ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · BERKLEY, 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/berkley/2022-annual-report
Yearly Traffic Safety Analysis
155 CRASHES IN
BERKLEY, MA
2022
In 2022, Berkley recorded 155 total crashes, a 40.9% increase from the 110 crashes documented in 2021. While the number of fatalities remained stable with one death in each period, the overall volume of collisions rose significantly. The most notable year-over-year shift was the increase in hit-and-run incidents, which surged from a single crash in 2021 to nine in 2022.
155
▲ 40.9%was 110
Total Crash Events
1
Persons Killed
36
▲ 5.9%was 34
Persons Injured
9
▲ 800.0%was 1
Hit-and-Run Crashes
Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 11 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 trends in Berkley show a significant upward movement year-over-year. Total crashes rose from 110 in 2021 to 155 in 2022, marking a 40.9% increase. The number of reported injuries saw a slight rise from 34 to 36, while fatalities held steady with one death recorded in each year.
9
Hit-and-Run Crashes — 2022
▲ 800.0% vs prior (1)
Hit-and-run incidents experienced a substantial year-over-year increase. The absolute number of hit-and-run crashes rose from 1 in 2021 to 9 in 2022, representing an 800% increase in count. As a result, the hit-and-run rate, which measures the percentage of total crashes classified as hit-and-run, climbed sharply from 0.9% to 5.8%.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Cyclists Killed
1
Motorists Killed
1
Pedestrians Injured
1
Cyclists Injured
34
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
The temporal patterns of crashes shifted between the two periods. In 2022, Friday was the peak day for crashes with 37 incidents, a change from 2021 when Sunday was the busiest day with 19 crashes. The peak hour also moved from 2 p.m. in 2021 (10 crashes) to 10 a.m. in 2022 (14 crashes), indicating a change in when collisions were most frequent.
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 was unchanged at one for both 2021 and 2022, the fatal crash rate per 100 crashes decreased from 0.91 to 0.65 due to the higher total number of collisions. The proportion of crashes resulting in any injury also declined, from 24.5% in 2021 to 20.6% in 2022. This was driven by a drop in the share of crashes involving serious injuries, which fell from 2.7% to 1.3%.
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 factor in both years was 'No improper driving,' with the count of such incidents increasing from 32 in 2021 to 46 in 2022. 'Followed too closely' remained a top factor, with its count rising from 9 to 12. A significant change was observed in crashes attributed to 'Swerving or avoiding,' which increased from 3 incidents in 2021 to 11 in 2022. Conversely, incidents involving 'Failure to keep in proper lane' decreased in count from 9 to 6.
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 proportion of crashes occurring in clear weather remained stable at approximately 67% for both 2021 and 2022. However, there was a shift in lighting and road surface conditions associated with crashes. The share of incidents happening in daylight increased from 50.0% in 2021 to 63.9% in 2022. Similarly, crashes on dry road surfaces accounted for a larger portion of the total, rising from 70.9% of incidents in 2021 to 82.6% 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 showed consistency, with Toyota and Ford holding the top two spots in both years; their involvement counts increased from 24 to 28 and 20 to 25, respectively. Analysis of persons involved in crashes shows the 26-34 age group was the largest demographic in both periods, increasing from 44 individuals in 2021 to 56 in 2022. The number of individuals from the 35-44 age group involved in crashes nearly doubled, rising from 22 to 41.
Top Vehicle Makes (234 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Vehicle unit records
16 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (230 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 increasingly occurred in higher speed zones in 2022 compared to the prior year. The number of incidents in 65 mph zones rose from 36 to 66, accounting for 42.6% of crashes with a recorded speed limit in 2022, up from 32.7% in 2021. The location of the single fatal crash also shifted, occurring in a 65 mph zone in 2022, whereas the 2021 fatality happened in a 35 mph zone.
Fatal crashes by zone: 65 mph: 1 of 66 (1.515%)
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: BERKLEY, MA
- Total crash records analyzed: 155
- Total persons involved: 263
- Total vehicles involved: 234
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). "BERKLEY, 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/berkley/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