Do robot corridors overlap high-injury streets?
Yes, and the overlap is the point. Every robot delivery corridor sits on top of streets a city has already flagged as its most dangerous. The LA West Side corridor contains 276 pedestrian High Injury Network segments; Austin's High Injury Network is just 8% of the street network yet carries about 60% of serious and fatal crashes, and Atlanta's roughly 8% of streets account for 88% of fatalities. The metrics differ city by city because each city publishes different data, a raw crash count here, a network share there, so this is a table of measured figures with each city's own source, not a single ranking. The reading is not that robots are unsafe on these streets. It is that a delivery robot and a pedestrian fatality are drawn to the same corridors, so the safety data a city already publishes is exactly the data a robot operator should be routing against.
Every figure is measured from a named city Vision Zero open dataset. How we verify.
Corridor by corridor
Each corridor's Vision Zero figure, with the exact metric each city publishes and its source. The figures are deliberately not collapsed into one number: a raw crash count and a network share are different measurements, so each row states what its number counts.
| Corridor | Metric | Figure | What it counts | Basis |
|---|---|---|---|---|
| LA West Side | Pedestrian High Injury Network segments | 276 | 276 pedestrian High Injury Network segments in corridor bbox from LA Geohub (2024 HIN, ArcGIS FeatureServer layer 4). HIN identifies streets with highest KSI. 207 ALL-mode HIN segments also in corridor. Source | Measured |
| SF Core | Pedestrian crashes in corridor | 12,691 | 12,691 pedestrian-involved traffic crashes in corridor bbox from DataSF (ubvf-ztfx). Total crashes in corridor: 48,381. SF HIN: corridors with 10+ KSI per mile (2024 update by SFDPH). Source | Measured |
| Chicago | Total crashes in corridor | 264,874 | 264,874 traffic crashes in Chicago corridor bbox from Chicago Data Portal (85ca-t3if). Chicago also has Traffic Crashes - People dataset (u6pd-qa9d) with injury details. Vision Zero Chicago uses IDOT data. Source | Measured |
| Austin | High Injury Network share of streets | 8% | Austin HIN = 8% of street network but contains 60% of serious injury/fatal crashes. Pedestrian serious injuries: 59 (2024) -> 50 (2025), down 15.3%. 750+ leading pedestrian intervals implemented. Source | Measured |
| Atlanta | High Injury Network share of fatalities | 88% | Less than 8% of Atlanta's streets accounted for 88% of fatalities and 52% of severe injuries. Just 10 streets = 1/3 of traffic deaths. ATLDOT Vision Zero Dashboard (ArcGIS). SourceCaptured ATLDOT dashboard URL 404s; figure corroborated by Propel ATL's High Injury Network page. | Measured |
| Miami | Vision Zero target year | 2040 | Miami-Dade Vision Zero goal: end traffic fatalities and serious injuries by 2040. Interactive hub at visionzeromdc.miamidade.gov shows crash trends and safety hotspots. Source | Measured |
| Phoenix | Vision Zero dashboard | Published | Tempe Vision Zero Dashboard shows fatal/serious injury crashes 2012-latest. Phoenix Road Safety Action Plan based on Vision Zero philosophy. ArcGIS crash detail dataset (ecdef8f62f1d45f888e2b06293961143). Source | Measured |
| Dallas-Fort Worth | Vision Zero program | Published | Dallas Vision Zero 2024 program on dallasopendata.com. Strategy to eliminate traffic fatalities and severe injuries. Source | Measured |
Why this overlap is an operator's routing input, not a verdict
A robot corridor overlapping a high-injury network is not a claim that robots cause crashes. It is a claim that the streets where delivery demand concentrates are the same streets where a city has already measured the most pedestrian harm. That makes the city's own Vision Zero data the single best public input to a robot's routing and speed policy. The overlap is an argument for using the safety data, not against deploying: the data already exists, corridor by corridor.
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Every figure is measured from a named city Vision Zero open dataset (LA Geohub, DataSF, Chicago Data Portal, Austin and Atlanta city dashboards, and others). The metrics differ by city because each publishes different data, so each row carries its own metric, grain sentence, and source rather than a collapsed ranking. The Atlanta row's captured ATLDOT URL 404s, so a corroborating Propel ATL source is cited; the figure is unchanged. These are measured-from-open-data facts, not reviewed DEPLOY registry records. How we verify
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