Yearly Traffic Safety Analysis

4,763 CRASHES IN
WORCESTER, MA
2022

All metrics benchmarked against2021

In 2022, Worcester recorded 4,763 total vehicle crashes, a slight increase of approximately 1.0% from the 4,717 crashes documented in 2021. The most significant year-over-year change was a 66.7% rise in total fatalities, which increased from 9 in 2021 to 15 in 2022. This increase in deaths occurred even as the total number of non-fatal injuries decreased by 5.7% over the same period.

4,763

1.0%was 4,717

Total Crash Events

15

66.7%was 9

Persons Killed

1,284

-5.7%was 1,362

Persons Injured

908

2.9%was 882

Hit-and-Run Crashes

Note: "Persons Killed" (15) counts individual fatalities across all crash events. "Fatal" in the severity table below (13) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 883 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

The overall crash trend in Worcester was relatively stable year-over-year, with total collisions increasing by just under 1.0% from 4,717 in 2021 to 4,763 in 2022. However, the severity of these crashes worsened significantly. The number of fatalities rose by 66.7%, from 9 to 15, and the count of fatal crashes increased from 9 to 13.

908

Hit-and-Run Crashes — 2022

2.9% vs prior (882)

Hit-and-run incidents increased in both count and as a proportion of total crashes. The number of hit-and-run crashes rose from 882 in 2021 to 908 in 2022. This represents a rise in the hit-and-run rate from 18.7% to 19.1% of all crashes, indicating a slight upward trend.

Vulnerable Road User Casualties

7

Pedestrians Killed

Prior: 1600.0%

0

Cyclists Killed

Prior: 00.0%

8

Motorists Killed

Prior: 80.0%

0

Other Killed

Prior: 00.0%

54

Pedestrians Injured

Prior: 56-3.6%

13

Cyclists Injured

Prior: 1030.0%

1,210

Motorists Injured

Prior: 1,288-6.1%

7

Other Injured

Prior: 8-12.5%

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

Temporal crash patterns showed some shifts between the two years. The most frequent day for crashes moved from Thursday (731 crashes) in 2021 to Friday (745 crashes) in 2022. The peak hour for collisions also shifted two hours earlier, moving from 4 PM in 2021 (389 crashes) to 2 PM in 2022 (393 crashes).

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

The severity of crashes worsened in 2022 compared to 2021. The number of fatal crashes increased from 9 to 13, raising their share of all crashes from 0.2% to 0.3%. While the total number of crashes resulting in any injury (serious, minor, or possible) fell from 958 in 2021 to 899 in 2022, the proportion of crashes involving no injuries increased from 60.1% to 62.3%.

Severity is per crash event (most severe injury). 13 fatal crash events resulted in 15 persons killed.

Outcome by Severity (Crash Events)

Fatal13fatal crashes0.3%
44.4%prior 9
Serious Injury55serious injury crashes1.2%
-6.8%prior 59
Minor Injury468minor injury crashes9.8%
4.2%prior 449
Possible Injury376possible injury crashes7.9%
-16.4%prior 450
No Injury2,968no injury crashes62.3%
4.8%prior 2,833

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, 'No improper driving,' remained stable with 1,637 incidents in 2022 compared to 1,635 in 2021. However, the rankings of other top factors shifted, with crashes attributed to 'Followed too closely' increasing by 31.9% in count (from 232 to 306), becoming the second most common factor in 2022. Conversely, incidents involving 'Failed to yield right of way' decreased by 13.2% in count (from 333 to 289), dropping it from second to third place.

Officer-Reported Primary Contributing Cause

No improper driving1,637 (34.4%)0.1%prior 1,635
Followed too closely306 (6.4%)31.9%prior 232
Failed to yield right of way289 (6.1%)-13.2%prior 333
Inattention186 (3.9%)-30.1%prior 266
Disregarded traffic signs, signals, road markings149 (3.1%)-11.8%prior 169
Failure to keep in proper lane or running off road126 (2.6%)-13.1%prior 145
Other improper action70 (1.5%)-32.7%prior 104
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner57 (1.2%)-26.0%prior 77
Made an improper turn57 (1.2%)7.5%prior 53
Exceeded authorized speed limit56 (1.2%)55.6%prior 36

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

Crashes in 2022 were more likely to occur in clear and dry conditions compared to the prior year. Collisions on dry road surfaces increased from 3,604 to 3,736, while those on wet surfaces fell from 703 to 616. Similarly, crashes during daylight hours rose from 3,052 to 3,171, while the number of crashes reported during rain or snow conditions saw a decrease from the previous year.

Weather

Clear2,891 (62.4%)
7.3%prior 2,694
Clear/Clear601 (13.0%)
4.0%prior 578
Cloudy420 (9.1%)
2.9%prior 408
Rain211 (4.6%)
-20.4%prior 265
Cloudy/Rain121 (2.6%)
-10.4%prior 135
Snow72 (1.6%)
-36.8%prior 114
Clear/Cloudy53 (1.1%)
-23.2%prior 69
Rain/Cloudy49 (1.1%)
8.9%prior 45
Cloudy/Cloudy30 (0.6%)
-25.0%prior 40
Snow/Sleet, hail (freezing rain or drizzle)24 (0.5%)
33.3%prior 18

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Weather condition at time of crash

Lighting

Daylight3,171 (68.3%)
3.9%prior 3,052
Dark - lighted roadway1,200 (25.9%)
-5.1%prior 1,264
Dusk124 (2.7%)
-7.5%prior 134
Dark - roadway not lighted73 (1.6%)
21.7%prior 60
Dawn48 (1.0%)
4.3%prior 46
Dark - unknown roadway lighting23 (0.5%)
-8.0%prior 25
Other2 (0.0%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Lighting condition field

Road Surface

Dry3,736 (80.9%)
3.7%prior 3,604
Wet616 (13.3%)
-12.4%prior 703
Snow149 (3.2%)
-3.9%prior 155
Ice100 (2.2%)
33.3%prior 75
Slush8 (0.2%)
-38.5%prior 13
Sand, mud, dirt, oil, gravel4 (0.1%)
Water (standing, moving)3 (0.1%)
-66.7%prior 9
Other1 (0.0%)

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 three vehicle makes involved in crashes remained consistent year-over-year: Toyota, Honda, and Ford. The number of Toyotas and Hondas in crashes increased to 1,817 and 1,067 respectively, while Fords decreased slightly to 858. The age distribution of persons involved in crashes also showed little change, with the 26-34 age group remaining the largest demographic in both 2021 (1,911 individuals) and 2022 (1,989 individuals).

Top Vehicle Makes (9,381 vehicles)

1
TOYOTA1,817 (19.4%)
8.4%prior 1,676
2
HONDA1,067 (11.4%)
10.8%prior 963
3
FORD858 (9.1%)
-4.8%prior 901
4
CHEVROLET618 (6.6%)
2.8%prior 601
5
NISSAN605 (6.4%)
-3.0%prior 624
6
SUBARU431 (4.6%)
13.4%prior 380
7
JEEP371 (4%)
-8.2%prior 404
8
HYUNDAI352 (3.8%)
-1.7%prior 358
9
DODGE194 (2.1%)
10.2%prior 176
10
KIA187 (2%)
-1.6%prior 190

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Vehicle unit records

2,239 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (8,709 persons with recorded sex)

Male4,840 (55.6%)
0.2%prior 4,828
Female3,863 (44.4%)
2.2%prior 3,778
X / Unspecified6 (0.1%)
100.0%prior 3

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 in 30 mph speed zones, the most frequent location for collisions, decreased from 992 in 2021 to 887 in 2022; however, these zones recorded two fatal crashes in 2022 after having none the prior year. Collisions in higher speed zones saw an increase, with crashes in 50 mph zones rising from 323 to 340 and those in 65 mph zones increasing from 106 to 124. The number of fatalities in the 50 mph and 65 mph zones remained constant at one fatality each per year.

Fatal crashes by zone: 30 mph: 2 of 887 (0.225%) · 50 mph: 1 of 340 (0.294%) · 65 mph: 1 of 124 (0.806%)

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: WORCESTER, MA
  • Total crash records analyzed: 4,763
  • Total persons involved: 11,405
  • Total vehicles involved: 9,381

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: 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/worcester/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

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Worcester, MA Crash Report — 2022 | ThatCarHitMe.com