Yearly Traffic Safety Analysis

508 CRASHES IN
WILMINGTON, MA
2022

All metrics benchmarked against2021

In 2022, Wilmington recorded 508 total crashes, a 4.0% decrease from the 529 crashes in 2021. Despite the overall drop in collisions, the number of fatalities doubled from 2 in 2021 to 4 in 2022. The most significant year-over-year shift was in hit-and-run incidents, which increased by 78.9% from 19 to 34.

508

-4.0%was 529

Total Crash Events

4

100.0%was 2

Persons Killed

147

Persons Injured

34

78.9%was 19

Hit-and-Run Crashes

Note: "Persons Killed" (4) counts individual fatalities across all crash events. "Fatal" in the severity table below (4) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 11 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

Overall traffic crashes in Wilmington saw a slight decline, falling by 4.0% from 529 in 2021 to 508 in 2022. While the total number of injuries remained unchanged at 147 for both years, fatalities increased from 2 to 4. This indicates a stable trend in injury-related incidents but a rise in the most severe outcomes.

34

Hit-and-Run Crashes — 2022

78.9% vs prior (19)

Hit-and-run incidents increased significantly in 2022 compared to the previous year. The total count of hit-and-run crashes rose by 78.9%, from 19 in 2021 to 34 in 2022. Consequently, the hit-and-run rate, representing the proportion of all crashes that were hit-and-runs, increased from 3.6% to 6.7%, indicating a clear upward trend.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

0

Cyclists Killed

Prior: 00.0%

4

Motorists Killed

Prior: 2100.0%

1

Pedestrians Injured

Prior: 4-75.0%

1

Cyclists Injured

Prior: 2-50.0%

145

Motorists Injured

Prior: 1412.8%

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 temporal patterns of crashes showed some shifts between 2021 and 2022. The peak day for crashes moved from Thursday (94 crashes) in 2021 to Friday (89 crashes) in 2022. Similarly, the peak hour for collisions shifted an hour earlier, from 3 p.m. in 2021 (42 crashes) to 2 p.m. in 2022 (51 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 the prior year. The number of fatal crashes doubled from 2 to 4, increasing their share of all crashes from 0.4% to 0.8%. The proportion of serious injury crashes also rose from 1.7% (9 crashes) to 2.4% (12 crashes), and minor injury crashes increased from 13.4% (71 crashes) to 15.4% (78 crashes). Crashes resulting in possible injury or no injury saw a slight decrease in their respective proportions.

Outcome by Severity (Crash Events)

Fatal4fatal crashes0.8%
100.0%prior 2
Serious Injury12serious injury crashes2.4%
33.3%prior 9
Minor Injury78minor injury crashes15.4%
9.9%prior 71
Possible Injury25possible injury crashes4.9%
-39.0%prior 41
No Injury378no injury crashes74.4%
-5.3%prior 399

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 consistent in ranking, with 'No improper driving' and 'Inattention' being the top two in both 2021 and 2022, although their counts decreased by 10.7% and 20.4% respectively. Notably, crashes attributed to 'Followed too closely' increased by 26.7%, rising from 45 incidents in 2021 to 57 in 2022. Conversely, crashes involving 'Failed to yield right of way' saw a 27.5% reduction in count, dropping from 40 to 29.

Officer-Reported Primary Contributing Cause

No improper driving117 (23%)-10.7%prior 131
Inattention74 (14.6%)-20.4%prior 93
Followed too closely57 (11.2%)26.7%prior 45
Driving too fast for conditions32 (6.3%)18.5%prior 27
Failed to yield right of way29 (5.7%)-27.5%prior 40
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner24 (4.7%)9.1%prior 22
Failure to keep in proper lane or running off road23 (4.5%)4.5%prior 22
Distracted18 (3.5%)-18.2%prior 22
Fatigued/asleep13 (2.6%)8.3%prior 12
Other improper action12 (2.4%)-36.8%prior 19

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

Crash conditions remained broadly similar between 2021 and 2022, with the majority of incidents in both years occurring in daylight (66.0% vs 67.9%) and on dry roads (73.9% vs 73.2%). The most notable shift was in crashes occurring during snowy conditions. Crashes where snow was the primary weather condition increased from 12 to 22, and those on snow-covered road surfaces more than doubled, rising from 15 in 2021 to 32 in 2022.

Weather

Clear307 (61.2%)
-4.4%prior 321
Cloudy64 (12.7%)
-15.8%prior 76
Rain36 (7.2%)
-21.7%prior 46
Snow22 (4.4%)
83.3%prior 12
Cloudy/Rain17 (3.4%)
-39.3%prior 28
Clear/Cloudy12 (2.4%)
Clear/Other8 (1.6%)
60.0%prior 5
Rain/Cloudy7 (1.4%)
-12.5%prior 8
Snow/Sleet, hail (freezing rain or drizzle)4 (0.8%)
Clear/Unknown4 (0.8%)
-55.6%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

Daylight345 (68.2%)
-1.1%prior 349
Dark - lighted roadway87 (17.2%)
1.2%prior 86
Dark - roadway not lighted55 (10.9%)
-15.4%prior 65
Dawn9 (1.8%)
0.0%prior 9
Dusk8 (1.6%)
-38.5%prior 13
Dark - unknown roadway lighting2 (0.4%)
-60.0%prior 5

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

Road Surface

Dry372 (73.4%)
-4.9%prior 391
Wet95 (18.7%)
-10.4%prior 106
Snow32 (6.3%)
113.3%prior 15
Ice6 (1.2%)
-53.8%prior 13
Sand, mud, dirt, oil, gravel1 (0.2%)
Slush1 (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 top four vehicle makes involved in crashes remained unchanged year-over-year, with Toyota, Honda, Ford, and Chevrolet leading in both periods. In 2022, Jeep (42 vehicles) replaced Nissan (41 vehicles) as the fifth most common make, compared to 2021 when Nissan held the spot with 74 vehicles. The 26-34 age group represented the largest number of persons involved in crashes in both years, though their count decreased from 205 in 2021 to 178 in 2022.

Top Vehicle Makes (913 vehicles)

1
TOYOTA143 (15.7%)
11.7%prior 128
2
HONDA137 (15%)
12.3%prior 122
3
FORD98 (10.7%)
-4.9%prior 103
4
CHEVROLET77 (8.4%)
-11.5%prior 87
5
JEEP42 (4.6%)
0.0%prior 42
6
NISSAN41 (4.5%)
-44.6%prior 74
7
HYUNDAI32 (3.5%)
-8.6%prior 35
8
SUBARU30 (3.3%)
-31.8%prior 44
9
GMC29 (3.2%)
31.8%prior 22
10
BMW21 (2.3%)
0.0%prior 21

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

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

Sex Distribution (919 persons with recorded sex)

Male571 (62.1%)
-4.0%prior 595
Female347 (37.8%)
-20.0%prior 434
X / Unspecified1 (0.1%)

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 saw minor changes, with the 65 mph zone accounting for the most crashes in both 2022 (167) and 2021 (159). In 2021, both fatal crashes occurred in the 65 mph zone. In 2022, fatalities were more distributed, with two in the 65 mph zone, one in a 45 mph zone, and one in a 30 mph zone. Crashes in lower speed zones, such as 30 mph and 35 mph, saw a decrease in count from 102 to 89 and 122 to 104, respectively.

Fatal crashes by zone: 30 mph: 1 of 89 (1.124%) · 45 mph: 1 of 6 (16.667%) · 65 mph: 2 of 167 (1.198%)

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: WILMINGTON, MA
  • Total crash records analyzed: 508
  • Total persons involved: 1,064
  • Total vehicles involved: 913

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). "WILMINGTON, 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/wilmington/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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