ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · NORFOLK, MA · 2022
Purpose: Machine-readable JSON endpoint for AI agents, LLMs, researchers, and programmatic consumers. Returns all underlying crash data and AI-generated commentary without HTML.
Authentication: None required. Public endpoint.
GET: https://thatcarhitme.com/api/crash-data/reports/data/massachusetts/norfolk/2022-annual-report
Yearly Traffic Safety Analysis
99 CRASHES IN
NORFOLK, MA
2022
In Norfolk, total crashes rose from 80 in 2021 to 99 in 2022, a 23.8% increase. While total injuries saw a slight rise from 17 to 18, there were no fatalities reported in either year. One of the most notable year-over-year shifts was a substantial increase in crashes occurring on unlit dark roadways, which grew from 5 incidents in 2021 to 21 in 2022.
99
▲ 23.8%was 80
Total Crash Events
0
Persons Killed
18
▲ 5.9%was 17
Persons Injured
3
▲ 50.0%was 2
Hit-and-Run Crashes
Note: "Persons Killed" (0) counts individual fatalities across all crash events. "Fatal" in the severity table below (0) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Crash totals in Norfolk showed an upward trend, increasing by 23.8% from 80 incidents in 2021 to 99 in 2022. The number of people injured rose slightly from 17 to 18 year-over-year. Fatalities remained at zero for both periods.
3
Hit-and-Run Crashes — 2022
▲ 50.0% vs prior (2)
The number of hit-and-run incidents increased from 2 in 2021 to 3 in 2022. This corresponds to a slight rise in the hit-and-run rate as a percentage of total crashes, which went from 2.5% in 2021 to 3.0% in 2022.
Vulnerable Road User Casualties
0
Motorists Killed
0
Other Killed
17
Motorists Injured
1
Other Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The timing of crashes shifted between the two periods. In 2022, the peak day for crashes was Friday with 16 incidents, changing from Wednesday (14 incidents) in the prior year. The busiest hour also moved from 4 p.m. in 2021 (11 crashes) to 5 p.m. in 2022, which saw 16 crashes.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
No fatal crashes were recorded in either 2021 or 2022. The overall proportion of crashes involving any injury was similar, at 18.2% in 2022 compared to 17.6% in 2021. However, the severity profile shifted, as the 2 serious injury crashes from 2021 were not repeated in 2022, while the count of minor injury crashes rose from 7 to 10.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Most severe injury per crash record
Top Contributing Factors
The leading contributing factors remained consistent, though their counts generally increased with the overall rise in crashes. "No improper driving" was the most cited factor in both years, and its count increased by 68.2%, from 22 crashes in 2021 to 37 in 2022. The count of crashes involving "Inattention" grew from 7 to 11, while those attributed to "Failure to keep in proper lane or running off road" increased from 7 to 9.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crash conditions saw a significant shift in lighting, with collisions on dark, unlighted roadways increasing from 5 incidents in 2021 to 21 in 2022. Conversely, crashes on wet road surfaces decreased from 17 to 7. The share of crashes occurring on dry roads increased from 68.8% in 2021 to 85.9% in 2022.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Road surface condition field
Vehicles & Demographics
The top vehicle makes involved in crashes were largely consistent, with Toyota and Ford leading in both years. In 2022, 27 Toyotas and 15 Fords were involved, compared to 22 and 15, respectively, in 2021. An analysis of persons involved in crashes shows a notable increase in the 16-20 age group (from 15 to 27 individuals) and the 45-54 age group (from 20 to 28 individuals).
Top Vehicle Makes (159 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Vehicle unit records
4 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (206 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Person-level records linked to crash events
Speed Limit Zones
Crashes remained concentrated in similar speed zones across both years. The 35 mph zone accounted for the most incidents in both periods, with the count increasing from 41 in 2021 to 54 in 2022. The number of crashes in 30 mph zones held steady at 23. There were no fatal crashes recorded in any speed zone for either year.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Posted speed limit at crash location
Data Sources & Methodology
Primary Data Source
All crash data in this report is sourced from Massachusetts Crash Data (MassDOT CDV), accessed programmatically via the Arcgis_yearly Open Data API (SODA). This dataset contains official police-reported motor vehicle traffic crash records maintained by the reporting jurisdiction's law enforcement agency. Records are published to the open data portal by the municipality and are subject to the portal's terms of use.
Data Retrieval
- Access method: Arcgis_yearly Open Data API (SoQL queries)
- Data format: Structured JSON via REST API
- Record types queried: Crash events, person records, and vehicle unit records
- Date filter applied: 2022-01-01 through 2022-12-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2022-01-01 through 2022-12-31 (365 days)
- Geographic scope: NORFOLK, MA
- Total crash records analyzed: 99
- Total persons involved: 210
- Total vehicles involved: 159
Analytical Methodology
- Severity classification: Uses the KABCO injury scale (K=Fatal, A=Incapacitating injury, B=Non-incapacitating injury, C=Possible injury, O=No injury/property damage only), the standard classification in U.S. Model Minimum Uniform Crash Criteria (MMUCC). Severity is assigned per crash event based on the most severe injury in that crash. A single fatal crash (K) may involve multiple fatalities; therefore the "Persons Killed" count in the headline KPIs may differ from the "Fatal" crash count in the severity breakdown.
- Contributing factors: Reflect the officer-determined primary contributory cause recorded at the time of the crash report. These are preliminary determinations and may not reflect final investigation findings.
- Hit-and-run classification: Based on the hit-and-run indicator field in the official crash report, as determined by the responding officer at the scene.
- Temporal analysis: Day-of-week and hour-of-day distributions are computed from the crash date/time timestamp in each record.
- Demographics: Age and sex distributions are drawn from person-level records linked to each crash event. A single crash may involve multiple persons.
- Vehicle data: Make information is drawn from vehicle unit records linked to each crash event.
- AI commentary: Narrative sections are generated by Google Gemini (large language model) based on the structured data. Commentary is descriptive, not predictive, and should not be interpreted as expert opinion.
Limitations & Disclaimers
- Only crashes reported to and documented by law enforcement are included. Minor incidents, unreported crashes, and near-misses are not captured in this dataset.
- Data reflects conditions at the time of the initial police report and may be subject to subsequent corrections, reclassifications, or supplements by the reporting agency.
- Open data portal records may experience a publication lag - recently occurring crashes may not yet appear in the dataset at the time of report generation.
- AI-generated commentary is produced by a large language model and is intended to highlight patterns in the data. It does not constitute legal, medical, or professional analysis.
- Percentages are calculated from reported data and are subject to rounding.
Non-Affiliation Disclosure
This report is produced independently by ThatCarHitMe.com (Injuria.ai). It is not affiliated with, endorsed by, or produced in partnership with any law enforcement agency, municipal government, state department of transportation, or the National Highway Traffic Safety Administration (NHTSA). Data is sourced from publicly available government open data portals.
Data License
The underlying crash data is provided under the municipality's Open Data Terms of Use and is made available to the public for unrestricted use. This analysis and report is © 2026 Injuria.ai and may be cited with attribution using the suggested citation below.
Corrections & Feedback
If you believe any data in this report is inaccurate or have questions about our methodology, please contact: data@injuria.ai. We are committed to accuracy and will issue corrections promptly.
Suggested Citation
ThatCarHitMe.com (Injuria.ai). "NORFOLK, MA Crash Intelligence Report: 2022." Published June 21, 2026. Reporting period: 2022-01-01 to 2022-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/norfolk/2022-annual-report
About the Publisher
ThatCarHitMe.com is a crash data intelligence platform developed by Injuria.ai, a legal technology company specializing in traffic safety analytics. We aggregate and analyze publicly available government crash data to produce structured intelligence reports for communities, researchers, journalists, and legal professionals. Our reports combine programmatic data retrieval from official open data portals with AI-assisted narrative analysis.
Questions about this report's data or methodology: data@injuria.ai
ThatCarHitMe.com · An Injuria.ai Company
ThatCarHitMe.com
An Injuria.ai Company
Crash Data Intelligence
Data: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly
Period: 2022-01-01 – 2022-12-31
Generated: June 21, 2026 · All rights reserved