Yearly Traffic Safety Analysis

1,050 CRASHES IN
FITCHBURG, MA
2022

All metrics benchmarked against2021

In 2022, Fitchburg recorded 1,050 total vehicle crashes, a notable 18.7% decrease from the 1,292 crashes documented in 2021. While the number of fatalities remained constant at one death in each period, the total number of injuries saw a significant year-over-year reduction of 27.5%, falling from 295 to 214.

1,050

-18.7%was 1,292

Total Crash Events

1

Persons Killed

214

-27.5%was 295

Persons Injured

12

-25.0%was 16

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. 84 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 trend in traffic incidents shows a significant year-over-year improvement. Total crashes decreased by 18.7%, from 1,292 in 2021 to 1,050 in 2022. Similarly, the number of people injured in these incidents fell by 27.5%, while the number of fatalities held steady at one for both years.

12

Hit-and-Run Crashes — 2022

-25.0% vs prior (16)

The number of hit-and-run incidents decreased from 16 in 2021 to 12 in 2022, a 25% reduction. The hit-and-run rate, which measures the proportion of total crashes that were hit-and-runs, also saw a slight downward trend, moving from 1.2% to 1.1% year-over-year.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

0

Cyclists Killed

Prior: 00.0%

1

Motorists Killed

Prior: 10.0%

0

Other Killed

Prior: 00.0%

8

Pedestrians Injured

Prior: 10-20.0%

3

Cyclists Injured

Prior: 4-25.0%

202

Motorists Injured

Prior: 281-28.1%

1

Other Injured

Prior: 0%

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

The time of day when crashes most frequently occurred remained consistent, with the 4 p.m. hour being the peak for both 2022 (97 crashes) and 2021 (113 crashes). However, the peak day for crashes shifted from Wednesday (212 crashes) in the prior year to Saturday (174 crashes) in the current year. Crash volumes on the peak day were lower in 2022 compared to the peak day in 2021.

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

While the absolute number of fatal crashes remained unchanged at one for both 2022 and 2021, the fatal crash rate increased from 0.08% to 0.1% due to the lower overall crash total in 2022. The proportion of crashes resulting in no injuries increased from 74.1% to 77.0% year-over-year. Crashes involving minor injuries fell from 9.6% of the total to 8.9%, and possible injury crashes dropped from a 6.5% share to 4.6%.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.1%
0.0%prior 1
Serious Injury15serious injury crashes1.4%
-6.3%prior 16
Minor Injury93minor injury crashes8.9%
-25.0%prior 124
Possible Injury48possible injury crashes4.6%
-42.9%prior 84
No Injury809no injury crashes77%
-15.5%prior 957

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 factors remained broadly similar year-over-year, with 'Inattention' being the most cited driver error in both periods, though its count decreased by 6.0% from 168 to 158. 'Failed to yield right of way' saw a significant 23.1% reduction in count, falling from 108 incidents in 2021 to 83 in 2022. Another notable change was the 55.9% drop in crashes attributed to 'Disregarded traffic signs, signals, road markings,' which decreased from 34 to 15.

Officer-Reported Primary Contributing Cause

No improper driving273 (26%)-13.6%prior 316
Inattention158 (15%)-6.0%prior 168
Followed too closely94 (9%)-12.1%prior 107
Failed to yield right of way83 (7.9%)-23.1%prior 108
Other improper action82 (7.8%)-24.1%prior 108
Failure to keep in proper lane or running off road40 (3.8%)-23.1%prior 52
Made an improper turn20 (1.9%)33.3%prior 15
Driving too fast for conditions19 (1.8%)0.0%prior 19
Disregarded traffic signs, signals, road markings15 (1.4%)-55.9%prior 34
Distracted14 (1.3%)-33.3%prior 21

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

The proportion of crashes occurring in clear weather increased from 68.3% in 2021 to 73.0% in 2022. Conversely, the share of crashes happening during daylight hours decreased slightly from 71.4% to 69.0%. Crashes on non-dry road surfaces (such as wet, ice, or snow) represented a larger share of the total in 2022, rising to 26.3% from 21.8% in the previous year.

Weather

Clear766 (73.9%)
-13.3%prior 883
Rain62 (6.0%)
-20.5%prior 78
Cloudy60 (5.8%)
-42.9%prior 105
Snow51 (4.9%)
21.4%prior 42
Cloudy/Rain18 (1.7%)
5.9%prior 17
Sleet, hail (freezing rain or drizzle)13 (1.3%)
-18.8%prior 16
Snow/Sleet, hail (freezing rain or drizzle)11 (1.1%)
37.5%prior 8
Clear/Other8 (0.8%)
-66.7%prior 24
Cloudy/Other7 (0.7%)
-30.0%prior 10
Cloudy/Snow7 (0.7%)
-22.2%prior 9

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

Lighting

Daylight724 (69.9%)
-21.6%prior 923
Dark - lighted roadway200 (19.3%)
-16.7%prior 240
Dark - roadway not lighted73 (7.0%)
1.4%prior 72
Dusk19 (1.8%)
58.3%prior 12
Dawn11 (1.1%)
-31.3%prior 16
Dark - unknown roadway lighting9 (0.9%)
-25.0%prior 12

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

Road Surface

Dry774 (74.4%)
-23.4%prior 1,010
Wet137 (13.2%)
-4.9%prior 144
Ice65 (6.2%)
16.1%prior 56
Snow53 (5.1%)
-3.6%prior 55
Slush8 (0.8%)
-11.1%prior 9
Sand, mud, dirt, oil, gravel3 (0.3%)
Other1 (0.1%)

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 across both years: Toyota, Honda, and Ford, although their absolute numbers decreased in line with the overall reduction in collisions. In 2022, Toyota was the most common make with 310 vehicles involved, down from 362 in 2021. The 26-34 age group consistently represented the largest segment of individuals involved in crashes, accounting for 19.0% of persons in 2022 (422 people) and 19.3% in 2021 (536 people).

Top Vehicle Makes (1,862 vehicles)

1
TOYOTA310 (16.6%)
-14.4%prior 362
2
FORD222 (11.9%)
-14.6%prior 260
3
HONDA216 (11.6%)
-25.5%prior 290
4
CHEVROLET148 (7.9%)
-22.9%prior 192
5
NISSAN139 (7.5%)
-8.6%prior 152
6
SUBARU107 (5.7%)
-14.4%prior 125
7
HYUNDAI100 (5.4%)
-11.5%prior 113
8
JEEP93 (5%)
-5.1%prior 98
9
DODGE50 (2.7%)
-37.5%prior 80
10
GMC49 (2.6%)
-23.4%prior 64

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

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

Sex Distribution (1,978 persons with recorded sex)

Male1,045 (52.8%)
-19.5%prior 1,298
Female933 (47.2%)
-19.2%prior 1,155

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 25 mph zones were the most frequent in both periods, accounting for 471 incidents in 2022 and 571 in 2021. The single fatal crash in 2022 occurred in a 55 mph zone, a shift from 2021 when the fatality was recorded in a 30 mph zone. There was a notable increase in crashes within 55 mph zones, which rose from 41 in 2021 to 61 in 2022.

Fatal crashes by zone: 55 mph: 1 of 61 (1.639%)

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: FITCHBURG, MA
  • Total crash records analyzed: 1,050
  • Total persons involved: 2,218
  • Total vehicles involved: 1,862

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). "FITCHBURG, 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/fitchburg/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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Fitchburg, MA Crash Report — 2022 | ThatCarHitMe.com