Yearly Traffic Safety Analysis

1,588 CRASHES IN
LAWRENCE, MA
2022

All metrics benchmarked against2021

In 2022, Lawrence recorded 1,588 total traffic crashes, a 3.5% increase from the 1,535 crashes reported in 2021. Despite the rise in total incidents, the number of fatalities saw a significant year-over-year decrease. The most notable change was the reduction in traffic-related deaths from four in 2021 to one in 2022.

1,588

3.5%was 1,535

Total Crash Events

1

-75.0%was 4

Persons Killed

552

-3.2%was 570

Persons Injured

53

-36.1%was 83

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. 21 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, the total number of crashes in Lawrence saw a modest increase of 3.5%, rising from 1,535 in 2021 to 1,588 in 2022. However, the severity of these crashes trended downward, with total injuries decreasing by 3.2% from 570 to 552. Fatalities also saw a substantial reduction, dropping from four in the prior year to one in the current year.

53

Hit-and-Run Crashes — 2022

-36.1% vs prior (83)

The number of hit-and-run incidents in Lawrence saw a significant decrease year-over-year. The total count of hit-and-run crashes fell by 36.1%, from 83 in 2021 to 53 in 2022. This downward trend is also reflected in the hit-and-run rate, which dropped from 5.4% of all crashes in the prior year to 3.3% in the current year.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 2-50.0%

0

Cyclists Killed

Prior: 00.0%

0

Motorists Killed

Prior: 2-100.0%

0

Other Killed

Prior: 00.0%

47

Pedestrians Injured

Prior: 3438.2%

8

Cyclists Injured

Prior: 10-20.0%

493

Motorists Injured

Prior: 525-6.1%

4

Other Injured

Prior: 1300.0%

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 temporal patterns of crashes showed a shift between the two periods. In 2022, the highest number of crashes occurred on Fridays (257 incidents), a change from 2021 when Tuesday was the peak day with 231 incidents. The peak hour for crashes also shifted slightly later in the day, moving from the 3 p.m. hour in 2021 (107 crashes) to the 4 p.m. hour in 2022 (124 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

Crash severity showed a mixed but generally less severe trend in 2022 compared to 2021. The number of fatal crashes dropped from four to one, with the corresponding share of total crashes decreasing from 0.3% to 0.1%. While crashes resulting in serious injuries increased from 24 to 39, those involving possible injuries fell from 136 to 88. The proportion of crashes with no reported injuries rose from 71.7% in 2021 to 73.6% in 2022.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.1%
-75.0%prior 4
Serious Injury39serious injury crashes2.5%
62.5%prior 24
Minor Injury270minor injury crashes17%
6.7%prior 253
Possible Injury88possible injury crashes5.5%
-35.3%prior 136
No Injury1,169no injury crashes73.6%
6.2%prior 1,101

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 top contributing factors cited in crashes remained consistent between 2021 and 2022, with 'No improper driving,' 'Inattention,' and 'Failed to yield right of way' being the most common in both years. However, the count for 'Inattention' as a factor decreased by 20.4%, from 225 incidents in 2021 to 179 in 2022. Conversely, crashes attributed to 'Distracted' driving increased in count by 34.9%, from 43 to 58. Crashes involving 'Followed too closely' also saw a notable drop in count, falling from 86 to 45.

Officer-Reported Primary Contributing Cause

No improper driving421 (26.5%)7.4%prior 392
Inattention179 (11.3%)-20.4%prior 225
Failed to yield right of way142 (8.9%)-4.1%prior 148
Distracted58 (3.7%)34.9%prior 43
Disregarded traffic signs, signals, road markings46 (2.9%)-30.3%prior 66
Followed too closely45 (2.8%)-47.7%prior 86
Other improper action40 (2.5%)5.3%prior 38
Over-correcting/over-steering32 (2%)45.5%prior 22
Failure to keep in proper lane or running off road30 (1.9%)11.1%prior 27
Visibility obstructed22 (1.4%)0.0%prior 22

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 year-over-year, with most incidents in both 2021 and 2022 occurring in 'Daylight' (1,060 and 1,036, respectively) and on 'Dry' road surfaces (1,268 and 1,326). The most notable shift was in lighting conditions, where the number of crashes on dark but lighted roadways increased from 373 in 2021 to 462 in 2022. The proportion of crashes occurring in adverse road conditions like wet, snow, or ice remained relatively stable across both years.

Weather

Clear996 (62.8%)
8.7%prior 916
Clear/Clear214 (13.5%)
-12.7%prior 245
Cloudy145 (9.1%)
0.0%prior 145
Rain89 (5.6%)
27.1%prior 70
Cloudy/Rain20 (1.3%)
-33.3%prior 30
Cloudy/Cloudy18 (1.1%)
-10.0%prior 20
Snow17 (1.1%)
-10.5%prior 19
Rain/Cloudy15 (0.9%)
-21.1%prior 19
Sleet, hail (freezing rain or drizzle)12 (0.8%)
50.0%prior 8
Rain/Rain11 (0.7%)
-47.6%prior 21

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

Lighting

Daylight1,036 (65.3%)
-2.3%prior 1,060
Dark - lighted roadway462 (29.1%)
23.9%prior 373
Dark - roadway not lighted37 (2.3%)
-9.8%prior 41
Dusk28 (1.8%)
-24.3%prior 37
Dawn14 (0.9%)
-30.0%prior 20
Dark - unknown roadway lighting10 (0.6%)

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

Road Surface

Dry1,326 (83.7%)
4.6%prior 1,268
Wet212 (13.4%)
-4.1%prior 221
Ice20 (1.3%)
185.7%prior 7
Snow20 (1.3%)
-31.0%prior 29
Slush3 (0.2%)
-40.0%prior 5
Sand, mud, dirt, oil, gravel2 (0.1%)
Water (standing, moving)1 (0.1%)
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 demographics of vehicles involved in crashes showed high consistency year-over-year. The top five vehicle makes remained unchanged, with Honda, Toyota, and Ford being the most frequently involved in both 2021 and 2022. Analysis of the age of persons involved shows that the 26-34 age group was the largest in both years, though their count decreased from 769 to 734. The number of individuals in the 35-44 age group involved in crashes saw an increase from 579 in 2021 to 650 in 2022.

Top Vehicle Makes (3,206 vehicles)

1
HONDA995 (31%)
10.1%prior 904
2
TOYOTA396 (12.4%)
-1.2%prior 401
3
FORD276 (8.6%)
-2.8%prior 284
4
ACURA173 (5.4%)
-7.5%prior 187
5
NISSAN167 (5.2%)
0.6%prior 166
6
CHEVROLET161 (5%)
1.3%prior 159
7
JEEP127 (4%)
17.6%prior 108
8
SUBARU72 (2.2%)
24.1%prior 58
9
DODGE70 (2.2%)
-26.3%prior 95
10
BMW68 (2.1%)
-9.3%prior 75

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

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

Sex Distribution (3,848 persons with recorded sex)

Male2,185 (56.8%)
4.1%prior 2,099
Female1,661 (43.2%)
0.1%prior 1,659
R2 (0.1%)
100.0%prior 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 vast majority of crashes in both years occurred in 30 mph speed zones, with the count in this zone increasing from 1,123 in 2021 to 1,235 in 2022. Crashes in lower speed zones of 25 mph also saw a slight rise from 143 to 151. Conversely, incidents in higher speed zones of 55 mph and 65 mph decreased. In 2021, three fatal crashes occurred in 30 mph zones and one in a 55 mph zone, while the speed zone for the single fatal crash in 2022 was not specified in this dataset.

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: LAWRENCE, MA
  • Total crash records analyzed: 1,588
  • Total persons involved: 4,322
  • Total vehicles involved: 3,206

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). "LAWRENCE, 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/lawrence/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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Lawrence, MA Crash Report — 2022 | ThatCarHitMe.com