Monthly Traffic Safety Analysis

156 CRASHES IN
LAWRENCE, MA
NOVEMBER 2022

All metrics benchmarked againstNovember 2021

In November 2022, LAWRENCE, MA experienced 156 total crashes, a 4.7% increase from the 149 crashes reported in November 2021. Total injuries decreased from 49 to 47, while fatalities remained at zero in both periods. The most notable year-over-year shift was a 71.4% decrease in hit-and-run crashes, falling from 7 to 2.

156

4.7%was 149

Total Crash Events

0

Persons Killed

47

-4.1%was 49

Persons Injured

2

-71.4%was 7

Hit-and-Run Crashes

Note: "Persons Killed" (0) counts individual fatalities across all crash events. "Fatal" in the severity table below (0) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 2 crashes with unreported severity are not shown in the severity breakdown.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend shows a slight increase in total crashes, rising from 149 in November 2021 to 156 in November 2022, representing a 4.7% increase. Concurrently, total injuries decreased by 4.1%, from 49 to 47. Fatalities remained stable at zero in both periods.

2

Hit-and-Run Crashes — November 2022

-71.4% vs prior (7)

Hit-and-run crashes decreased significantly from 7 in November 2021 to 2 in November 2022, representing a 71.4% reduction in count. The hit-and-run rate concurrently decreased from 4.7% to 1.3% year-over-year.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

0

Motorists Killed

Prior: 00.0%

7

Pedestrians Injured

Prior: 1600.0%

40

Motorists Injured

Prior: 48-16.7%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The peak day for crashes shifted from Tuesday with 26 crashes in November 2021 to Wednesday with 34 crashes in November 2022. The peak hour also shifted, moving from 4 p.m. with 10 crashes in the prior period to 3 p.m. with 12 crashes in the current period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Fatalities remained at 0 in both November 2021 and November 2022. The number of serious injuries (A) remained constant at 3 in both periods. Minor injuries (B) decreased from 29 to 20, while possible injuries (C) increased from 9 to 13.

Outcome by Severity (Crash Events)

Serious Injury3serious injury crashes1.9%
0.0%prior 3
Minor Injury20minor injury crashes12.8%
-31.0%prior 29
Possible Injury13possible injury crashes8.3%
44.4%prior 9
No Injury118no injury crashes75.6%
9.3%prior 108

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · Most severe injury per crash record

Top Contributing Factors

The count of crashes attributed to 'No improper driving' increased by 19 (57.6%), from 33 to 52. Conversely, 'Disregarded traffic signs, signals, road markings' decreased by 8 crashes (72.7%), from 11 to 3. 'Followed too closely' decreased by 4 crashes (50%), from 8 to 4, and 'Distracted' decreased by 4 crashes (57.1%), from 7 to 3.

Officer-Reported Primary Contributing Cause

No improper driving52 (33.3%)57.6%prior 33
Failed to yield right of way19 (12.2%)5.6%prior 18
Inattention13 (8.3%)8.3%prior 12
Over-correcting/over-steering6 (3.8%)
Failure to keep in proper lane or running off road6 (3.8%)
Followed too closely4 (2.6%)-50.0%prior 8
Disregarded traffic signs, signals, road markings3 (1.9%)-72.7%prior 11
Made an improper turn3 (1.9%)
Distracted3 (1.9%)-57.1%prior 7
Visibility obstructed3 (1.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring during clear weather conditions decreased slightly from 130 in November 2021 to 127 in November 2022. Crashes during rainy conditions increased from 10 to 18, and those on wet road surfaces increased from 16 to 23. Crashes during daylight hours increased from 73 to 94, while crashes in dark-lighted roadways decreased from 62 to 52.

Weather

Clear104 (66.7%)
2.0%prior 102
Clear/Clear23 (14.7%)
-17.9%prior 28
Rain13 (8.3%)
116.7%prior 6
Cloudy10 (6.4%)
Cloudy/Rain3 (1.9%)
Cloudy/Cloudy1 (0.6%)
Rain/Cloudy1 (0.6%)
Rain/Snow1 (0.6%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · Weather condition at time of crash

Lighting

Daylight94 (60.3%)
28.8%prior 73
Dark - lighted roadway52 (33.3%)
-16.1%prior 62
Dark - roadway not lighted4 (2.6%)
Dark - unknown roadway lighting3 (1.9%)
Dusk2 (1.3%)
-66.7%prior 6
Dawn1 (0.6%)
-83.3%prior 6

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · Lighting condition field

Road Surface

Dry133 (85.3%)
1.5%prior 131
Wet23 (14.7%)
43.8%prior 16

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · Road surface condition field

Vehicles & Demographics

The total number of vehicles involved in crashes increased from 305 in November 2021 to 312 in November 2022. Honda remained the top make, though its involvement count decreased from 101 to 91 vehicles. Toyota saw an increase from 31 to 37 vehicles, moving it from third to second in top makes, while Ford decreased from 40 to 24 vehicles.

Top Vehicle Makes (312 vehicles)

1
HONDA91 (29.2%)
-9.9%prior 101
2
TOYOTA37 (11.9%)
19.4%prior 31
3
FORD24 (7.7%)
-40.0%prior 40
4
ACURA18 (5.8%)
12.5%prior 16
5
CHEVROLET15 (4.8%)
87.5%prior 8
6
NISSAN13 (4.2%)
-18.8%prior 16
7
JEEP10 (3.2%)
-16.7%prior 12
8
DODGE10 (3.2%)
-28.6%prior 14
9
HYUNDAI9 (2.9%)
80.0%prior 5
10
KIA8 (2.6%)
60.0%prior 5

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · Vehicle unit records

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

Sex Distribution (366 persons with recorded sex)

Male203 (55.5%)
0.0%prior 203
Female163 (44.5%)
4.5%prior 156

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · Person-level records linked to crash events

Speed Limit Zones

Crashes in 30 mph zones increased from 118 in November 2021 to 129 in November 2022. Conversely, crashes in 25 mph zones decreased from 12 to 7. Crashes in 65 mph zones increased from 2 to 4, while no fatalities were recorded in any speed zone during either period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-11-01 to 2022-11-30 · 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: 2022-11-01 through 2022-11-30
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2022-11-01 through 2022-11-30 (30 days)
  • Geographic scope: LAWRENCE, MA
  • Total crash records analyzed: 156
  • Total persons involved: 410
  • Total vehicles involved: 312

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: November 2022." Published June 21, 2026. Reporting period: 2022-11-01 to 2022-11-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/lawrence/november-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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