ μ΅λ 0ν νμΉμΌλ‘ κ°μ₯ 빨리 λμ°©νλ κ²½λ‘λ₯Ό κ³μ°ν΄μ€. | {
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neural-transit-v1 | train-network-0018-case-10 | train | {
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{
"id": "Sdc1d",
"name": "μ¨μμ"
},
{
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},
{
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"name": "λλμ"
},
{
"id": "S3a46",
"name": "μ... | {
"origin": "S8bdc",
"destination": "S3a46",
"departure_seconds": 30104,
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} | 08:21:44μ μλΉμμμ μλ΄μκΉμ§ μ΅λ 1ν νμΉμΌλ‘ κ°μ₯ 빨리 λμ°©νλ κ²½λ‘λ₯Ό κ³μ°ν΄μ€. | {
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} | {
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neural-transit-v1 | train-network-0012-case-07 | train | {
"network_id": "train-network-0012-base",
"stops": [
{
"id": "Sd9ab",
"name": "μλΉμ"
},
{
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{
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},
{
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"name": "νλΉμ"
},
{
"id": "S2471",
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"origin": "S2471",
"destination": "S7783",
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} | 24:05:46μ ν΄μμμμ νλΉμκΉμ§ μ΅λ 0ν νμΉμΌλ‘ κ°μ₯ 빨리 λμ°©νλ κ²½λ‘λ₯Ό κ³μ°ν΄μ€. | {
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}
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} | {
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"S8773... | {
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neural-transit-v1 | train-network-0018-case-07 | train | {
"network_id": "train-network-0018-base",
"stops": [
{
"id": "Sdc1d",
"name": "μ¨μμ"
},
{
"id": "S8bdc",
"name": "μλΉμ"
},
{
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"name": "λ³νμ"
},
{
"id": "S6e5f",
"name": "λλμ"
},
{
"id": "S3a46",
"name": "μλ΄μ"... | {
"origin": "S8bdc",
"destination": "S6e5f",
"departure_seconds": 30134,
"max_transfers": 0
} | 08:22:14μ μλΉμμμ λλμκΉμ§ μ΅λ 0ν νμΉμΌλ‘ κ°μ₯ 빨리 λμ°©νλ κ²½λ‘λ₯Ό κ³μ°ν΄μ€. | {
"status": "success",
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"legs": [
{
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"from_stop": "S8bdc",
"to_stop": "S6e5f",
"departure_seconds": 30164,
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}
]
} | {
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"S6e5f... | {
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neural-transit-v1 | train-network-0000-case-02 | train | {
"network_id": "train-network-0000-base",
"stops": [
{
"id": "S8cd0",
"name": "λ리μ"
},
{
"id": "S7248",
"name": "ν΄μμ"
},
{
"id": "S7d62",
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},
{
"id": "S4771",
"name": "μλ΄μ"
},
{
"id": "S0cce",
"name": "λ°λ€μ"... | {
"origin": "S0cce",
"destination": "S7248",
"departure_seconds": 88166,
"max_transfers": 2
} | 24:29:26μ λ°λ€μμμ ν΄μμκΉμ§ μ΅λ 2ν νμΉμΌλ‘ κ°μ₯ 빨리 λμ°©νλ κ²½λ‘λ₯Ό κ³μ°ν΄μ€. | {
"status": "success",
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},
{
... | {
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"counterfactual_group": null,
"coun... |
neural-transit-v1 | train-network-0007-case-03 | train | {
"network_id": "train-network-0007-base",
"stops": [
{
"id": "Sdf6f",
"name": "λ°λ€μ"
},
{
"id": "Sc538",
"name": "μλ΄μ"
},
{
"id": "S3b35",
"name": "λ§λ£¨μ"
},
{
"id": "Sa09a",
"name": "κ°λμ"
},
{
"id": "S6152",
"name": "ν΄μμ"... | {
"origin": "Sb0fd",
"destination": "Sc538",
"departure_seconds": 31827,
"max_transfers": 3
} | 08:50:27μ μλΉμμμ μλ΄μκΉμ§ μ΅λ 3ν νμΉμΌλ‘ κ°μ₯ 빨리 λμ°©νλ κ²½λ‘λ₯Ό κ³μ°ν΄μ€. | {
"status": "success",
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"duration_seconds": 1258,
"boardings": 4,
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"legs": [
{
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"from_stop": "Sb0fd",
"to_stop": "Sa09a",
"departure_seconds": 31887,
"arrival_seconds": 31917
},
{
... | {
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},
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},
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"Sa09a... | {
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neural-transit-v1 | train-network-0000-case-00 | train | {
"network_id": "train-network-0000-base",
"stops": [
{
"id": "S8cd0",
"name": "λ리μ"
},
{
"id": "S7248",
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},
{
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},
{
"id": "S4771",
"name": "μλ΄μ"
},
{
"id": "S0cce",
"name": "λ°λ€μ"... | {
"origin": "S0cce",
"destination": "S8cd0",
"departure_seconds": 88166,
"max_transfers": 0
} | 24:29:26μ λ°λ€μμμ λ리μκΉμ§ μ΅λ 0ν νμΉμΌλ‘ κ°μ₯ 빨리 λμ°©νλ κ²½λ‘λ₯Ό κ³μ°ν΄μ€. | {
"status": "success",
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"boardings": 1,
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"legs": [
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"from_stop": "S0cce",
"to_stop": "S8cd0",
"departure_seconds": 88226,
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}
]
} | {
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},
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"S4771... | {
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Synthetic Transit Routing KO
A small synthetic dataset for studying whether language models can compute transit routes directly from a provided timetable, rather than calling an external routing engine during inference.
The dataset contains 352 examples: 220 training, 44 validation, 44 test, and 44 out-of-distribution (OOD) examples. Training networks contain 5β6 stops, while OOD networks contain 8β10 stops. Stop names and routing questions are in Korean.
Data format
Each JSONL record contains:
network: synthetic stops, scheduled trips, and transfer time.query: origin, destination, departure time, and transfer limit.question: a Korean routing question.target: the earliest-arrival reference answer, including route legs, arrival time, total duration, boardings, and transfers.trace: offline RAPTOR computation snapshots.schema_version,sample_id,split, andmetadata: identifiers and generation metadata.
Times are represented as seconds from the start of the service day and may exceed 86,400 for after-midnight service.
Generation and validation
Reference answers were generated using a raptor-demo adapter and cross-checked against an independent bounded exhaustive boarding oracle. All 352 reference answers matched, with zero mismatches.
Examples cover direct journeys, up to three transfers, missed departures and connections, after-midnight journeys, unavailable routes, and timetable modifications that change the correct answer.
Network groups and recorded topology hashes are separated across splits. However, these hashes do not constitute a complete graph-isomorphism check.
Intended use
Use the timetable and routing question as model inputs, and the reference answer as supervision. These files use a custom structured schema, not a ready-made messages chat format.
During evaluation, do not expose target, trace, or answer-related metadata to the model. Keep test and OOD examples held out from training.
Limitations
This is an experimental synthetic pilot, not a nationwide or real-world transit benchmark. It excludes walking, real-time delays, frequency-based service, pickup/drop-off restrictions, and ambiguous stop names.
Reference-label validation does not imply that a fine-tuned model can reliably calculate routes or generalize to real transit networks. The dataset provides timetables as inputs; it does not encode an entire GTFS feed into model parameters.
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