Monthly Traffic Safety Analysis

368 CRASHES IN
WORCESTER, MA
JUNE 2022

All metrics benchmarked againstJune 2021

Total crashes in WORCESTER increased by 2.8% from 358 in June 2021 to 368 in June 2022. A significant year-over-year shift was observed in total fatalities, which rose from 0 to 1 during this period.

368

2.8%was 358

Total Crash Events

1

Persons Killed

124

20.4%was 103

Persons Injured

60

-3.2%was 62

Hit-and-Run Crashes

Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 58 crashes with unreported severity are not shown in the severity breakdown.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-06-01 to 2022-06-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall crash incidents in WORCESTER showed an upward trend year-over-year, with total crashes rising from 358 to 368. This period also experienced an increase in total fatalities from 0 to 1, and total injuries increased from 103 to 124.

60

Hit-and-Run Crashes — June 2022

-3.2% vs prior (62)

The number of hit-and-run crashes decreased from 62 in the prior period to 60 in the current period. Consequently, the hit-and-run rate also saw a slight decrease, moving from 17.3% to 16.3% of all crashes.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 0%

0

Cyclists Killed

Prior: 00.0%

0

Motorists Killed

Prior: 00.0%

2

Pedestrians Injured

Prior: 6-66.7%

3

Cyclists Injured

Prior: 1200.0%

119

Motorists Injured

Prior: 9624.0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-06-01 to 2022-06-30 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The peak day for crashes remained Wednesday, increasing from 62 crashes in the prior period to 75 crashes in the current period. However, the peak crash hour shifted from 1p with 31 crashes in the prior period to 4p with 40 crashes in the current period, indicating a change in daily crash patterns.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-06-01 to 2022-06-30 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-06-01 to 2022-06-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The current period recorded 1 fatal crash (0.3% of total crashes), compared to 0 fatal crashes in the prior period. Serious injury crashes decreased from 5 (1.4%) to 2 (0.5%), while minor injury crashes increased from 39 (10.9%) to 54 (14.7%). Crashes resulting in no injury also increased from 205 (57.3%) to 220 (59.8%).

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.3%
Serious Injury2serious injury crashes0.5%
-60.0%prior 5
Minor Injury54minor injury crashes14.7%
38.5%prior 39
Possible Injury33possible injury crashes9%
10.0%prior 30
No Injury220no injury crashes59.8%
7.3%prior 205

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-06-01 to 2022-06-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-06-01 to 2022-06-30 · Most severe injury per crash record

Top Contributing Factors

Crashes attributed to 'No improper driving' increased slightly from 125 to 129 year-over-year. A notable increase was observed in 'Followed too closely' crashes, rising from 18 to 29 incidents. Conversely, crashes linked to 'Inattention' significantly decreased from 32 to 7.

Officer-Reported Primary Contributing Cause

No improper driving129 (35.1%)3.2%prior 125
Failed to yield right of way32 (8.7%)14.3%prior 28
Followed too closely29 (7.9%)61.1%prior 18
Disregarded traffic signs, signals, road markings9 (2.4%)0.0%prior 9
Failure to keep in proper lane or running off road9 (2.4%)-35.7%prior 14
Inattention7 (1.9%)-78.1%prior 32
Exceeded authorized speed limit6 (1.6%)
Fatigued/asleep5 (1.4%)
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner4 (1.1%)-20.0%prior 5
Other improper action4 (1.1%)-66.7%prior 12

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-06-01 to 2022-06-30 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in clear weather conditions increased from 283 to 302, while those in rainy conditions decreased from 24 to 13. Crashes during daylight hours decreased from 282 to 269, but crashes in dark, lighted roadway conditions increased from 48 to 74. Crashes on dry road surfaces increased from 314 to 337, while those on wet surfaces decreased from 29 to 21.

Weather

Clear248 (68.9%)
2.9%prior 241
Clear/Clear54 (15.0%)
28.6%prior 42
Cloudy29 (8.1%)
7.4%prior 27
Clear/Cloudy11 (3.1%)
83.3%prior 6
Cloudy/Rain5 (1.4%)
-28.6%prior 7
Rain4 (1.1%)
-60.0%prior 10
Rain/Cloudy3 (0.8%)
Cloudy/Cloudy2 (0.6%)
Clear/Rain1 (0.3%)
Cloudy/Clear1 (0.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-06-01 to 2022-06-30 · Weather condition at time of crash

Lighting

Daylight269 (74.1%)
-4.6%prior 282
Dark - lighted roadway74 (20.4%)
54.2%prior 48
Dusk8 (2.2%)
60.0%prior 5
Dark - unknown roadway lighting4 (1.1%)
Dawn4 (1.1%)
-20.0%prior 5
Dark - roadway not lighted4 (1.1%)
-20.0%prior 5

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-06-01 to 2022-06-30 · Lighting condition field

Road Surface

Dry337 (93.9%)
7.3%prior 314
Wet21 (5.8%)
-27.6%prior 29
Sand, mud, dirt, oil, gravel1 (0.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-06-01 to 2022-06-30 · Road surface condition field

Vehicles & Demographics

The total number of persons involved in crashes increased from 853 to 871. Among age groups, those aged 26-34 saw the largest increase in involvement, rising from 131 to 149 persons, while the 16-20 age group decreased from 78 to 68. Toyota remained the most frequently involved vehicle make, increasing from 110 to 139 vehicles, with Honda also seeing a notable increase from 59 to 82 vehicles.

Top Vehicle Makes (724 vehicles)

1
TOYOTA139 (19.2%)
26.4%prior 110
2
HONDA82 (11.3%)
39.0%prior 59
3
FORD68 (9.4%)
-12.8%prior 78
4
CHEVROLET48 (6.6%)
-7.7%prior 52
5
NISSAN43 (5.9%)
-18.9%prior 53
6
JEEP34 (4.7%)
36.0%prior 25
7
SUBARU29 (4%)
11.5%prior 26
8
HYUNDAI25 (3.5%)
0.0%prior 25
9
KIA24 (3.3%)
100.0%prior 12
10
VOLKSWAGEN16 (2.2%)
6.7%prior 15

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-06-01 to 2022-06-30 · Vehicle unit records

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

Sex Distribution (699 persons with recorded sex)

Male376 (53.8%)
-5.3%prior 397
Female322 (46.1%)
16.2%prior 277
X / Unspecified1 (0.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-06-01 to 2022-06-30 · Person-level records linked to crash events

Speed Limit Zones

Crashes occurring in 30 mph speed zones decreased from 109 to 71 incidents year-over-year. Incidents in 50 mph zones also saw a decrease from 33 to 26 crashes. Conversely, crashes in 65 mph speed zones increased from 6 to 16 incidents.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-06-01 to 2022-06-30 · 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: 2022-06-01 through 2022-06-30
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2022-06-01 through 2022-06-30 (30 days)
  • Geographic scope: WORCESTER, MA
  • Total crash records analyzed: 368
  • Total persons involved: 871
  • Total vehicles involved: 724

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: June 2022." Published June 21, 2026. Reporting period: 2022-06-01 to 2022-06-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/worcester/june-2022-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 — June 2022 | ThatCarHitMe.com