Monthly Traffic Safety Analysis

208 CRASHES IN
BROCKTON, MA
JANUARY 2026

All metrics benchmarked againstJanuary 2025

Total crashes in Brockton decreased by 13.0%, from 239 in January 2025 to 208 in January 2026. This period saw a notable 57.1% reduction in hit-and-run crashes, dropping from 14 to 6 incidents. Total injuries also decreased, from 113 to 103.

208

▼ -13.0%was 239

Total Crash Events

0

Persons Killed

103

▼ -8.8%was 113

Persons Injured

6

▼ -57.1%was 14

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. 15 crashes with unreported severity are not shown in the severity breakdown.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crash incidents in Brockton decreased year-over-year. Total crashes fell by 13.0%, from 239 in January 2025 to 208 in January 2026. Similarly, the number of total injuries decreased by 8.8%, from 113 to 103.

6

Hit-and-Run Crashes — January 2026

▼ -57.1% vs prior (14)

Hit-and-run crashes decreased significantly from 14 incidents in January 2025 to 6 incidents in January 2026. This resulted in the hit-and-run rate falling from 5.9% to 2.9% of all crashes, indicating a downward trend.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Motorists Killed

Prior: 0→0.0%

3

Pedestrians Injured

Prior: 3→0.0%

100

Motorists Injured

Prior: 109↓-8.3%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The peak day for crashes shifted from Friday with 39 incidents in January 2025 to Monday with 40 incidents in January 2026. The peak hour also changed, moving from 5 PM with 24 crashes in January 2025 to 9 AM with 16 crashes in January 2026.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Fatalities remained at 0 in both January 2025 and January 2026. Serious injury crashes (code A) increased from 2 (0.8% share) in January 2025 to 3 (1.4% share) in January 2026. Minor injury crashes (code B) decreased from 34 (14.2% share) to 32 (15.4% share), while possible injury crashes (code C) decreased from 30 (12.6% share) to 28 (13.5% share).

Outcome by Severity (Crash Events)

Serious Injury3serious injury crashes1.4%
↑50.0%prior 2
Minor Injury32minor injury crashes15.4%
↓-5.9%prior 34
Possible Injury28possible injury crashes13.5%
↓-6.7%prior 30
No Injury130no injury crashes62.5%
↓-6.5%prior 139

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Most severe injury per crash record

Top Contributing Factors

Crashes attributed to 'No improper driving' decreased by 9 incidents, from 67 to 58, representing a 13.4% reduction. Conversely, 'Failed to yield right of way' crashes increased by 9 incidents, from 25 to 34, marking a 36.0% rise. Crashes due to 'Inattention' more than doubled, increasing by 6 incidents from 5 to 11, a 120.0% change.

Officer-Reported Primary Contributing Cause

No improper driving58 (27.9%)↓-13.4%prior 67
Failed to yield right of way34 (16.3%)↑36.0%prior 25
Inattention11 (5.3%)↑120.0%prior 5
Followed too closely11 (5.3%)↓-15.4%prior 13
Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway8 (3.8%)↓-11.1%prior 9
Failure to keep in proper lane or running off road5 (2.4%)↓-64.3%prior 14
Disregarded traffic signs, signals, road markings4 (1.9%)↓-71.4%prior 14
Distracted4 (1.9%)
Driving too fast for conditions4 (1.9%)↓-42.9%prior 7
Other improper action3 (1.4%)↓-62.5%prior 8

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in 'Clear' weather conditions decreased by 48, from 149 in January 2025 to 101 in January 2026. Conversely, crashes during 'Snow' conditions increased by 23, from 17 to 40 incidents. On road surfaces, 'Dry' conditions saw a decrease of 54 crashes (from 152 to 98), while 'Snow' conditions saw an increase of 31 crashes (from 28 to 59).

Weather

Clear101 (48.6%)
↓-32.2%prior 149
Snow40 (19.2%)
↑135.3%prior 17
Cloudy21 (10.1%)
↑40.0%prior 15
Clear/Cloudy8 (3.8%)
↓-20.0%prior 10
Rain7 (3.4%)
↓-22.2%prior 9
Clear/Unknown5 (2.4%)
↓-28.6%prior 7
Snow/Sleet, hail (freezing rain or drizzle)4 (1.9%)
Clear/Other4 (1.9%)
↓-42.9%prior 7
Snow/Blowing sand, snow3 (1.4%)
Snow/Other2 (1.0%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Weather condition at time of crash

Lighting

Daylight107 (51.4%)
↓-21.9%prior 137
Dark - lighted roadway83 (39.9%)
↑31.7%prior 63
Dusk9 (4.3%)
↑28.6%prior 7
Dawn5 (2.4%)
↓-80.0%prior 25
Dark - roadway not lighted4 (1.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Lighting condition field

Road Surface

Dry98 (47.1%)
↓-35.5%prior 152
Snow59 (28.4%)
↑110.7%prior 28
Wet44 (21.2%)
↑37.5%prior 32
Ice5 (2.4%)
↓-81.5%prior 27
Other1 (0.5%)
Slush1 (0.5%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Road surface condition field

Vehicles & Demographics

The total number of vehicles involved in crashes decreased from 453 to 401. Toyota remained the most frequently involved make, with 88 incidents in January 2026, a slight decrease from 90. Honda saw an increase in involvement from 45 to 64, while Ford saw a decrease from 53 to 32.

Top Vehicle Makes (401 vehicles)

1
TOYOTA88 (21.9%)
↓-2.2%prior 90
2
HONDA64 (16%)
↑42.2%prior 45
3
CHEVROLET36 (9%)
↑2.9%prior 35
4
FORD32 (8%)
↓-39.6%prior 53
5
NISSAN28 (7%)
↓-37.8%prior 45
6
JEEP18 (4.5%)
→0.0%prior 18
7
HYUNDAI15 (3.7%)
↓-31.8%prior 22
8
KIA12 (3%)
↑20.0%prior 10
9
SUBARU10 (2.5%)
↑11.1%prior 9
10
MERCEDES-BENZ9 (2.2%)
→0.0%prior 9

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Vehicle unit records

40 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (483 persons with recorded sex)

Male301 (62.3%)
↓-3.5%prior 312
Female182 (37.7%)
↓-24.5%prior 241

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · Person-level records linked to crash events

Speed Limit Zones

Crashes in 30 mph speed zones decreased from 208 in January 2025 to 176 in January 2026. Similarly, crashes in 65 mph speed zones decreased from 8 to 6 incidents. No fatal crashes were recorded in any speed zone during either period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-01-01 to 2026-01-31 · 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: 2026-01-01 through 2026-01-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2026-01-01 through 2026-01-31 (31 days)
  • Geographic scope: BROCKTON, MA
  • Total crash records analyzed: 208
  • Total persons involved: 528
  • Total vehicles involved: 401

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). "BROCKTON, MA Crash Intelligence Report: January 2026." Published June 21, 2026. Reporting period: 2026-01-01 to 2026-01-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/brockton/january-2026-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

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Brockton, MA Crash Report — January 2026 | ThatCarHitMe.com