Monthly Traffic Safety Analysis

10 CRASHES IN
HARVARD, MA
APRIL 2026

All metrics benchmarked againstApril 2025

In April 2026, Harvard experienced 10 crashes, a 28.6% decrease from the 14 crashes recorded in April 2025. Total injuries also saw a significant reduction, falling by 50% from 6 injuries to 3. The most notable shift was the decrease in overall crash volume and associated injuries.

10

-28.6%was 14

Total Crash Events

0

Persons Killed

3

-50.0%was 6

Persons Injured

0

-100.0%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. 1 crash with unreported severity is not shown in the severity breakdown.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-04-01 to 2026-04-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crash data for April 2026 indicates a downward trend compared to the previous year. Total crashes decreased by 28.6%, from 14 in April 2025 to 10 in April 2026. Similarly, the number of injuries fell by 50%, from 6 to 3, contributing to a safer period year-over-year.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

3

Motorists Injured

Prior: 6-50.0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-04-01 to 2026-04-30 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal distribution of crashes showed some shifts year-over-year. While Wednesday remained a peak day, its crash count decreased from 4 in April 2025 to 3 in April 2026, and Thursday's peak count of 4 crashes dropped to 1. The peak crash hour shifted from 7 PM in April 2025 to 4 PM in April 2026, with both hours recording 2 crashes.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-04-01 to 2026-04-30 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-04-01 to 2026-04-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Fatal crashes remained at zero in both April 2025 and April 2026. Total injuries decreased from 6 in the prior period to 3 in the current period. Crashes resulting in minor injuries decreased from 3 to 2, while crashes with no injuries decreased from 10 to 7, maintaining similar proportions of the total crashes in both periods.

Outcome by Severity (Crash Events)

Minor Injury2minor injury crashes20%
-33.3%prior 3
No Injury7no injury crashes70%
-30.0%prior 10

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-04-01 to 2026-04-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-04-01 to 2026-04-30 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factors changed significantly year-over-year. 'No improper driving' crashes decreased from 5 in April 2025 to 1 in April 2026. Conversely, 'Operating vehicle in erratic, reckless, careless, negligent or aggressive manner' emerged as a factor with 2 crashes in April 2026, not listed among the top factors in April 2025. 'Followed too closely' crashes, which accounted for 3 incidents in the prior period, were not a top factor in the current period.

Officer-Reported Primary Contributing Cause

Operating vehicle in erratic, reckless, careless, negligent or aggressive manner2 (20%)
Inattention1 (10%)
Failed to yield right of way1 (10%)
Over-correcting/over-steering1 (10%)
No improper driving1 (10%)-80.0%prior 5
Glare1 (10%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-04-01 to 2026-04-30 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in clear weather conditions increased from 8 in April 2025 to 9 in April 2026, while adverse weather crashes decreased from 6 to 1. Similarly, crashes on dry road surfaces remained at 9, but crashes on wet surfaces decreased from 3 to 1, and no crashes occurred on snowy or standing water surfaces in the current period, unlike the prior period. Crashes during daylight decreased from 9 to 6, while crashes in unlighted dark conditions remained at 3.

Weather

Clear5 (50.0%)
Clear/Clear3 (30.0%)
-40.0%prior 5
Clear/Unknown1 (10.0%)
Rain1 (10.0%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-04-01 to 2026-04-30 · Weather condition at time of crash

Lighting

Daylight6 (60.0%)
-33.3%prior 9
Dark - roadway not lighted3 (30.0%)
Dark - lighted roadway1 (10.0%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-04-01 to 2026-04-30 · Lighting condition field

Road Surface

Dry9 (90.0%)
0.0%prior 9
Wet1 (10.0%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-04-01 to 2026-04-30 · Road surface condition field

Vehicles & Demographics

Top Vehicle Makes (15 vehicles)

1
FORD3 (20%)
2
TOYOTA3 (20%)
-40.0%prior 5
3
SUBARU2 (13.3%)
4
JEEP1 (6.7%)
5
CHEVROLET1 (6.7%)
6
MITS1 (6.7%)
7
NISSAN1 (6.7%)
8
KIA1 (6.7%)
9
HONDA1 (6.7%)
10
HYUNDAI1 (6.7%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-04-01 to 2026-04-30 · Vehicle unit records

Sex Distribution (22 persons with recorded sex)

Female13 (59.1%)
18.2%prior 11
Male9 (40.9%)
-57.1%prior 21

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-04-01 to 2026-04-30 · Person-level records linked to crash events

Speed Limit Zones

There was a notable shift in the distribution of crashes across speed zones. Crashes in 55 mph zones decreased significantly from 6 in April 2025 to 3 in April 2026. Conversely, crashes in lower speed zones increased, with 20 mph zones seeing an increase from 1 to 2 crashes, and 25 mph and 30 mph zones appearing with 1 and 2 crashes respectively in the current period, where they were not present in the prior period's top counts. Fatal crashes remained at zero across all speed zones in both periods.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2026-04-01 to 2026-04-30 · 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: 2026-04-01 through 2026-04-30
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2026-04-01 through 2026-04-30 (30 days)
  • Geographic scope: HARVARD, MA
  • Total crash records analyzed: 10
  • Total persons involved: 22
  • Total vehicles involved: 15

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). "HARVARD, MA Crash Intelligence Report: April 2026." Published June 21, 2026. Reporting period: 2026-04-01 to 2026-04-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/harvard/april-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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Harvard, MA Crash Report — April 2026 | ThatCarHitMe.com