Yearly Traffic Safety Analysis

5,298 CRASHES IN
WORCESTER, MA
2025

All metrics benchmarked against2024

In 2025, Worcester recorded 5,298 total vehicle crashes, a 6.4% decrease from the 5,662 crashes reported in 2024. This year-over-year comparison shows a general decline in crash incidents, with total injuries and fatalities also decreasing. One of the most notable changes was a significant shift in the location of crashes, with the majority moving from 30 mph zones in 2024 to 25 mph zones in 2025.

5,298

▼ -6.4%was 5,662

Total Crash Events

▼ -22.2%was 9

Persons Killed

1,619

▼ -3.5%was 1,677

Persons Injured

923

▼ -16.1%was 1,100

Hit-and-Run Crashes

Note: "Persons Killed" (7) counts individual fatalities across all crash events. "Fatal" in the severity table below (7) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 437 crashes with unreported severity are not shown in the severity breakdown.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, traffic crashes in Worcester showed a downward trend in 2025 compared to the previous year. Total crashes decreased by 6.4%, from 5,662 in 2024 to 5,298 in 2025. This trend extended to crash outcomes, with total injuries falling by 3.5% from 1,677 to 1,619, and total fatalities decreasing from 9 to 7 year-over-year.

923

Hit-and-Run Crashes — 2025

▼ -16.1% vs prior (1,100)

There was a positive trend regarding hit-and-run incidents, which decreased in both count and rate. The number of hit-and-run crashes fell by 16.1%, from 1,100 in 2024 to 923 in 2025. Consequently, the hit-and-run rate, which represents the proportion of all crashes that are hit-and-runs, decreased from 19.4% in 2024 to 17.4% in 2025.

Vulnerable Road User Casualties

Pedestrians Killed

Prior: 2↑50.0%

Cyclists Killed

Prior: 0→0.0%

Motorists Killed

Prior: 7↓-42.9%

Other Killed

Prior: 0→0.0%

Pedestrians Injured

Prior: 104↓-11.5%

Cyclists Injured

Prior: 42↑19.0%

1,462

Motorists Injured

Prior: 1,516↓-3.6%

Other Injured

Prior: 15→0.0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes saw some shifts between 2024 and 2025. The peak day for crashes moved from Friday (914 crashes) in 2024 to Wednesday (843 crashes) in 2025. The evening commute remained the most frequent time for incidents, with the peak hour in both years occurring in the late afternoon. In 2024, the peak was the 5 PM hour with 504 crashes, while in 2025, the 4 PM and 5 PM hours tied for the peak with 426 crashes each.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While the total number of crashes decreased, the severity profile shifted slightly. The rate of fatal crashes per incident declined from 0.16% in 2024 to 0.13% in 2025. However, the proportion of crashes involving a serious injury increased from 1.78% to 1.94%. Overall, the percentage of crashes resulting in any level of injury (from possible to fatal) rose from 21.9% in 2024 to 22.8% in 2025, even as the absolute number of injuries and fatalities fell.

Outcome by Severity (Crash Events)

Fatal7fatal crashes0.1%

↓-22.2%prior 9

Serious Injury103serious injury crashes1.9%

↑2.0%prior 101

Minor Injury626minor injury crashes11.8%

↓-0.5%prior 629

Possible Injury471possible injury crashes8.9%

↓-5.6%prior 499

No Injury3,654no injury crashes69%

↓-2.1%prior 3,733

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factors for crashes saw notable shifts in 2025. While 'No improper driving' remained the most common finding, its count decreased from 1,979 in 2024 to 1,788 in 2025. The count of crashes attributed to 'Failed to yield right of way' increased by 16.9%, rising from 313 to 366 incidents. 'Followed too closely' saw a significant 31.7% increase in count, from 224 to 295 incidents, moving it from the fourth to the third most frequent factor. Conversely, crashes attributed to 'Inattention' dropped by 34.8% in count, falling from 233 to 152.

Officer-Reported Primary Contributing Cause

No improper driving1,788 (33.7%)↓-9.7%prior 1,979

Failed to yield right of way366 (6.9%)↑16.9%prior 313

Followed too closely295 (5.6%)↑31.7%prior 224

Disregarded traffic signs, signals, road markings237 (4.5%)↑13.9%prior 208

Inattention152 (2.9%)↓-34.8%prior 233

Failure to keep in proper lane or running off road138 (2.6%)↑2.2%prior 135

Other improper action105 (2%)↑56.7%prior 67

Exceeded authorized speed limit72 (1.4%)↑1.4%prior 71

Driving too fast for conditions68 (1.3%)↑61.9%prior 42

Made an improper turn66 (1.2%)↓-1.5%prior 67

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes in both periods predominantly occurred in clear conditions on dry roads during daylight hours. In 2025, 67.6% of crashes happened in daylight, a slight increase in proportion from 65.6% in 2024. The proportion of crashes on dry roads remained stable at approximately 77% for both years. However, there was a noticeable increase in the share of crashes occurring on snow or ice, which accounted for 6.9% of all crashes in 2025, up from 5.9% in the prior year.

Weather

Clear2,676 (51.6%)

↓-16.8%prior 3,218

Clear/Clear1,175 (22.6%)

↑48.4%prior 792

Cloudy342 (6.6%)

↓-20.1%prior 428

Rain204 (3.9%)

↓-22.1%prior 262

Cloudy/Rain133 (2.6%)

↓-16.4%prior 159

Snow94 (1.8%)

↓-9.6%prior 104

Clear/Cloudy84 (1.6%)

↓-15.2%prior 99

Rain/Cloudy68 (1.3%)

↑41.7%prior 48

Cloudy/Cloudy63 (1.2%)

↑70.3%prior 37

Rain/Rain55 (1.1%)

↓-9.8%prior 61

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Weather condition at time of crash

Lighting

Daylight3,580 (69.4%)

↓-3.7%prior 3,716

Dark - lighted roadway1,247 (24.2%)

↓-13.0%prior 1,433

Dusk136 (2.6%)

↓-5.6%prior 144

Dark - roadway not lighted88 (1.7%)

↑6.0%prior 83

Dawn74 (1.4%)

↓-14.0%prior 86

Dark - unknown roadway lighting31 (0.6%)

↓-34.0%prior 47

Other2 (0.0%)

Reported but invalid1 (0.0%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Lighting condition field

Road Surface

Dry4,109 (79.7%)

↓-5.1%prior 4,332

Wet643 (12.5%)

↓-17.1%prior 776

Snow200 (3.9%)

↓-14.2%prior 233

Ice167 (3.2%)

↑65.3%prior 101

Slush23 (0.4%)

↓-30.3%prior 33

Sand, mud, dirt, oil, gravel4 (0.1%)

Water (standing, moving)4 (0.1%)

Other3 (0.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Road surface condition field

Vehicles & Demographics

The composition of vehicles and persons involved in crashes remained largely consistent year-over-year. The top three vehicle makes involved in crashes were Toyota, Honda, and Ford in both 2025 and 2024, with their rankings unchanged and counts decreasing in line with the overall trend. The demographic profile of persons involved in crashes also showed little change, with the proportional representation across different age groups, such as the 26-34 and 65+ brackets, remaining stable between the two periods.

Top Vehicle Makes (10,500 vehicles)

TOYOTA2,098 (20%)

↓-6.0%prior 2,231

HONDA1,285 (12.2%)

↓-2.4%prior 1,316

FORD927 (8.8%)

↓-9.7%prior 1,027

CHEVROLET647 (6.2%)

↑1.7%prior 636

NISSAN585 (5.6%)

↓-11.4%prior 660

SUBARU516 (4.9%)

↓-5.1%prior 544

JEEP439 (4.2%)

↓-3.7%prior 456

HYUNDAI349 (3.3%)

↓-17.9%prior 425

GMC220 (2.1%)

↑2.3%prior 215

MERCEDES-BENZ198 (1.9%)

↓-0.5%prior 199

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Vehicle unit records

2,216 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (10,774 persons with recorded sex)

Male6,124 (56.8%)

↓-2.1%prior 6,253

Female4,631 (43.0%)

↓-5.3%prior 4,889

X / Unspecified19 (0.2%)

↑18.8%prior 16

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Person-level records linked to crash events

Speed Limit Zones

A significant shift occurred in the speed zones where crashes were reported. In 2024, the majority of incidents (2,930 crashes) occurred in 30 mph zones. In 2025, this shifted dramatically, with 25 mph zones accounting for the highest number of crashes at 3,697. Fatal crashes also reflect this change; in 2024, five fatalities occurred in 30 mph zones, whereas in 2025, fatalities were split between 25 mph zones (2 fatalities) and 30 mph zones (4 fatalities).

Fatal crashes by zone: 25 mph: 2 of 3,697 (0.054%) · 30 mph: 4 of 605 (0.661%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · 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: 2025-01-01 through 2025-12-31
Report generated: June 21, 2026

Data Coverage

Reporting period: 2025-01-01 through 2025-12-31 (365 days)
Geographic scope: WORCESTER, MA
Total crash records analyzed: 5,298
Total persons involved: 13,046
Total vehicles involved: 10,500

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). "WORCESTER, MA Crash Intelligence Report: 2025." Published June 21, 2026. Reporting period: 2025-01-01 to 2025-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/worcester/2025-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

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5,298 CRASHES INWORCESTER, MA2025