ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · PEABODY, 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/peabody/2022-annual-report
Yearly Traffic Safety Analysis
1,297 CRASHES IN
PEABODY, MA
2022
In 2022, Peabody recorded 1,297 total traffic crashes, a 60.5% increase from the 808 crashes documented in 2021. This substantial rise in crash volume represents the most notable year-over-year shift in the data. While the number of total injuries increased by 86.7% from 240 to 448, total fatalities decreased from 3 to 2.
1,297
▲ 60.5%was 808
Total Crash Events
2
▼ -33.3%was 3
Persons Killed
448
▲ 86.7%was 240
Persons Injured
58
▲ 81.3%was 32
Hit-and-Run Crashes
Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 46 crashes with unreported severity are not shown in the severity breakdown.
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 data for Peabody indicates a significant rising trend year-over-year. Total collisions surged by 60.5%, from 808 in 2021 to 1,297 in 2022. This increase was accompanied by an 86.7% rise in total injuries, although fatalities saw a slight decrease from 3 to 2.
58
Hit-and-Run Crashes — 2022
▲ 81.3% vs prior (32)
Hit-and-run incidents increased significantly in both count and rate. The number of hit-and-run crashes rose by 81.3%, from 32 in 2021 to 58 in 2022. The hit-and-run rate also trended upward, increasing from 4.0% of all crashes in the prior year to 4.5% in the current year.
Vulnerable Road User Casualties
1
Pedestrians Killed
0
Cyclists Killed
1
Motorists Killed
14
Pedestrians Injured
9
Cyclists Injured
425
Motorists 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
Temporal patterns shifted between the two periods. The peak day for crashes moved from Saturday (135 crashes) in 2021 to Friday (205 crashes) in 2022. Similarly, the peak hour for collisions occurred earlier, shifting from 5 p.m. in 2021 (69 crashes) to 3 p.m. in 2022 (111 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
While the number of fatal crashes decreased from 3 in 2021 to 2 in 2022, the proportion of crashes involving some form of injury increased. Crashes with reported injuries (Serious, Minor, or Possible) constituted 25.3% of all incidents in 2022, up from 21.4% in 2021. Notably, the count of serious injury crashes increased from 5 to 17 year-over-year.
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 top three contributing factors—'No improper driving,' 'Inattention,' and 'Followed too closely'—were consistent across both years. However, the count of crashes attributed to 'Inattention' grew by 76.2%, from 151 incidents in 2021 to 266 in 2022. The count of crashes involving 'Following too closely' also rose from 114 to 140, a 22.8% increase, though its share of total crashes decreased from 14.1% to 10.8%.
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
The distribution of crashes across different conditions remained broadly similar year-over-year. Crashes on dry roads accounted for 82.9% of incidents in 2022 compared to 81.3% in 2021, and daylight crashes made up 69.1% of the total versus 66.6% the prior year. The proportion of crashes in clear weather saw a slight increase, rising to 70.6% of all crashes in 2022 from 63.6% in 2021.
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 three vehicle makes involved in crashes—Toyota, Honda, and Ford—maintained their rankings in 2022 as they held in 2021. Regarding persons involved, the 26-34 age group was the most frequently represented in both periods, with its count increasing from 332 individuals to 528. All age demographics saw a rise in involvement, with the number of persons aged 65 and older increasing by 78% from 188 to 335.
Top Vehicle Makes (2,547 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Vehicle unit records
182 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (2,733 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 lower speed zones, with 25 mph and 30 mph zones accounting for the highest volumes in both years. The number of crashes in 25 mph zones increased by 89% from 167 to 316, and collisions in 30 mph zones rose by 74% from 162 to 282. The location of fatal crashes shifted, with two fatalities occurring in a 35 mph zone in 2021 and two occurring in a 25 mph zone in 2022.
Fatal crashes by zone: 25 mph: 2 of 316 (0.633%)
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: PEABODY, MA
- Total crash records analyzed: 1,297
- Total persons involved: 2,996
- Total vehicles involved: 2,547
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). "PEABODY, 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/peabody/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