{"data":{"event":{"id":"ecfcd983-fc97-44a7-9c12-373d62a8cbc9","slug":"project-green-light-traffic-signals","title":"Google Project Green Light recommends traffic-signal changes in partner cities","short_summary":"Google Research’s Green Light, expanded publicly in 2023, uses Maps-derived models to suggest signal timing; Google reported up to 30 percent fewer stops and 10 percent lower intersection emissions in early tests covering tens of millions of monthly trips.","full_description":"City engineers accept or reject recommendations on existing hardware. Scientific American documented mixed local uptake, including Seattle reverting at least one change and Manchester often ignoring suggestions that conflicted with bus or neighborhood priorities. Impact estimates are Google-modeled, not independently metered citywide.","ledger_type":"benefit","primary_domain_id":"99fd8a40-a540-41d3-af98-57c1709ee422","event_status":"verified","event_date":"2023-10-10T00:00:00.000Z","discovery_date":"2023-10-10T00:00:00.000Z","first_published_date":"2023-10-10T00:00:00.000Z","last_reviewed_date":"2023-10-10T00:00:00.000Z","geographic_scope":"Partner cities on four continents","affected_population":"Drivers at participating intersections (Google cited tens of millions of monthly trips)","base_impact_tier":2,"base_score":"3.00","attribution_multiplier":"0.5000","evidence_multiplier":"0.2500","realization_multiplier":"0.7000","durability_multiplier":"0.4000","current_event_score":"0.073500","confidence_level":"high","score_explanation":"Real but localized traffic intervention (tier 2). Attribution shared with city engineers. Developer-reported metrics (0.25). Realization is live intersections. Durability limited and reversible.","methodology_version_id":"d7881163-fb23-4e71-8625-bac1a8662c0f","original_methodology_version_id":"d7881163-fb23-4e71-8625-bac1a8662c0f","published_at":"2023-10-10T00:00:00.000Z","created_at":"2026-09-13T20:47:20.075Z","updated_at":"2026-09-13T20:47:20.075Z","flags":[],"domain_name":"Climate & Environment","domain_slug":"climate-environment","methodology_version":"0.1"},"contributions":[{"id":"c20b5df0-b79d-437f-a856-3c7453423274","event_id":"ecfcd983-fc97-44a7-9c12-373d62a8cbc9","model_id":"f54eb626-e98c-4854-9642-9bc95e0a63c9","role_description":"Inferred stop-and-go patterns and proposed signal timing.","attribution_multiplier":"0.5000","credit_share":"0.7000","contribution_score":"0.073500","attribution_rationale":"Model proposed timings; cities implemented a subset.","attribution_confidence":"medium","first_used_date":"2021-01-01T00:00:00.000Z","model_version_if_known":null,"review_status":"approved","created_at":"2026-09-13T20:47:20.075Z","model_slug":"green-light-traffic-model","model_name":"Green Light Traffic Model","identity_class":"named_release","is_internal":false,"family_name":"Google Maps models","family_slug":"google-google-maps-models","organization_name":"Google","organization_slug":"google"}],"sources":[{"id":"8a036b2f-1c01-4f6e-a5ad-1b576819f562","event_id":"ecfcd983-fc97-44a7-9c12-373d62a8cbc9","url":"https://blog.google/company-news/outreach-and-initiatives/sustainability/google-ai-reduce-greenhouse-emissions-project-greenlight/","canonical_url":"https://blog.google/company-news/outreach-and-initiatives/sustainability/google-ai-reduce-greenhouse-emissions-project-greenlight/","source_type":"developer_announcement","publisher":"Google","author":null,"publication_date":"2023-10-10T00:00:00.000Z","retrieved_at":"2026-09-13T20:47:20.075Z","title":"How Google AI is helping reduce greenhouse gas emissions","excerpt":null,"content_hash":null,"source_reliability_class":"low","is_primary_source":true,"is_independent":false,"is_peer_reviewed":false,"archived_url":null,"created_at":"2026-09-13T20:47:20.075Z"},{"id":"1a99801f-66af-42ed-b8ad-990e75c20c02","event_id":"ecfcd983-fc97-44a7-9c12-373d62a8cbc9","url":"https://www.scientificamerican.com/article/googles-project-green-light-uses-ai-to-take-on-city-traffic/","canonical_url":"https://www.scientificamerican.com/article/googles-project-green-light-uses-ai-to-take-on-city-traffic/","source_type":"journalism","publisher":"Scientific American","author":null,"publication_date":"2024-06-01T00:00:00.000Z","retrieved_at":"2026-09-13T20:47:20.075Z","title":"Google’s Project Green Light Uses AI to Take on City Traffic","excerpt":null,"content_hash":null,"source_reliability_class":"medium","is_primary_source":false,"is_independent":true,"is_peer_reviewed":false,"archived_url":null,"created_at":"2026-09-13T20:47:20.075Z"}],"claims":[{"id":"bc61e4d5-58f9-4226-98a9-af5e8696a099","event_id":"ecfcd983-fc97-44a7-9c12-373d62a8cbc9","claim_text":"Green Light recommendations were implemented at intersections in multiple cities.","claim_type":"outcome","claim_status":"supported","confidence_score":null,"created_at":"2026-09-13T20:47:20.075Z","updated_at":"2026-09-13T20:47:20.075Z"},{"id":"2bd4fcb5-9b65-4818-9c6a-6aac62ec0bf5","event_id":"ecfcd983-fc97-44a7-9c12-373d62a8cbc9","claim_text":"The program cut intersection emissions by 10 percent everywhere it was tried.","claim_type":"outcome","claim_status":"disputed","confidence_score":null,"created_at":"2026-09-13T20:47:20.075Z","updated_at":"2026-09-13T20:47:20.075Z"}],"revisions":[{"id":"b0f68a17-0630-4324-b61c-13e707c131f2","event_id":"ecfcd983-fc97-44a7-9c12-373d62a8cbc9","model_id":null,"previous_score":"0.000000","new_score":"0.073500","previous_factors":{},"new_factors":{"evidence":0.25,"base_score":3,"durability":0.4,"attribution":0.5,"realization":0.7},"change_reason":"Initial adjudicated seed score under methodology 0.1.","trigger_type":"seed","trigger_source_ids":null,"reviewer_id":null,"review_status":"published","created_at":"2026-09-13T20:47:20.075Z"}],"secondary":[{"name":"Engineering","slug":"engineering"}]},"methodology_version":"0.1"}