{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:SUSRXEPNVJLE47YHDGHCUWJCZ5","short_pith_number":"pith:SUSRXEPN","schema_version":"1.0","canonical_sha256":"95251b91edaa564e7f07198e2a5922cf789e86f195b2f0fd64e612fc731acf5a","source":{"kind":"arxiv","id":"1811.01315","version":2},"attestation_state":"computed","paper":{"title":"Modeling Stated Preference for Mobility-on-Demand Transit: A Comparison of Machine Learning and Logit Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.AP","stat.ML"],"primary_cat":"cs.LG","authors_text":"Alan Yu, Pascal Van Hentenryck, Xiang Yan, Xilei Zhao","submitted_at":"2018-11-04T02:55:49Z","abstract_excerpt":"Logit models are usually applied when studying individual travel behavior, i.e., to predict travel mode choice and to gain behavioral insights on traveler preferences. Recently, some studies have applied machine learning to model travel mode choice and reported higher out-of-sample predictive accuracy than traditional logit models (e.g., multinomial logit). However, little research focuses on comparing the interpretability of machine learning with logit models. In other words, how to draw behavioral insights from the high-performance \"black-box\" machine-learning models remains largely unsolved"},"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":"1811.01315","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-11-04T02:55:49Z","cross_cats_sorted":["cs.AI","stat.AP","stat.ML"],"title_canon_sha256":"6acf1099af1c585f8597e6d9a61cf403ddc0f81c76e190cbea86a4d0af68958c","abstract_canon_sha256":"5a9a82df338f6d88fa90cde5b4545791516a788b52b6030051a5e7250cb6e919"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:49:40.678700Z","signature_b64":"76rh9GfSOyc49ky9KJD60CxuAUE4yAlh+K3JlTVi5hi3fKa+zXUOQGSY7XE4OwZqYSJyhdsxYVo78pq3G8JoAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"95251b91edaa564e7f07198e2a5922cf789e86f195b2f0fd64e612fc731acf5a","last_reissued_at":"2026-05-17T23:49:40.678181Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:49:40.678181Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Modeling Stated Preference for Mobility-on-Demand Transit: A Comparison of Machine Learning and Logit Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.AP","stat.ML"],"primary_cat":"cs.LG","authors_text":"Alan Yu, Pascal Van Hentenryck, Xiang Yan, Xilei Zhao","submitted_at":"2018-11-04T02:55:49Z","abstract_excerpt":"Logit models are usually applied when studying individual travel behavior, i.e., to predict travel mode choice and to gain behavioral insights on traveler preferences. Recently, some studies have applied machine learning to model travel mode choice and reported higher out-of-sample predictive accuracy than traditional logit models (e.g., multinomial logit). However, little research focuses on comparing the interpretability of machine learning with logit models. In other words, how to draw behavioral insights from the high-performance \"black-box\" machine-learning models remains largely unsolved"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.01315","kind":"arxiv","version":2},"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":"1811.01315","created_at":"2026-05-17T23:49:40.678260+00:00"},{"alias_kind":"arxiv_version","alias_value":"1811.01315v2","created_at":"2026-05-17T23:49:40.678260+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.01315","created_at":"2026-05-17T23:49:40.678260+00:00"},{"alias_kind":"pith_short_12","alias_value":"SUSRXEPNVJLE","created_at":"2026-05-18T12:32:53.628368+00:00"},{"alias_kind":"pith_short_16","alias_value":"SUSRXEPNVJLE47YH","created_at":"2026-05-18T12:32:53.628368+00:00"},{"alias_kind":"pith_short_8","alias_value":"SUSRXEPN","created_at":"2026-05-18T12:32:53.628368+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/SUSRXEPNVJLE47YHDGHCUWJCZ5","json":"https://pith.science/pith/SUSRXEPNVJLE47YHDGHCUWJCZ5.json","graph_json":"https://pith.science/api/pith-number/SUSRXEPNVJLE47YHDGHCUWJCZ5/graph.json","events_json":"https://pith.science/api/pith-number/SUSRXEPNVJLE47YHDGHCUWJCZ5/events.json","paper":"https://pith.science/paper/SUSRXEPN"},"agent_actions":{"view_html":"https://pith.science/pith/SUSRXEPNVJLE47YHDGHCUWJCZ5","download_json":"https://pith.science/pith/SUSRXEPNVJLE47YHDGHCUWJCZ5.json","view_paper":"https://pith.science/paper/SUSRXEPN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1811.01315&json=true","fetch_graph":"https://pith.science/api/pith-number/SUSRXEPNVJLE47YHDGHCUWJCZ5/graph.json","fetch_events":"https://pith.science/api/pith-number/SUSRXEPNVJLE47YHDGHCUWJCZ5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SUSRXEPNVJLE47YHDGHCUWJCZ5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SUSRXEPNVJLE47YHDGHCUWJCZ5/action/storage_attestation","attest_author":"https://pith.science/pith/SUSRXEPNVJLE47YHDGHCUWJCZ5/action/author_attestation","sign_citation":"https://pith.science/pith/SUSRXEPNVJLE47YHDGHCUWJCZ5/action/citation_signature","submit_replication":"https://pith.science/pith/SUSRXEPNVJLE47YHDGHCUWJCZ5/action/replication_record"}},"created_at":"2026-05-17T23:49:40.678260+00:00","updated_at":"2026-05-17T23:49:40.678260+00:00"}