ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · BERLIN, MA · 2023
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/berlin/2023-annual-report
Yearly Traffic Safety Analysis
146 CRASHES IN
BERLIN, MA
2023
In Berlin, MA, total traffic crashes increased slightly from 141 in 2022 to 146 in 2023, a change of approximately 3.5%. While total crashes remained relatively stable and no fatalities occurred in either year, the most notable year-over-year shift was a 53.2% increase in the number of people injured, which rose from 47 to 72.
146
▲ 3.5%was 141
Total Crash Events
0
Persons Killed
72
▲ 53.2%was 47
Persons Injured
5
▲ 400.0%was 1
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. 17 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall traffic safety trends in Berlin show a slight increase in the total volume of crashes, which rose by 3.5% from 141 in 2022 to 146 in 2023. While fatalities remained at zero for both years, the number of persons injured in these incidents increased significantly from 47 to 72, marking a 53.2% rise.
5
Hit-and-Run Crashes — 2023
▲ 400.0% vs prior (1)
The number of hit-and-run crashes increased from a single incident in 2022 to 5 incidents in 2023. Consequently, the hit-and-run rate, which measures the percentage of total crashes that are hit-and-runs, rose from 0.7% to 3.4% year-over-year. This indicates an upward trend for this type of collision during the analysis period.
Vulnerable Road User Casualties
0
Motorists Killed
72
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The daily pattern of crashes shifted year-over-year, with the peak day moving from Saturday (31 crashes) in 2022 to Tuesday (24 crashes) in 2023. Despite this change in the weekly trend, the peak hour for collisions was consistent across both periods. The 3 p.m. hour was the most frequent time for crashes in both 2022 and 2023, with 15 incidents recorded in that hour each year.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
There were no fatal crashes recorded in either 2022 or 2023. However, the overall proportion of crashes involving an injury increased, rising from 24.8% of all crashes in 2022 to 30.8% in 2023. While the number of serious injury crashes decreased from 8 to 5, crashes resulting in minor injuries (11 to 18) and possible injuries (16 to 22) both increased.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Most severe injury per crash record
Top Contributing Factors
In both 2022 and 2023, a significant portion of crashes were attributed to 'No improper driving,' though the count for this factor decreased from 30 to 25. The count of crashes involving 'Followed too closely' increased from 19 in 2022 to 24 in 2023, making it the second most-cited factor in the current period. Crashes where 'Failed to yield right of way' was a factor also increased in count from 12 to 14.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes occurring on wet roads became more frequent, accounting for 22.6% of incidents in 2023 compared to 12.8% in 2022. This aligns with an increase in crashes during rainy weather, which rose from 8 incidents in 2022 to 17 in 2023. The proportion of crashes occurring in daylight also increased, from 68.1% in the prior period to 74.0% in the current period.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Road surface condition field
Vehicles & Demographics
The top three vehicle makes involved in crashes—Toyota, Honda, and Ford—remained the same across both years. A notable change was the increase in Subaru vehicles involved in crashes, which rose from 5 in 2022 to 21 in 2023. Analysis of persons involved in crashes shows a significant increase in the 26-34 age group, from 63 individuals in 2022 to 81 in 2023.
Top Vehicle Makes (254 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Vehicle unit records
8 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (308 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Person-level records linked to crash events
Speed Limit Zones
A shift occurred in the speed zones where crashes were most prevalent. Incidents in 65 mph zones increased from 31 in 2022 to 46 in 2023, while crashes in 40 mph zones decreased from 43 to 34. Crashes in 35 mph zones also saw a rise from 25 to 33. No fatalities were recorded in any speed zone for either year.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · 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: 2023-01-01 through 2023-12-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2023-01-01 through 2023-12-31 (365 days)
- Geographic scope: BERLIN, MA
- Total crash records analyzed: 146
- Total persons involved: 327
- Total vehicles involved: 254
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). "BERLIN, MA Crash Intelligence Report: 2023." Published June 21, 2026. Reporting period: 2023-01-01 to 2023-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/berlin/2023-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: 2023-01-01 – 2023-12-31
Generated: June 21, 2026 · All rights reserved