Yearly Traffic Safety Analysis

489 CRASHES IN
DRACUT, MA
2022

All metrics benchmarked against2021

In 2022, Dracut recorded 489 total traffic crashes, an increase of 9.6% from the 446 crashes reported in 2021. While the number of fatalities remained stable at one death in each period, total injuries rose from 157 to 168. One of the most significant changes was the 67.6% increase in crashes attributed to a driver failing to yield the right of way, which rose from 34 incidents in 2021 to 57 in 2022.

489

9.6%was 446

Total Crash Events

1

Persons Killed

168

7.0%was 157

Persons Injured

25

47.1%was 17

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. 20 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

Traffic crashes in Dracut trended upward from 2021 to 2022. The total number of crashes increased by 9.6%, from 446 to 489. Similarly, the number of people injured in these incidents rose by 7.0%, from 157 to 168, while the number of fatalities held steady at one for both years.

25

Hit-and-Run Crashes — 2022

47.1% vs prior (17)

Hit-and-run incidents increased notably from 2021 to 2022. The number of hit-and-run crashes rose from 17 to 25, a 47.1% increase in count. This caused the hit-and-run rate, which measures the proportion of all crashes that are hit-and-runs, to climb from 3.8% in 2021 to 5.1% in 2022.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

1

Motorists Killed

Prior: 10.0%

4

Pedestrians Injured

Prior: 6-33.3%

164

Motorists Injured

Prior: 1509.3%

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 timing of crashes showed notable shifts between the two periods. The peak day for crashes moved from Saturday (92 crashes) in 2021 to Friday (83 crashes) in 2022, with Saturday crashes decreasing significantly. While the 5 PM hour was a peak time in both years, 2022 saw a more concentrated afternoon rush hour period, with both the 4 PM and 5 PM hours recording 50 crashes each, a substantial increase from the 29 and 33 crashes seen in those respective hours 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

Crash severity distributions showed some changes between 2021 and 2022. The number of fatal crashes remained constant at one incident in each year, resulting in a slight decrease in the fatal crash rate from 0.22% to 0.20% of all crashes. The count of serious injury crashes rose from 14 to 17, while the proportion of crashes with no injuries increased from 68.6% in 2021 to 71.4% in 2022.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.2%
0.0%prior 1
Serious Injury17serious injury crashes3.5%
21.4%prior 14
Minor Injury74minor injury crashes15.1%
7.2%prior 69
Possible Injury28possible injury crashes5.7%
-26.3%prior 38
No Injury349no injury crashes71.4%
14.1%prior 306

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 cited in crashes remained consistent year-over-year, though their counts changed. "Inattention" remained a top factor, with its incident count increasing from 78 in 2021 to 90 in 2022. The most significant change was in crashes attributed to "Failed to yield right of way," which surged by 67.6% from a count of 34 incidents to 57. Conversely, crashes involving "Distracted" driving as a primary factor decreased from a count of 17 in 2021 to 10 in 2022.

Officer-Reported Primary Contributing Cause

No improper driving133 (27.2%)8.1%prior 123
Inattention90 (18.4%)15.4%prior 78
Failed to yield right of way57 (11.7%)67.6%prior 34
Failure to keep in proper lane or running off road23 (4.7%)-4.2%prior 24
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner18 (3.7%)5.9%prior 17
Other improper action14 (2.9%)40.0%prior 10
Followed too closely13 (2.7%)-13.3%prior 15
Distracted10 (2%)-41.2%prior 17
Glare10 (2%)
Fatigued/asleep10 (2%)-16.7%prior 12

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 prevalence of crashes in adverse weather and road conditions increased from 2021 to 2022. Crashes on wet road surfaces rose from 52 to 77, and incidents on roads with snow or ice increased from 18 to 34. Correspondingly, crashes during snowy weather increased from 3 to 15 incidents. Despite these changes, the majority of crashes in both years occurred in clear weather and on dry roads.

Weather

Clear368 (75.6%)
4.5%prior 352
Rain37 (7.6%)
60.9%prior 23
Cloudy35 (7.2%)
16.7%prior 30
Snow15 (3.1%)
Cloudy/Rain12 (2.5%)
9.1%prior 11
Snow/Sleet, hail (freezing rain or drizzle)4 (0.8%)
Rain/Cloudy3 (0.6%)
-50.0%prior 6
Other1 (0.2%)
Cloudy/Snow1 (0.2%)
Cloudy/Sleet, hail (freezing rain or drizzle)1 (0.2%)

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

Lighting

Daylight298 (61.2%)
11.2%prior 268
Dark - lighted roadway110 (22.6%)
12.2%prior 98
Dark - roadway not lighted32 (6.6%)
-13.5%prior 37
Dark - unknown roadway lighting21 (4.3%)
-8.7%prior 23
Dusk15 (3.1%)
66.7%prior 9
Other6 (1.2%)
Dawn5 (1.0%)
-50.0%prior 10

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

Road Surface

Dry373 (76.6%)
0.0%prior 373
Wet77 (15.8%)
48.1%prior 52
Snow18 (3.7%)
100.0%prior 9
Ice16 (3.3%)
77.8%prior 9
Sand, mud, dirt, oil, gravel2 (0.4%)
Other1 (0.2%)

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

Vehicles & Demographics

The makes of vehicles involved in crashes remained broadly similar, with Toyota and Honda being the most frequent. In 2022, Toyota (142 vehicles) surpassed Honda (133 vehicles) as the most common make involved in crashes, reversing their 2021 ranking. The demographic profile of persons involved in crashes shifted, with the 26-34 age group's share increasing from 16.5% to 19.1% of all involved persons. Conversely, the representation of the 16-20 age group decreased from 12.7% to 10.1%.

Top Vehicle Makes (858 vehicles)

1
TOYOTA142 (16.6%)
18.3%prior 120
2
HONDA133 (15.5%)
9.0%prior 122
3
FORD84 (9.8%)
3.7%prior 81
4
CHEVROLET83 (9.7%)
23.9%prior 67
5
NISSAN60 (7%)
42.9%prior 42
6
JEEP33 (3.8%)
-5.7%prior 35
7
SUBARU27 (3.1%)
35.0%prior 20
8
HYUNDAI27 (3.1%)
3.8%prior 26
9
MAZDA24 (2.8%)
118.2%prior 11
10
VOLKSWAGEN23 (2.7%)
76.9%prior 13

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

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

Sex Distribution (958 persons with recorded sex)

Male539 (56.3%)
17.4%prior 459
Female419 (43.7%)
15.4%prior 363

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

The distribution of crashes across speed zones changed between the two periods. While the 30 mph zone was the site of the most crashes in both years, the number of incidents in 35 mph zones more than doubled, increasing from 25 in 2021 to 52 in 2022. In contrast, crashes in 45 mph zones decreased from 35 to 25. The single fatal crash in 2021 occurred in a 30 mph zone, whereas the 2022 fatal crash did not occur in a speed zone listed in the data.

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: DRACUT, MA
  • Total crash records analyzed: 489
  • Total persons involved: 1,046
  • Total vehicles involved: 858

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). "DRACUT, 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/dracut/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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Dracut, MA Crash Report — 2022 | ThatCarHitMe.com