{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:IRSVB7R57WUEWR5N7RKFYIROO3","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"f9c6fd310740fa8d20840b60af47ea741d724d84f0750c46e54763deb9cca6b4","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2019-12-02T23:07:43Z","title_canon_sha256":"73a6814accf50e954b0e6490f5714a0ed415bc52c425e5aeb0749b52701b204e"},"schema_version":"1.0","source":{"id":"1912.01118","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1912.01118","created_at":"2026-07-05T01:25:02Z"},{"alias_kind":"arxiv_version","alias_value":"1912.01118v2","created_at":"2026-07-05T01:25:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1912.01118","created_at":"2026-07-05T01:25:02Z"},{"alias_kind":"pith_short_12","alias_value":"IRSVB7R57WUE","created_at":"2026-07-05T01:25:02Z"},{"alias_kind":"pith_short_16","alias_value":"IRSVB7R57WUEWR5N","created_at":"2026-07-05T01:25:02Z"},{"alias_kind":"pith_short_8","alias_value":"IRSVB7R5","created_at":"2026-07-05T01:25:02Z"}],"graph_snapshots":[{"event_id":"sha256:76cfb277cb5f80a3304dfd9697d71f9212125d7ca7dd6aa8747b4bbdf0f09a50","target":"graph","created_at":"2026-07-05T01:25:02Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1912.01118/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We present a novel approach for traffic forecasting in urban traffic scenarios using a combination of spectral graph analysis and deep learning. We predict both the low-level information (future trajectories) as well as the high-level information (road-agent behavior) from the extracted trajectory of each road-agent. Our formulation represents the proximity between the road agents using a weighted dynamic geometric graph (DGG). We use a two-stream graph-LSTM network to perform traffic forecasting using these weighted DGGs. The first stream predicts the spatial coordinates of road-agents, while","authors_text":"Aniket Bera, Dinesh Manocha, Rohan Chandra, Srujan Panuganti, Tianrui Guan, Trisha Mittal, Uttaran Bhattacharya","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2019-12-02T23:07:43Z","title":"Forecasting Trajectory and Behavior of Road-Agents Using Spectral Clustering in Graph-LSTMs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1912.01118","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:347449064cd9c70c5a1d8fe4eaa9c714a4fe2c9258706ecaefe38adc6b59960e","target":"record","created_at":"2026-07-05T01:25:02Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"f9c6fd310740fa8d20840b60af47ea741d724d84f0750c46e54763deb9cca6b4","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2019-12-02T23:07:43Z","title_canon_sha256":"73a6814accf50e954b0e6490f5714a0ed415bc52c425e5aeb0749b52701b204e"},"schema_version":"1.0","source":{"id":"1912.01118","kind":"arxiv","version":2}},"canonical_sha256":"446550fe3dfda84b47adfc545c222e76ec5e16be469eecd25546ce0f208b0a5b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"446550fe3dfda84b47adfc545c222e76ec5e16be469eecd25546ce0f208b0a5b","first_computed_at":"2026-07-05T01:25:02.135607Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:25:02.135607Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"hL5BWzx0lGqT02omce3LS3jD9fJ0mtCSAJ51Gm+ejiI9rf6R6sRaWDdq/NuqzHLptHttANZrlnHLGpQjv0syBA==","signature_status":"signed_v1","signed_at":"2026-07-05T01:25:02.136354Z","signed_message":"canonical_sha256_bytes"},"source_id":"1912.01118","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:347449064cd9c70c5a1d8fe4eaa9c714a4fe2c9258706ecaefe38adc6b59960e","sha256:76cfb277cb5f80a3304dfd9697d71f9212125d7ca7dd6aa8747b4bbdf0f09a50"],"state_sha256":"2312ca90808e94edd09a602d44fcfcc94300b907ac821196150674b79f99e981"}