{"as_of":"2026-08-11T09:16:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9c9aa0c6d9ef939394564f44ca3125fd663ea0533c7a7bf797c444c583753d3a","coverage":[{"denominator":53,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":53,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T19:17:00.000556Z","state":"measured"},{"denominator":53,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":53,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2502.06870/citation-record","integrity":"/paper/2502.06870/integrity","json":"/paper/2502.06870/citation-record.json","paper":"/paper/2502.06870"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:16:59.805773Z","title":", \" * write output.state after.block = add.period write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.805773Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:9c39b814ec1d77766c5a0626d36d660f8269b4c5610661fc4623d50ddc625123","observation_id":"67eadfc1-353f-4e09-9670-93d42c5f5fc6","resolution":{"observed_at":"2026-08-08T19:16:59.805773Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:16:59.810548Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.810548Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:e2c1e3e286df600e3e2c068c7b2599b8717ab5695a4c9d53d8df4a19ee47378a","observation_id":"dabeee8b-d655-47e5-80e2-7388c67db5d2","resolution":{"observed_at":"2026-08-08T19:16:59.810548Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:17:00.572019Z","title":null,"venue":null,"work_id":"62f364a9-5c1f-4b43-9cd5-50bfcb19b6f3","year":2020},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.814514Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:6777b8dfae495a778d2801e751262cc5ae0d9dc25f84d1d7351b6ab848dc72a5","observation_id":"2b09fe6c-5a37-41d6-8374-bd1bc4264a14","resolution":{"observed_at":"2026-08-08T19:17:00.575480Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:17:00.561518Z","title":null,"venue":null,"work_id":"7c242318-36a8-4cb0-994b-b888d9f23df7","year":2023},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.818521Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:c150af126093991bdeb156b380148a49fd78a360708ed8ef5d040f84c2e7a547","observation_id":"d418ecb9-00bb-4a82-8631-5e174bbc3a11","resolution":{"observed_at":"2026-08-08T19:17:00.564804Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:17:00.550670Z","title":null,"venue":null,"work_id":"90ee1c8d-79bc-48dd-b7f9-c5231b7ff825","year":2022},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.822243Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:7c498d02945b1b32cc67373a66f6130831f0f63a134f947fab299d35823d0562","observation_id":"ead4fc64-7229-48f6-a78b-67caab4246ad","resolution":{"observed_at":"2026-08-08T19:17:00.554248Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:17:00.539488Z","title":"S.; and Guo, C","venue":null,"work_id":"4a9ad024-8bc9-4df1-a311-c9f88a979543","year":2019},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.826095Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:87b48a1251e49b448ae70e7296d3359ffbfee766c0e154a833b01ef54e4a53a5","observation_id":"23bd34ff-8de9-40fe-8e35-479e6870417c","resolution":{"observed_at":"2026-08-08T19:17:00.542875Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:17:00.528836Z","title":"S.; and Chen, G","venue":null,"work_id":"f036f93d-6b21-45cd-b140-78615d3bf9ca","year":2017},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.829731Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:82e2bc448e8bcafd0822caa72ef50f746d56259c0b44a9d9ccb1467193f4c9b7","observation_id":"2cb3b31b-e81f-4b7d-bdc0-82bcc5e5ae23","resolution":{"observed_at":"2026-08-08T19:17:00.532353Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:17:00.517276Z","title":null,"venue":null,"work_id":"c1a7e4d9-df4c-4f76-b113-da6ad5414c91","year":2018},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.833388Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:fc6c1289318872d60257ad71e5a1126c143ae42c68294e36d039dd72955519eb","observation_id":"be19d604-c3b5-4420-95a1-4e2d298e649d","resolution":{"observed_at":"2026-08-08T19:17:00.521652Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:17:00.506754Z","title":"K.; and Ellison, R","venue":null,"work_id":"c591fba8-e5ab-4868-82ab-49f97f80d0be","year":2021},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.837321Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:eab0f0531bd2235163106c1992dbd1edc3ddaebb2e063fad5d3d070783f293f6","observation_id":"2be86e1e-e69c-409f-9c24-b1377aa14e83","resolution":{"observed_at":"2026-08-08T19:17:00.510225Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:17:00.495026Z","title":null,"venue":null,"work_id":"ce4d62ba-185c-46fa-8ac9-b0f274897ad4","year":2021},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.840859Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:c197655ff9c607b7ce65c71a2c8699550c2d314dff905953d3f6d66e753e3bac","observation_id":"16411ada-5207-48fa-89be-f631663883e1","resolution":{"observed_at":"2026-08-08T19:17:00.499178Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:16:59.844122Z","title":"S.; and Bao, H","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.844122Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:60629019fcbe8b70c841b8bff95f7482d2ce17653c17c9a8f1766ef049f4ba61","observation_id":"65cd6a38-6e09-4e1d-9aad-3e552ca3db33","resolution":{"observed_at":"2026-08-08T19:16:59.844122Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:17:00.477958Z","title":null,"venue":null,"work_id":"4bdf2689-a2c4-4b85-ba75-7fde1fdaf6da","year":2021},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.848033Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:c3271747d7697c85d836f5ed915afe1b221771a4c66cc521a529fe68032920e8","observation_id":"08a275e4-2fa3-4ca5-a31f-b09e3a6361c4","resolution":{"observed_at":"2026-08-08T19:17:00.481303Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:17:00.467831Z","title":null,"venue":null,"work_id":"59a558f1-7462-47d6-b6c4-658e861d054a","year":2020},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.851497Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:97c9b0b6e9b7f218b6fbcdd36752f06bfbdc7efbea49140434883dbeab03b0cd","observation_id":"a7db29f7-dc83-4b62-87db-7f8c464e0e48","resolution":{"observed_at":"2026-08-08T19:17:00.471316Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:17:00.456321Z","title":null,"venue":null,"work_id":"aec4c595-0d32-45f8-84b5-477feea1be48","year":2016},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.854828Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:f1721d8bfc8900cb144359f8db8b144cef5fe1f7181615791493264dd110410a","observation_id":"aa9a7ae3-33c4-4e82-86ae-480dad0a47a7","resolution":{"observed_at":"2026-08-08T19:17:00.460263Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:17:00.444294Z","title":null,"venue":null,"work_id":"1c3deb96-fe95-4ed4-9574-d12fecaccb96","year":2023},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.858952Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:fae08b659713b343185c8988eed95afefde417be9d274c9e8d8e0e2d2e474b43","observation_id":"a96f1699-9ead-46d9-b32e-2242b0625e19","resolution":{"observed_at":"2026-08-08T19:17:00.448961Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:17:00.433231Z","title":"L.; Ying, Z.; and Leskovec, J","venue":null,"work_id":"ae204adc-213c-42a8-9bb2-cee769c91841","year":2017},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.862322Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:8eea04e42ba6b8b99d6bd9da0abbec84e46683ee598ade367dd39320e15562f9","observation_id":"b1e1fbea-afcc-4432-b922-1cc35078e6e6","resolution":{"observed_at":"2026-08-08T19:17:00.437009Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:17:00.421632Z","title":null,"venue":null,"work_id":"22e8443c-0cf2-46bf-b8f3-d126463fe8e2","year":2023},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.865512Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:97688e65d8a1beb44cc595b76730a38fd7b109787fe9fff8ceadbce2631dd66c","observation_id":"07406984-d4a7-4482-ad94-b4d3c78998c2","resolution":{"observed_at":"2026-08-08T19:17:00.425552Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:17:00.410954Z","title":null,"venue":null,"work_id":"b8c796c2-ff91-420d-83f8-eec30615b5aa","year":2022},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.869491Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:2029e4318c6314f0a178d5b6be54da3e267b439fa97b076812f9bbf7b8c9fc1a","observation_id":"d31a6e76-d060-46b6-bf0e-806a3ad9a576","resolution":{"observed_at":"2026-08-08T19:17:00.414441Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:17:00.400338Z","title":null,"venue":null,"work_id":"ad395212-3252-4a93-8ba5-772c3b2d3e2f","year":2020},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.872847Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:6c5d6909333353cbbcba2ad22aa28095e9d150e7763a220ae35287967596c9bb","observation_id":"9ea5fa65-5801-463e-8430-8a1764cf86f7","resolution":{"observed_at":"2026-08-08T19:17:00.403926Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:16:59.877680Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.877680Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:2e9eb445b0517fe37ffdace80671951226b164fc119a46d49ef8415560c0b4e6","observation_id":"e52d55da-cc3c-4c02-a1e6-78e86837321b","resolution":{"observed_at":"2026-08-08T19:16:59.877680Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:17:00.382260Z","title":"X.; and Wang, J","venue":null,"work_id":"a0efd17f-4605-4251-ba41-b544dfe952da","year":2023},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.881293Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:a3d7d935efeec2ff13a47ff49a149057b7fddbc3ae4b53c92a4f4beb938f452b","observation_id":"8bf58007-7697-445b-aeb3-3feb81b42461","resolution":{"observed_at":"2026-08-08T19:17:00.386396Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2308.12899","last_updated":"2024-03-07T16:22:21Z","snapshot_observed_at":"2026-08-03T14:57:08.137613Z","submitted_at":"2023-08-24T16:20:00Z","title":"Unified Data Management and Comprehensive Performance Evaluation for Urban Spatial-Temporal Prediction [Experiment, Analysis & Benchmark]","version":3},"cited_work":{"arxiv_id":"2308.12899","doi":null,"metadata_source":"pith","pith_arxiv_id":"2308.12899","snapshot_observed_at":"2026-08-08T19:17:00.062217Z","title":"Unified Data Management and Comprehensive Performance Evaluation for Urban Spatial-Temporal Prediction [Experiment, Analysis & Benchmark]","venue":"cs.LG","work_id":"2a71474b-b25e-4334-a768-31f94bb5576d","year":2023},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.884722Z"},"links":{"cited_paper":"/paper/2308.12899","citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:8b3960e10df7d038020a2d4d8957e22049b784c310c0f90518640bc55ea68e00","observation_id":"34db30e8-cc1a-4694-a53f-92814c7081e3","resolution":{"observed_at":"2026-08-08T19:17:00.066493Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:17:00.372214Z","title":null,"venue":null,"work_id":"6f2d8ca8-d844-4ebf-8171-24361aaf9d1f","year":2023},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.889574Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:ac485c61d1d3ca0bc2f5f5a2b707367566cbc1c206817e04978a63228535ce1b","observation_id":"aad542ff-d80d-4ca0-88e7-edde820a05d4","resolution":{"observed_at":"2026-08-08T19:17:00.375648Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:17:00.361563Z","title":"X.; Wang, J.; and Jiang, J","venue":null,"work_id":"5c4fb71c-45bd-4e4f-bc15-bcfafb26f5e9","year":2023},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.893119Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:f63fb1c29baaa93cbaa0870fd005b63cc8e3e040bd2eda8a752653c090c4b104","observation_id":"dbbdfee1-7f2f-4d94-a3f9-6869cc78952f","resolution":{"observed_at":"2026-08-08T19:17:00.365358Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:17:00.350115Z","title":null,"venue":null,"work_id":"14dd7b13-b6b4-4705-b465-d92a4c0bdadb","year":2021},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.896414Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:4f745ab26757d38e7db81bb07d10ed16732d147b3c76f6acb8e9ad9f63ceacae","observation_id":"b9268e36-1610-43f2-afcc-2bd16db45ac8","resolution":{"observed_at":"2026-08-08T19:17:00.353969Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:17:00.339383Z","title":"S.; and Wei, W","venue":null,"work_id":"eb1a126c-4fc9-4de3-bba2-1d00fd422bc7","year":2018},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.899666Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:2bc4a1839a73301cdfda8756520668623373b181bc3e52a6630bdd25b14aa617","observation_id":"5322d18e-783b-4865-b347-b5c9281386a3","resolution":{"observed_at":"2026-08-08T19:17:00.343231Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:17:00.328102Z","title":null,"venue":null,"work_id":"cc88db2f-c421-4928-995e-a7fbb09a1755","year":2018},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.903105Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:4c911d8a2d0920f60464e977dc4ec67fc1c31db203f55eb7409b2f5adb817358","observation_id":"593f5ea0-e8ec-4de0-9c1c-159eb5172891","resolution":{"observed_at":"2026-08-08T19:17:00.331499Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:17:00.316673Z","title":"S.; and Lin, Y","venue":null,"work_id":"47ab7bea-c7bb-4659-adc1-90ea931dffc8","year":2023},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.906764Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:ded5b923868c6c1d641d6a622b61d9cb04c2bd2acbf860deb75780fc3cc8879e","observation_id":"53e28409-ab95-4ae3-9961-e41fc7348317","resolution":{"observed_at":"2026-08-08T19:17:00.320766Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:17:00.305542Z","title":null,"venue":null,"work_id":"78776a63-78c3-471f-b0c1-897e4f182b1e","year":2022},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.910607Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:986a6de37f3f41b1200bd5821a860930c942cceaa4de65da38f929c108120b92","observation_id":"9a374ac1-e6fa-4929-a9d5-afd3c29df411","resolution":{"observed_at":"2026-08-08T19:17:00.309403Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:17:00.294686Z","title":null,"venue":null,"work_id":"1eb1b2aa-28a1-4bb8-8c20-017eb0345089","year":2024},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.914752Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:ce250ecf9c3a44cf015d47ffbf4b6be939e28aa8c33b12eba106128cf85f4bc7","observation_id":"d35095f5-bf51-4ff1-a6a4-7a9a677522db","resolution":{"observed_at":"2026-08-08T19:17:00.298192Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:17:00.284168Z","title":null,"venue":null,"work_id":"2d610c29-7f17-412e-9f64-3de9840b14b3","year":2022},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.918633Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:080eb3a5ea66f4f94a779ac694ba3c2e8f523180b18b95767f44864f2cd14e25","observation_id":"5c5055be-2212-4847-87f6-0f0b33936bde","resolution":{"observed_at":"2026-08-08T19:17:00.287806Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:17:00.273285Z","title":null,"venue":null,"work_id":"f729558d-a624-4cb6-bd55-3a967adc41e3","year":2014},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.922103Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:21f201e3423ceb3dd1c8eed3e2e44920346b89bf978177a865a3e6e40356e14b","observation_id":"08620af9-7374-45f1-81e1-8ad3a5caacf5","resolution":{"observed_at":"2026-08-08T19:17:00.277287Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:17:00.262961Z","title":null,"venue":null,"work_id":"a3aa1cde-16cf-4e93-8ad2-29b84396ca61","year":2021},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.925684Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:8a07673b532326b481ca756d8b59cf6984366881cb7c313958048ccdd811f7b3","observation_id":"91db817a-baa9-4379-aae3-71719002dbd1","resolution":{"observed_at":"2026-08-08T19:17:00.266651Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:17:00.252148Z","title":null,"venue":null,"work_id":"087d6d16-2b3d-47b9-b035-ca7ebd8110cb","year":2015},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.929607Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:1a5aaa106c1216c325d28fdb97e081c73142150ff4c91dca5c98b3bf54d20fb8","observation_id":"4f83b74a-642b-4ab3-9bf8-3d38f628adc1","resolution":{"observed_at":"2026-08-08T19:17:00.256291Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:16:59.933157Z","title":null,"venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.933157Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:024b0cbe0c0d9f3cdf5fcf66a8dfa8c4f9335979f09957bd6f56917d96a0cfd2","observation_id":"a9cbef6b-068b-4fcc-a060-f791f746496f","resolution":{"observed_at":"2026-08-08T19:16:59.933157Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:17:00.233553Z","title":"N.; Kaiser, L.; and Polosukhin, I","venue":null,"work_id":"bded988b-a53c-4219-b815-cec952cf3f2e","year":2017},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.937096Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:9988bf0f02deb84f1199b674b39da25bf14283ceed5f79f83323cf3af78ce4dd","observation_id":"a4766794-64c9-491e-86d1-2556dbfb81f4","resolution":{"observed_at":"2026-08-08T19:17:00.237024Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:17:00.223044Z","title":null,"venue":null,"work_id":"9d3215ba-2d69-4c07-ae8f-aca90bf19723","year":2018},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.940614Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:4d80d678734e4643f39a8edf8a26160dbe34bc718208734588d00f8fe037fc26","observation_id":"f80dbdf6-5f5f-4d42-a974-d8a85548b43a","resolution":{"observed_at":"2026-08-08T19:17:00.226518Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:17:00.211329Z","title":null,"venue":null,"work_id":"09d1c00e-05aa-44a1-afe7-14315c0b1b89","year":2018},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.944342Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:53fba48f4d0bbbdedd4cafabd9acad7e25f45bb7d432f23d5655071d16fbcc54","observation_id":"3980b1e8-e4c1-4c0b-b762-9de6c90d9a3c","resolution":{"observed_at":"2026-08-08T19:17:00.215668Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:16:59.948560Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.948560Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:369108eb06b4596c1dc26c2bb29baa105a40632f3d94844cda2839aa70be45cd","observation_id":"e963a4e6-cd10-4076-8e98-2ed150ee3146","resolution":{"observed_at":"2026-08-08T19:16:59.948560Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:17:00.194585Z","title":null,"venue":null,"work_id":"7c8407cc-bd4d-457f-a1c4-6a2e61024af1","year":2021},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.951963Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:f7b34775ab2c33c689bcd596516a6086b820bab0a51defee2de1a54b4fa916de","observation_id":"a54eb685-1bd8-4441-bc1c-8c4cc07a44b1","resolution":{"observed_at":"2026-08-08T19:17:00.198015Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:17:00.183266Z","title":"X.; Peng, F.; and Lin, X","venue":null,"work_id":"d0b30259-9891-4ff8-be03-e55b9759f086","year":2019},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.955872Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:98b9cf3e9454150db125b17cf9509698cc95d4233af4b3f7cfb8f8fd4a9aeb22","observation_id":"8668a339-ba26-4507-ae22-b0794cd7ca1c","resolution":{"observed_at":"2026-08-08T19:17:00.187498Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:17:00.173196Z","title":null,"venue":null,"work_id":"80faf669-d0cc-47b4-bb88-8edcd71735ba","year":2019},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.960207Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:b8353c7f1ce5e639a4a8357aabf8a7769b82a35fce7877a80cb349a37b4954eb","observation_id":"2552acd7-e559-4ec0-8abd-da8732a2d6bf","resolution":{"observed_at":"2026-08-08T19:17:00.176599Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:17:00.163094Z","title":"X.; and Jin, Y","venue":null,"work_id":"18e2acbd-3181-4611-b93d-2bc4a6e7d780","year":2019},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.963708Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:0274ff49a7cfaa9e7205157be48c0010f500a137550fe884b95b5b3fc3f08778","observation_id":"b234fc55-c1e0-4c0f-b9e1-e219eaa3bedd","resolution":{"observed_at":"2026-08-08T19:17:00.166548Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:17:00.152166Z","title":"X.; Wang, J.; and Pan, D","venue":null,"work_id":"e74e1aab-ff56-43af-8085-3c44d4afcc95","year":2020},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.967447Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:b376522c6703133c0f189355f3c5060947d2a66eaf1d0eb4df94df841ee6b7e3","observation_id":"cb9e8cb9-3f81-4e20-9ac3-c8ab3f38426e","resolution":{"observed_at":"2026-08-08T19:17:00.155683Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:17:00.140068Z","title":null,"venue":null,"work_id":"ca598f1e-5d31-4d10-8280-16e1ee670e55","year":2020},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.971458Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:f55b511adab7955ecfd89376af2fd29473228c131f964717ab8cb45d58d9000d","observation_id":"a9dec2e1-2a5a-4752-884e-800845788f67","resolution":{"observed_at":"2026-08-08T19:17:00.144174Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:17:00.129809Z","title":null,"venue":null,"work_id":"ed8d9577-c798-4b7b-be47-6a4fb59f95a0","year":2019},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.974879Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:adb155f2845a14943ac5768797e8603946ea4d3e78e561318ae494c9bd8a4550","observation_id":"719fdb45-44da-4aa5-a859-a0122fdf0304","resolution":{"observed_at":"2026-08-08T19:17:00.133447Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:17:00.118822Z","title":"B.; Guo, C.; Hu, J.; Tang, J.; and Yang, B","venue":null,"work_id":"109d452b-d5d1-41fd-80a5-1eff7db9f63e","year":2021},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.978429Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:08cc72336d06078cef32e82afd7b9c193f6a168be1c4c9bbad9e6ac4bb9f0eb9","observation_id":"f98fa44f-d4f8-48e3-a2ec-3f4b0d2791dc","resolution":{"observed_at":"2026-08-08T19:17:00.122661Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:17:00.107204Z","title":"B.; Guo, C.; Hu, J.; Yang, B.; Tang, J.; and Jensen, C","venue":null,"work_id":"26edbdde-07f5-4131-ab62-509a92faf489","year":2022},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.982128Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:a7edcc6db1622f0ddffd5b337aeb321a4045e0fcf4082bd736a5693b8fe05884","observation_id":"55b1eae3-5a78-4e71-90f0-98309d6dea9d","resolution":{"observed_at":"2026-08-08T19:17:00.111357Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:17:00.096124Z","title":null,"venue":null,"work_id":"eaf14132-1761-4230-a17a-d5bd542a6004","year":2017},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.985766Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:71aadd160a7025a895351dc68ecf05203fc95562c9906d74aa89151adb27a4b5","observation_id":"cf976865-7231-4ff9-b93c-4389491c0a21","resolution":{"observed_at":"2026-08-08T19:17:00.099875Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.10524","last_updated":"2025-01-15T09:17:01Z","snapshot_observed_at":"2026-08-01T11:26:06.178732Z","submitted_at":"2024-10-14T14:04:36Z","title":"Get Rid of Isolation: A Continuous Multi-task Spatio-Temporal Learning Framework","version":2},"cited_work":{"arxiv_id":"2410.10524","doi":null,"metadata_source":"pith","pith_arxiv_id":"2410.10524","snapshot_observed_at":"2026-08-08T19:17:00.043505Z","title":"Get Rid of Isolation: A Continuous Multi-task Spatio-Temporal Learning Framework","venue":"cs.LG","work_id":"06a131f6-3d83-442e-afa0-872fa6cf412f","year":2024},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.989276Z"},"links":{"cited_paper":"/paper/2410.10524","citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:427e99060ee6b1ab72671ae1f16f55c839d00c6fae0ae806b383acbabf5fdddc","observation_id":"96db7e1d-6f2a-401d-bf98-adf22e1a6ac0","resolution":{"observed_at":"2026-08-08T19:17:00.049864Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.00953","last_updated":"2024-12-01T20:10:55Z","snapshot_observed_at":"2026-07-06T19:59:47.022132Z","submitted_at":"2024-12-01T20:10:55Z","title":"BIGCity: A Universal Spatiotemporal Model for Unified Trajectory and Traffic State Data Analysis","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.00953","snapshot_observed_at":"2026-08-08T19:16:59.993154Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.993154Z"},"links":{"cited_paper":"/paper/2412.00953","citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:9139ef1bfb3f35fbb0d2a0a9ee24ed9d6fa969717afe13236bfa03cb98b2e7a0","observation_id":"132c6fbf-4664-4e11-9bc1-fbe7f1905430","resolution":{"observed_at":"2026-08-08T19:16:59.993154Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:17:00.085581Z","title":null,"venue":null,"work_id":"063e604b-0094-4e8f-b6c9-123d8f7a7475","year":2022},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-08T19:16:59.997156Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:428725758bf7b0916f1b1c58b226c6f12dffc5d0bd9ebd6c41d3d4ca69143424","observation_id":"634fa6c3-e766-4003-90a1-1a8ba9832560","resolution":{"observed_at":"2026-08-08T19:17:00.089137Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:17:00.073897Z","title":null,"venue":null,"work_id":"968c8ff0-40c6-4e1a-851d-347a4c64941e","year":2024},"citing_paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning","version":1},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-08T19:17:00.000556Z"},"links":{"citing_paper":"/paper/2502.06870"},"observation_digest":"sha256:0680aa158d6b5cbaeb4dd07d0f0e233fbd0c6ff991bf41a354d4ad299f150128","observation_id":"4bb27295-5dc5-4876-b4c1-187cb0fc65c8","resolution":{"observed_at":"2026-08-08T19:17:00.077695Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2502.06870","last_updated":"2025-02-08T06:36:54Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-10T20:35:30.037199Z","submitted_at":"2025-02-08T06:36:54Z","title":"Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning"},"reference_resolution":{"displayed":53,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":37,"verified_exact":2,"verified_fuzzy":14},"total_outbound_references":53},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 0 inbound Pith citation observations for arXiv:2502.06870."}