{"as_of":"2026-08-10T18:44:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f1b05dfb435ec29668ee5365ff2baff8ee6e02afc48e9e54840723af5c2ae3bb","coverage":[{"denominator":30,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":30,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T11:12:53.301259Z","state":"measured"},{"denominator":30,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":30,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+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/2509.03118/citation-record","integrity":"/paper/2509.03118/integrity","json":"/paper/2509.03118/citation-record.json","paper":"/paper/2509.03118"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:12:55.704504Z","title":"Investigating the role of green transport, environmental taxes and expenditures in mitigating the transport co2 emissions,","venue":null,"work_id":"a6030166-81ab-42a7-8078-5d7467b20afb","year":2023},"citing_paper":{"arxiv_id":"2509.03118","last_updated":"2025-09-03T08:20:06Z","snapshot_observed_at":"2026-08-10T04:17:52.981746Z","submitted_at":"2025-09-03T08:20:06Z","title":"A Hierarchical Deep Reinforcement Learning Framework for Traffic Signal Control with Predictable Cycle Planning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T11:12:50.465894Z"},"links":{"citing_paper":"/paper/2509.03118"},"observation_digest":"sha256:5ff4b3f7240503461eda9b384815b67b4871e8358901b1f605a0bec4946059ae","observation_id":"7a8d3691-0b96-494b-8f8c-33c1dc028f38","resolution":{"observed_at":"2026-08-05T11:12:55.707446Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T11:12:55.695454Z","title":"Environmen- tal generation scheduling considering air pollution control technologies and weather effects,","venue":null,"work_id":"775acd78-4d2e-4c4d-8387-32a6b19d049d","year":2016},"citing_paper":{"arxiv_id":"2509.03118","last_updated":"2025-09-03T08:20:06Z","snapshot_observed_at":"2026-08-10T04:17:52.981746Z","submitted_at":"2025-09-03T08:20:06Z","title":"A Hierarchical Deep Reinforcement Learning Framework for Traffic Signal Control with Predictable Cycle Planning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T11:12:50.552714Z"},"links":{"citing_paper":"/paper/2509.03118"},"observation_digest":"sha256:c14a454cb7a9dd8e075cc4b31b062bfff8c39b0a8bf4ef8bbb7dbb68db5349f0","observation_id":"9d6b9b83-eb20-474a-8b50-84eda24139fe","resolution":{"observed_at":"2026-08-05T11:12:55.698752Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T11:12:55.685763Z","title":"Experiences with adaptive signal control in germany,","venue":null,"work_id":"03941c9b-3991-46fc-9b46-629ac471de7d","year":2013},"citing_paper":{"arxiv_id":"2509.03118","last_updated":"2025-09-03T08:20:06Z","snapshot_observed_at":"2026-08-10T04:17:52.981746Z","submitted_at":"2025-09-03T08:20:06Z","title":"A Hierarchical Deep Reinforcement Learning Framework for Traffic Signal Control with Predictable Cycle Planning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T11:12:50.628735Z"},"links":{"citing_paper":"/paper/2509.03118"},"observation_digest":"sha256:817926c34a54ee2cdc45a500f8800a93826d893560922d1f98f1adaf489221cf","observation_id":"b5f22816-3a42-46db-8b6c-8cd0cc853acc","resolution":{"observed_at":"2026-08-05T11:12:55.688918Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.08117","last_updated":"2020-01-16T16:29:35Z","snapshot_observed_at":"2026-08-05T07:12:03.305016Z","submitted_at":"2019-04-17T08:07:29Z","title":"A Survey on Traffic Signal Control Methods","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.08117","snapshot_observed_at":"2026-08-05T11:12:50.702882Z","title":"A survey on traffic signal control methods,","venue":null,"work_id":null,"year":1904},"citing_paper":{"arxiv_id":"2509.03118","last_updated":"2025-09-03T08:20:06Z","snapshot_observed_at":"2026-08-10T04:17:52.981746Z","submitted_at":"2025-09-03T08:20:06Z","title":"A Hierarchical Deep Reinforcement Learning Framework for Traffic Signal Control with Predictable Cycle Planning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T11:12:50.702882Z"},"links":{"cited_paper":"/paper/1904.08117","citing_paper":"/paper/2509.03118"},"observation_digest":"sha256:939d808d583ad22d97a53f6fca8d4e6a1aa71934f66a41f9a9fd1696dfc184f2","observation_id":"0f0f9a0f-208f-4d9e-9e86-35042f2ffd77","resolution":{"observed_at":"2026-08-05T11:12:50.702882Z","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":"2026-08-05T11:12:55.677494Z","title":"Deep reinforcement learning for intelligent transportation systems: A survey,","venue":null,"work_id":"eecabfce-a149-49be-b419-0607a2b03f4d","year":2020},"citing_paper":{"arxiv_id":"2509.03118","last_updated":"2025-09-03T08:20:06Z","snapshot_observed_at":"2026-08-10T04:17:52.981746Z","submitted_at":"2025-09-03T08:20:06Z","title":"A Hierarchical Deep Reinforcement Learning Framework for Traffic Signal Control with Predictable Cycle Planning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T11:12:50.762040Z"},"links":{"citing_paper":"/paper/2509.03118"},"observation_digest":"sha256:fbe3beb49b9b1abf2404bbcf9e0db5cb1e26c0595f78da1d1344500fc310eb42","observation_id":"f9649a4d-b4b0-437b-a9ea-ff9488f7260f","resolution":{"observed_at":"2026-08-05T11:12:55.680088Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2112.02336","last_updated":"2021-12-04T13:49:58Z","snapshot_observed_at":"2026-08-09T17:33:26.795650Z","submitted_at":"2021-12-04T13:49:58Z","title":"Efficient Pressure: Improving efficiency for signalized intersections","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.02336","snapshot_observed_at":"2026-08-05T11:12:50.851633Z","title":"Efficient pressure: Improving efficiency for signalized intersections,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.03118","last_updated":"2025-09-03T08:20:06Z","snapshot_observed_at":"2026-08-10T04:17:52.981746Z","submitted_at":"2025-09-03T08:20:06Z","title":"A Hierarchical Deep Reinforcement Learning Framework for Traffic Signal Control with Predictable Cycle Planning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T11:12:50.851633Z"},"links":{"cited_paper":"/paper/2112.02336","citing_paper":"/paper/2509.03118"},"observation_digest":"sha256:abbab294a4c850ce8dce92cff0207c26e3b61f8087cd05c11b45eac69959b498","observation_id":"a5c43140-5d45-4638-91bf-1c20f6deaaa4","resolution":{"observed_at":"2026-08-05T11:12:50.851633Z","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":"2026-08-05T11:12:55.670018Z","title":"Matlit: Mat-based cooperative reinforcement learning for urban traffic signal control,","venue":null,"work_id":"3e296315-cc91-4310-bd3f-d3561b398a4b","year":2025},"citing_paper":{"arxiv_id":"2509.03118","last_updated":"2025-09-03T08:20:06Z","snapshot_observed_at":"2026-08-10T04:17:52.981746Z","submitted_at":"2025-09-03T08:20:06Z","title":"A Hierarchical Deep Reinforcement Learning Framework for Traffic Signal Control with Predictable Cycle Planning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T11:12:50.968246Z"},"links":{"citing_paper":"/paper/2509.03118"},"observation_digest":"sha256:62564cca30f3574de1842064e583cfdf0b8f17d82991697322554777f2b717a2","observation_id":"1429f43e-2740-4e51-a8b2-1d428e712550","resolution":{"observed_at":"2026-08-05T11:12:55.672609Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T11:12:55.661617Z","title":"Large-scale traffic signal control using constrained network partition and adaptive deep reinforcement learning,","venue":null,"work_id":"bd13e788-b868-4976-98db-dc246fbca3d6","year":2024},"citing_paper":{"arxiv_id":"2509.03118","last_updated":"2025-09-03T08:20:06Z","snapshot_observed_at":"2026-08-10T04:17:52.981746Z","submitted_at":"2025-09-03T08:20:06Z","title":"A Hierarchical Deep Reinforcement Learning Framework for Traffic Signal Control with Predictable Cycle Planning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T11:12:51.033879Z"},"links":{"citing_paper":"/paper/2509.03118"},"observation_digest":"sha256:ce459da5432da1a6318d8c071d6097919f004b8395b98b7d16cb0a44d5142f55","observation_id":"9f518e34-190f-4730-ae11-112df2e8677c","resolution":{"observed_at":"2026-08-05T11:12:55.664362Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T11:12:55.653924Z","title":"Communication strategy on macro-and-micro traffic state in cooperative deep reinforcement learning for regional traffic signal control,","venue":null,"work_id":"900079b9-1b28-4ea3-87e8-3822f4b9ecf9","year":2025},"citing_paper":{"arxiv_id":"2509.03118","last_updated":"2025-09-03T08:20:06Z","snapshot_observed_at":"2026-08-10T04:17:52.981746Z","submitted_at":"2025-09-03T08:20:06Z","title":"A Hierarchical Deep Reinforcement Learning Framework for Traffic Signal Control with Predictable Cycle Planning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T11:12:51.144031Z"},"links":{"citing_paper":"/paper/2509.03118"},"observation_digest":"sha256:712bf25a86b3d30131fb7c459de7fe61e9f6ca7384368a48118c04169cd97004","observation_id":"bbf6e2a2-59cf-4324-b00f-4e103ce2d38a","resolution":{"observed_at":"2026-08-05T11:12:55.656690Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T11:12:55.646105Z","title":"Halight: Hierarchical deep reinforcement learning for cooperative arterial traffic signal control with cycle strategy,","venue":null,"work_id":"bd97d938-8d76-4359-b40d-05a9bc6ad412","year":2022},"citing_paper":{"arxiv_id":"2509.03118","last_updated":"2025-09-03T08:20:06Z","snapshot_observed_at":"2026-08-10T04:17:52.981746Z","submitted_at":"2025-09-03T08:20:06Z","title":"A Hierarchical Deep Reinforcement Learning Framework for Traffic Signal Control with Predictable Cycle Planning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T11:12:51.271703Z"},"links":{"citing_paper":"/paper/2509.03118"},"observation_digest":"sha256:1bde8896ec302f9b55e00fa739d1ad78960fb7a4699d291b1575c527dbf88b70","observation_id":"a5a15137-5756-4865-b78a-da84d87102d8","resolution":{"observed_at":"2026-08-05T11:12:55.648840Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T11:12:55.637121Z","title":"Multi-agent reinforcement learning: Independent vs. cooper- ative agents,","venue":null,"work_id":"6faa08b8-e8da-410e-b24d-34a696b2839a","year":1993},"citing_paper":{"arxiv_id":"2509.03118","last_updated":"2025-09-03T08:20:06Z","snapshot_observed_at":"2026-08-10T04:17:52.981746Z","submitted_at":"2025-09-03T08:20:06Z","title":"A Hierarchical Deep Reinforcement Learning Framework for Traffic Signal Control with Predictable Cycle Planning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T11:12:51.356589Z"},"links":{"citing_paper":"/paper/2509.03118"},"observation_digest":"sha256:36ef7beb585a69dffc5621ae017d73f98e69364ad6589f78b2f93a45b8c90a13","observation_id":"3dd4867d-71ef-4ad5-8ad3-5aa3a4ff540e","resolution":{"observed_at":"2026-08-05T11:12:55.640200Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T11:12:55.628259Z","title":"Presslight: Learning max pressure control to coordinate traffic signals in arterial network,","venue":null,"work_id":"546ed3a8-59cf-4a76-a9be-11564f94bbea","year":2019},"citing_paper":{"arxiv_id":"2509.03118","last_updated":"2025-09-03T08:20:06Z","snapshot_observed_at":"2026-08-10T04:17:52.981746Z","submitted_at":"2025-09-03T08:20:06Z","title":"A Hierarchical Deep Reinforcement Learning Framework for Traffic Signal Control with Predictable Cycle Planning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T11:12:51.513464Z"},"links":{"citing_paper":"/paper/2509.03118"},"observation_digest":"sha256:9f3c45bf8dd706019af57d34a2d5b27cc15a8f550ebb41ec972a0f3c43c11858","observation_id":"1553cdb6-4d29-4cca-a321-79f277d49a0e","resolution":{"observed_at":"2026-08-05T11:12:55.631393Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T11:12:55.619658Z","title":"Colight: Learning network-level cooper- ation for traffic signal control,","venue":null,"work_id":"475d0360-1827-4c7b-8f33-806bc03e131a","year":2019},"citing_paper":{"arxiv_id":"2509.03118","last_updated":"2025-09-03T08:20:06Z","snapshot_observed_at":"2026-08-10T04:17:52.981746Z","submitted_at":"2025-09-03T08:20:06Z","title":"A Hierarchical Deep Reinforcement Learning Framework for Traffic Signal Control with Predictable Cycle Planning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T11:12:51.594694Z"},"links":{"citing_paper":"/paper/2509.03118"},"observation_digest":"sha256:d490f8c6ae52adfc8879a7760a744d1beaebde5aa9092144806305c060b25003","observation_id":"6dc2df2b-c817-45d9-8ad5-4c277841b2a8","resolution":{"observed_at":"2026-08-05T11:12:55.622302Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T11:12:55.486382Z","title":"Large-scale traffic signal control by a nash deep q-network approach,","venue":null,"work_id":"1fc1d125-2241-46f8-a40a-09c9039d2c3a","year":2023},"citing_paper":{"arxiv_id":"2509.03118","last_updated":"2025-09-03T08:20:06Z","snapshot_observed_at":"2026-08-10T04:17:52.981746Z","submitted_at":"2025-09-03T08:20:06Z","title":"A Hierarchical Deep Reinforcement Learning Framework for Traffic Signal Control with Predictable Cycle Planning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T11:12:51.692983Z"},"links":{"citing_paper":"/paper/2509.03118"},"observation_digest":"sha256:2beee0779701c7ce4baffd625d6a0225e5d1be7f8413255f44c8426d9aa952c4","observation_id":"1028e560-12c3-4f41-9815-d324ef24d55f","resolution":{"observed_at":"2026-08-05T11:12:55.595319Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T11:12:55.393803Z","title":"Hierarchical deep reinforcement learning for continuous action control,","venue":null,"work_id":"a583974c-2707-4dda-987d-a8f0780eb33c","year":2018},"citing_paper":{"arxiv_id":"2509.03118","last_updated":"2025-09-03T08:20:06Z","snapshot_observed_at":"2026-08-10T04:17:52.981746Z","submitted_at":"2025-09-03T08:20:06Z","title":"A Hierarchical Deep Reinforcement Learning Framework for Traffic Signal Control with Predictable Cycle Planning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T11:12:51.803288Z"},"links":{"citing_paper":"/paper/2509.03118"},"observation_digest":"sha256:bb063192defd7cf495e5b360459886d4ccc296497f6b41d25a3c1af17be6c2c1","observation_id":"5e348093-3cef-43c8-b7a0-0a7f19d6c7d8","resolution":{"observed_at":"2026-08-05T11:12:55.436258Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T11:12:55.141723Z","title":"Feudal networks for hierarchical rein- forcement learning,","venue":null,"work_id":"6cf8c833-bd12-42f2-b36f-36957d0219db","year":2017},"citing_paper":{"arxiv_id":"2509.03118","last_updated":"2025-09-03T08:20:06Z","snapshot_observed_at":"2026-08-10T04:17:52.981746Z","submitted_at":"2025-09-03T08:20:06Z","title":"A Hierarchical Deep Reinforcement Learning Framework for Traffic Signal Control with Predictable Cycle Planning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T11:12:51.895765Z"},"links":{"citing_paper":"/paper/2509.03118"},"observation_digest":"sha256:07d7a5f3c2cca19b339b57bcfef48763c635f2bff660c4759640ff9e50b172cf","observation_id":"35d6eb67-72ce-4ce3-8578-1cc26d220f64","resolution":{"observed_at":"2026-08-05T11:12:55.260249Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T11:12:54.871134Z","title":"Hierarchically and cooperatively learning traffic signal control,","venue":null,"work_id":"6587b533-ca5e-4bc9-aab4-cecec5050c23","year":2021},"citing_paper":{"arxiv_id":"2509.03118","last_updated":"2025-09-03T08:20:06Z","snapshot_observed_at":"2026-08-10T04:17:52.981746Z","submitted_at":"2025-09-03T08:20:06Z","title":"A Hierarchical Deep Reinforcement Learning Framework for Traffic Signal Control with Predictable Cycle Planning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-05T11:12:51.994211Z"},"links":{"citing_paper":"/paper/2509.03118"},"observation_digest":"sha256:729b6133358f1f9b1a3814226b9579f065e1bb14b56bd5419b56ce4fe8bbb033","observation_id":"5c517fcb-bf04-4750-9fff-d365b6aa692e","resolution":{"observed_at":"2026-08-05T11:12:55.011669Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T11:12:52.054814Z","title":"The scoot on-line traffic signal optimisation technique,","venue":null,"work_id":null,"year":1982},"citing_paper":{"arxiv_id":"2509.03118","last_updated":"2025-09-03T08:20:06Z","snapshot_observed_at":"2026-08-10T04:17:52.981746Z","submitted_at":"2025-09-03T08:20:06Z","title":"A Hierarchical Deep Reinforcement Learning Framework for Traffic Signal Control with Predictable Cycle Planning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T11:12:52.054814Z"},"links":{"citing_paper":"/paper/2509.03118"},"observation_digest":"sha256:1ae8c3f5f56faa553a1e5fa7951e48822ccb94bbb68da97b8208efd81022fd1e","observation_id":"cfbab5c1-772e-49c3-a3ef-44e2d69b3a36","resolution":{"observed_at":"2026-08-05T11:12:52.054814Z","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":"2026-08-05T11:12:54.675650Z","title":"Intellilight: A reinforcement learning approach for intelligent traffic light control,","venue":null,"work_id":"79f641c9-7275-4f86-891d-27b9dbced190","year":2018},"citing_paper":{"arxiv_id":"2509.03118","last_updated":"2025-09-03T08:20:06Z","snapshot_observed_at":"2026-08-10T04:17:52.981746Z","submitted_at":"2025-09-03T08:20:06Z","title":"A Hierarchical Deep Reinforcement Learning Framework for Traffic Signal Control with Predictable Cycle Planning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-05T11:12:52.137199Z"},"links":{"citing_paper":"/paper/2509.03118"},"observation_digest":"sha256:c1300dbbf0d1987fa19a0f35f5afce9f37b118529f57e1ee3453dae2c9d35d02","observation_id":"e413dc06-1ed5-4e8e-9d14-1f774862cf63","resolution":{"observed_at":"2026-08-05T11:12:54.786857Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T11:12:54.507564Z","title":"Coordinated deep reinforcement learners for traffic light control,","venue":null,"work_id":"665a3cd7-8997-4e97-8210-735ac6658ffe","year":2016},"citing_paper":{"arxiv_id":"2509.03118","last_updated":"2025-09-03T08:20:06Z","snapshot_observed_at":"2026-08-10T04:17:52.981746Z","submitted_at":"2025-09-03T08:20:06Z","title":"A Hierarchical Deep Reinforcement Learning Framework for Traffic Signal Control with Predictable Cycle Planning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-05T11:12:52.190267Z"},"links":{"citing_paper":"/paper/2509.03118"},"observation_digest":"sha256:5dceb88c27ee6734fdeab1645c187127c7953c5ff12fbbc350651a0ff5812c47","observation_id":"0f5a31f5-5a74-4ec7-ba2a-6b5f9d2e9ad7","resolution":{"observed_at":"2026-08-05T11:12:54.594700Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T11:12:54.337123Z","title":"An experimental review of reinforcement learning algorithms for adaptive traffic signal control,","venue":null,"work_id":"3b67e867-7448-4353-8c7c-40cb09c7fefd","year":2016},"citing_paper":{"arxiv_id":"2509.03118","last_updated":"2025-09-03T08:20:06Z","snapshot_observed_at":"2026-08-10T04:17:52.981746Z","submitted_at":"2025-09-03T08:20:06Z","title":"A Hierarchical Deep Reinforcement Learning Framework for Traffic Signal Control with Predictable Cycle Planning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-05T11:12:52.279159Z"},"links":{"citing_paper":"/paper/2509.03118"},"observation_digest":"sha256:e815ca62596014e2253767ccb0700baae69364870c4edd561f3036172afc1553","observation_id":"a72237c7-fe4d-41b8-8353-5a0efb31c711","resolution":{"observed_at":"2026-08-05T11:12:54.440105Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1509.02971","last_updated":"2019-07-05T10:47:27Z","snapshot_observed_at":"2026-07-06T04:29:24.362640Z","submitted_at":"2015-09-09T23:01:36Z","title":"Continuous control with deep reinforcement learning","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1509.02971","snapshot_observed_at":"2026-08-05T11:12:52.432243Z","title":"Continuous control with deep reinforce- ment learning,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2509.03118","last_updated":"2025-09-03T08:20:06Z","snapshot_observed_at":"2026-08-10T04:17:52.981746Z","submitted_at":"2025-09-03T08:20:06Z","title":"A Hierarchical Deep Reinforcement Learning Framework for Traffic Signal Control with Predictable Cycle Planning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-05T11:12:52.432243Z"},"links":{"cited_paper":"/paper/1509.02971","citing_paper":"/paper/2509.03118"},"observation_digest":"sha256:8834c90481091f90518a94c5b3562a1e8970083f587bffcbb173669d915e922f","observation_id":"597140ee-f5c6-4053-9232-f675925b93ef","resolution":{"observed_at":"2026-08-05T11:12:52.432243Z","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":"2026-08-05T11:12:52.556753Z","title":"Traffic signal settings,","venue":null,"work_id":null,"year":1958},"citing_paper":{"arxiv_id":"2509.03118","last_updated":"2025-09-03T08:20:06Z","snapshot_observed_at":"2026-08-10T04:17:52.981746Z","submitted_at":"2025-09-03T08:20:06Z","title":"A Hierarchical Deep Reinforcement Learning Framework for Traffic Signal Control with Predictable Cycle Planning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-05T11:12:52.556753Z"},"links":{"citing_paper":"/paper/2509.03118"},"observation_digest":"sha256:e2b3f0657d70ef7ff35c58c5af4c8b1d408329d6db8b2a36375a58ac4a276e32","observation_id":"177661b9-59d8-4e65-98cd-dc5f3f08dfeb","resolution":{"observed_at":"2026-08-05T11:12:52.556753Z","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":"2026-08-05T11:12:54.267116Z","title":"Traffic signal timing manual","venue":null,"work_id":"a4d1b9b2-0372-4dda-8a3f-e7cd892f9a91","year":2008},"citing_paper":{"arxiv_id":"2509.03118","last_updated":"2025-09-03T08:20:06Z","snapshot_observed_at":"2026-08-10T04:17:52.981746Z","submitted_at":"2025-09-03T08:20:06Z","title":"A Hierarchical Deep Reinforcement Learning Framework for Traffic Signal Control with Predictable Cycle Planning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-05T11:12:52.648942Z"},"links":{"citing_paper":"/paper/2509.03118"},"observation_digest":"sha256:3f673c055a5ff0e7c0ad632f9396f8ac58089bdaae6e7a1610bc9b5885dc8fa0","observation_id":"2f0438a4-cb5d-4ba5-ac27-a69a37083e3b","resolution":{"observed_at":"2026-08-05T11:12:54.318539Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T11:12:54.067620Z","title":"Maxband: A versatile program for setting signals on arteries and triangular networks,","venue":null,"work_id":"8953ee76-97fd-4d72-b0ed-ab16640a4794","year":1981},"citing_paper":{"arxiv_id":"2509.03118","last_updated":"2025-09-03T08:20:06Z","snapshot_observed_at":"2026-08-10T04:17:52.981746Z","submitted_at":"2025-09-03T08:20:06Z","title":"A Hierarchical Deep Reinforcement Learning Framework for Traffic Signal Control with Predictable Cycle Planning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T11:12:52.742657Z"},"links":{"citing_paper":"/paper/2509.03118"},"observation_digest":"sha256:1b8cafbaf9e32e1c59ceb148c167ac6c3904256e4802073534e3978b8211725f","observation_id":"f0a66968-034f-4fe6-8f69-455b56e81bc9","resolution":{"observed_at":"2026-08-05T11:12:54.179304Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T11:12:53.795759Z","title":"Cityflow: A multi-agent reinforcement learning environment for large scale city traffic scenario,","venue":null,"work_id":"443fc11a-d998-4f56-a112-a5b729628dcf","year":2019},"citing_paper":{"arxiv_id":"2509.03118","last_updated":"2025-09-03T08:20:06Z","snapshot_observed_at":"2026-08-10T04:17:52.981746Z","submitted_at":"2025-09-03T08:20:06Z","title":"A Hierarchical Deep Reinforcement Learning Framework for Traffic Signal Control with Predictable Cycle Planning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-05T11:12:52.853674Z"},"links":{"citing_paper":"/paper/2509.03118"},"observation_digest":"sha256:8cb3c9a89448b74c3e66d784353c7f02b589a457b5f3921fb849efe58e125d93","observation_id":"61ca6217-a5ba-42b8-8642-4a3b5adfb965","resolution":{"observed_at":"2026-08-05T11:12:53.965000Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"nlin/0411066","last_updated":"2005-02-01T18:36:37Z","snapshot_observed_at":"2026-07-07T06:21:29.464842Z","submitted_at":"2004-11-30T17:25:00Z","title":"Self-Organizing Traffic Lights","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"nlin/0411066","snapshot_observed_at":"2026-08-05T11:12:53.012797Z","title":"Self-organizing traffic lights,","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2509.03118","last_updated":"2025-09-03T08:20:06Z","snapshot_observed_at":"2026-08-10T04:17:52.981746Z","submitted_at":"2025-09-03T08:20:06Z","title":"A Hierarchical Deep Reinforcement Learning Framework for Traffic Signal Control with Predictable Cycle Planning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-05T11:12:53.012797Z"},"links":{"cited_paper":"/paper/nlin/0411066","citing_paper":"/paper/2509.03118"},"observation_digest":"sha256:36d281e0c7aff681f8d9dad07a06c7ce44a305948b9221413555e6c98b335eb4","observation_id":"9b6ba835-e273-4dbc-b2cd-0bf4d4d038e0","resolution":{"observed_at":"2026-08-05T11:12:53.012797Z","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":"2026-08-05T11:12:53.645750Z","title":"Max pressure control of a network of signalized inter- sections,","venue":null,"work_id":"050e2e48-4340-42ed-8cef-cba6c9a40afc","year":2013},"citing_paper":{"arxiv_id":"2509.03118","last_updated":"2025-09-03T08:20:06Z","snapshot_observed_at":"2026-08-10T04:17:52.981746Z","submitted_at":"2025-09-03T08:20:06Z","title":"A Hierarchical Deep Reinforcement Learning Framework for Traffic Signal Control with Predictable Cycle Planning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-05T11:12:53.114797Z"},"links":{"citing_paper":"/paper/2509.03118"},"observation_digest":"sha256:8b42213e14a8a68ee84c76d8dc50a8767cb62d4f05f9c64ae91b763b21e2cfc2","observation_id":"c5037fff-7848-46b1-91b0-e97e43254d1d","resolution":{"observed_at":"2026-08-05T11:12:53.719179Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-05T11:12:53.226547Z","title":"Human-level control through deep reinforcement learning,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2509.03118","last_updated":"2025-09-03T08:20:06Z","snapshot_observed_at":"2026-08-10T04:17:52.981746Z","submitted_at":"2025-09-03T08:20:06Z","title":"A Hierarchical Deep Reinforcement Learning Framework for Traffic Signal Control with Predictable Cycle Planning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-05T11:12:53.226547Z"},"links":{"citing_paper":"/paper/2509.03118"},"observation_digest":"sha256:c69a2dac3d41498b060191be9f158296c92c1461e1bab4d1a15584aa5c813164","observation_id":"2dacb78f-ea2e-474c-888f-2a12edcffe8a","resolution":{"observed_at":"2026-08-05T11:12:53.226547Z","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":"2026-08-05T11:12:53.490332Z","title":"Dueling network architectures for deep reinforcement learning,","venue":null,"work_id":"b69e5ba1-8c72-4182-b620-c17bfdd68918","year":2016},"citing_paper":{"arxiv_id":"2509.03118","last_updated":"2025-09-03T08:20:06Z","snapshot_observed_at":"2026-08-10T04:17:52.981746Z","submitted_at":"2025-09-03T08:20:06Z","title":"A Hierarchical Deep Reinforcement Learning Framework for Traffic Signal Control with Predictable Cycle Planning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-05T11:12:53.301259Z"},"links":{"citing_paper":"/paper/2509.03118"},"observation_digest":"sha256:ce9c97e97bd74d5c9733542094c4d4729f67b0de9ed1bdedde9c7508ba61f6de","observation_id":"d2cb906e-9e1d-444b-8720-2f1651ed457c","resolution":{"observed_at":"2026-08-05T11:12:53.566958Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2509.03118","last_updated":"2025-09-03T08:20:06Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-10T04:17:52.981746Z","submitted_at":"2025-09-03T08:20:06Z","title":"A Hierarchical Deep Reinforcement Learning Framework for Traffic Signal Control with Predictable Cycle Planning"},"reference_resolution":{"displayed":30,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":7,"verified_exact":0,"verified_fuzzy":23},"total_outbound_references":30},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2509.03118."}