{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:NHT2LJAQYNNWITRLWJUGXKTCJA","short_pith_number":"pith:NHT2LJAQ","schema_version":"1.0","canonical_sha256":"69e7a5a410c35b644e2bb2686baa624826677d48b149abed1d21bbdb667458ca","source":{"kind":"arxiv","id":"1811.04345","version":1},"attestation_state":"computed","paper":{"title":"Optimizing Taxi Carpool Policies via Reinforcement Learning and Spatio-Temporal Mining","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Ishan Jindal, Jieping Ye, Matthew Nokleby, Xuewen Chen, Zhiwei Qin","submitted_at":"2018-11-11T04:13:31Z","abstract_excerpt":"In this paper, we develop a reinforcement learning (RL) based system to learn an effective policy for carpooling that maximizes transportation efficiency so that fewer cars are required to fulfill the given amount of trip demand. For this purpose, first, we develop a deep neural network model, called ST-NN (Spatio-Temporal Neural Network), to predict taxi trip time from the raw GPS trip data. Secondly, we develop a carpooling simulation environment for RL training, with the output of ST-NN and using the NYC taxi trip dataset. In order to maximize transportation efficiency and minimize traffic "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1811.04345","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-11-11T04:13:31Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"7aab686010cffca7563e785c61a823bffc1fa07a890e4ac99a3c907a33a5d6e1","abstract_canon_sha256":"43c3e6d80361c7a359e72893044a1a26b7578b6fdbda127af0e0234f1d0d1258"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:01:05.508308Z","signature_b64":"D6vp87Q5n/Hj8b3WzYHvk98nobWa4aGG2XkTbWM/jFT2ufHGWVRlIqM82RkOWuYVBo0nCUtm2tXeXp4lLj4gCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"69e7a5a410c35b644e2bb2686baa624826677d48b149abed1d21bbdb667458ca","last_reissued_at":"2026-05-18T00:01:05.507645Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:01:05.507645Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Optimizing Taxi Carpool Policies via Reinforcement Learning and Spatio-Temporal Mining","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Ishan Jindal, Jieping Ye, Matthew Nokleby, Xuewen Chen, Zhiwei Qin","submitted_at":"2018-11-11T04:13:31Z","abstract_excerpt":"In this paper, we develop a reinforcement learning (RL) based system to learn an effective policy for carpooling that maximizes transportation efficiency so that fewer cars are required to fulfill the given amount of trip demand. For this purpose, first, we develop a deep neural network model, called ST-NN (Spatio-Temporal Neural Network), to predict taxi trip time from the raw GPS trip data. Secondly, we develop a carpooling simulation environment for RL training, with the output of ST-NN and using the NYC taxi trip dataset. In order to maximize transportation efficiency and minimize traffic "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.04345","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1811.04345","created_at":"2026-05-18T00:01:05.507736+00:00"},{"alias_kind":"arxiv_version","alias_value":"1811.04345v1","created_at":"2026-05-18T00:01:05.507736+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.04345","created_at":"2026-05-18T00:01:05.507736+00:00"},{"alias_kind":"pith_short_12","alias_value":"NHT2LJAQYNNW","created_at":"2026-05-18T12:32:40.477152+00:00"},{"alias_kind":"pith_short_16","alias_value":"NHT2LJAQYNNWITRL","created_at":"2026-05-18T12:32:40.477152+00:00"},{"alias_kind":"pith_short_8","alias_value":"NHT2LJAQ","created_at":"2026-05-18T12:32:40.477152+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NHT2LJAQYNNWITRLWJUGXKTCJA","json":"https://pith.science/pith/NHT2LJAQYNNWITRLWJUGXKTCJA.json","graph_json":"https://pith.science/api/pith-number/NHT2LJAQYNNWITRLWJUGXKTCJA/graph.json","events_json":"https://pith.science/api/pith-number/NHT2LJAQYNNWITRLWJUGXKTCJA/events.json","paper":"https://pith.science/paper/NHT2LJAQ"},"agent_actions":{"view_html":"https://pith.science/pith/NHT2LJAQYNNWITRLWJUGXKTCJA","download_json":"https://pith.science/pith/NHT2LJAQYNNWITRLWJUGXKTCJA.json","view_paper":"https://pith.science/paper/NHT2LJAQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1811.04345&json=true","fetch_graph":"https://pith.science/api/pith-number/NHT2LJAQYNNWITRLWJUGXKTCJA/graph.json","fetch_events":"https://pith.science/api/pith-number/NHT2LJAQYNNWITRLWJUGXKTCJA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NHT2LJAQYNNWITRLWJUGXKTCJA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NHT2LJAQYNNWITRLWJUGXKTCJA/action/storage_attestation","attest_author":"https://pith.science/pith/NHT2LJAQYNNWITRLWJUGXKTCJA/action/author_attestation","sign_citation":"https://pith.science/pith/NHT2LJAQYNNWITRLWJUGXKTCJA/action/citation_signature","submit_replication":"https://pith.science/pith/NHT2LJAQYNNWITRLWJUGXKTCJA/action/replication_record"}},"created_at":"2026-05-18T00:01:05.507736+00:00","updated_at":"2026-05-18T00:01:05.507736+00:00"}