Monthly Traffic Safety Analysis

168 CRASHES IN
LOWELL, MA
JANUARY 2024

All metrics benchmarked againstJanuary 2023

In January 2024, Lowell experienced 168 total crashes, a decrease from 241 crashes reported in January 2023. This represents a 30.29% reduction in total crashes year-over-year, accompanied by a 53.13% decrease in total injuries, making the overall reduction in crash and injury incidents the most notable shift.

168

-30.3%was 241

Total Crash Events

0

Persons Killed

30

-53.1%was 64

Persons Injured

19

-60.4%was 48

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. 61 crashes with unreported severity are not shown in the severity breakdown.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-01-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crash data for January 2024 indicates a falling trend compared to January 2023. Total crashes decreased by 30.29%, from 241 to 168, while total injuries saw a 53.13% reduction, falling from 64 to 30. Fatalities remained at zero in both periods.

19

Hit-and-Run Crashes — January 2024

-60.4% vs prior (48)

Hit-and-run crashes decreased significantly from 48 in January 2023 to 19 in January 2024, a reduction of 60.4%. The hit-and-run rate also fell from 19.9% of total crashes in the prior period to 11.3% in the current period, indicating a downward trend.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 00.0%

0

Motorists Killed

Prior: 00.0%

0

Other Killed

Prior: 00.0%

1

Cyclists Injured

Prior: 0%

28

Motorists Injured

Prior: 60-53.3%

1

Other Injured

Prior: 0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-01-31 · 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 Monday, with 44 crashes in January 2023, to Wednesday, with 39 crashes in January 2024. The peak hour for crashes also changed, moving from 5 PM, which recorded 29 crashes in the prior period, to 3 PM, which recorded 17 crashes in the current period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-01-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-01-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

No fatal crashes or fatalities occurred in either January 2023 or January 2024. Total injuries decreased from 64 in the prior period to 30 in the current period. Serious injury crashes decreased from 2 (0.8% of total crashes) to 1 (0.6% of total crashes), while minor injury crashes decreased from 23 (9.5%) to 12 (7.1%).

Outcome by Severity (Crash Events)

Serious Injury1serious injury crashes0.6%
-50.0%prior 2
Minor Injury12minor injury crashes7.1%
-47.8%prior 23
Possible Injury8possible injury crashes4.8%
-60.0%prior 20
No Injury86no injury crashes51.2%
-45.2%prior 157

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-01-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-01-31 · Most severe injury per crash record

Top Contributing Factors

The top contributing factor, 'No improper driving,' decreased from 81 crashes in January 2023 to 47 crashes in January 2024, a 42% reduction in count. 'Failed to yield right of way' decreased from 18 to 6 crashes, a 66.7% reduction, while 'Inattention' decreased from 14 to 10 crashes, a 28.6% reduction in count.

Officer-Reported Primary Contributing Cause

No improper driving47 (28%)-42.0%prior 81
Inattention10 (6%)-28.6%prior 14
Followed too closely7 (4.2%)-46.2%prior 13
Failed to yield right of way6 (3.6%)-66.7%prior 18
Other improper action3 (1.8%)
Distracted2 (1.2%)-66.7%prior 6
Disregarded traffic signs, signals, road markings2 (1.2%)-71.4%prior 7
Driving too fast for conditions2 (1.2%)-60.0%prior 5
Physical impairment1 (0.6%)
Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway1 (0.6%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-01-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in 'Clear' weather remained constant at 86, but snow-related crashes increased from 20 in January 2023 to 36 in January 2024. Conversely, crashes during 'Rain' decreased from 16 to 10. Crashes on dry roads decreased from 125 to 85, and crashes on wet roads decreased from 73 to 35, while crashes on icy roads increased from 1 to 10.

Weather

Clear86 (51.5%)
0.0%prior 86
Snow36 (21.6%)
80.0%prior 20
Cloudy19 (11.4%)
-5.0%prior 20
Rain10 (6.0%)
-37.5%prior 16
Sleet, hail (freezing rain or drizzle)6 (3.6%)
Snow/Sleet, hail (freezing rain or drizzle)5 (3.0%)
-28.6%prior 7
Clear/Clear1 (0.6%)
-96.8%prior 31
Cloudy/Rain1 (0.6%)
Cloudy/Snow1 (0.6%)
Rain/Rain1 (0.6%)
-85.7%prior 7

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-01-31 · Weather condition at time of crash

Lighting

Daylight90 (53.9%)
-23.1%prior 117
Dark - lighted roadway71 (42.5%)
-29.0%prior 100
Dusk4 (2.4%)
Dark - roadway not lighted1 (0.6%)
-90.0%prior 10
Dawn1 (0.6%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-01-31 · Lighting condition field

Road Surface

Dry85 (50.9%)
-32.0%prior 125
Wet35 (21.0%)
-52.1%prior 73
Snow32 (19.2%)
6.7%prior 30
Ice10 (6.0%)
Slush5 (3.0%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-01-31 · Road surface condition field

Vehicles & Demographics

The number of persons involved in crashes decreased across most age groups, with the 26-34 age group seeing the largest drop from 104 in January 2023 to 50 in January 2024. The 16-20 age group also experienced a significant decrease from 51 to 15. Toyota, Honda, and Ford remained the top three vehicle makes involved in crashes, though their individual counts decreased year-over-year.

Top Vehicle Makes (313 vehicles)

1
TOYOTA64 (20.4%)
-22.0%prior 82
2
HONDA51 (16.3%)
-35.4%prior 79
3
FORD40 (12.8%)
-21.6%prior 51
4
NISSAN22 (7%)
-15.4%prior 26
5
CHEVROLET19 (6.1%)
-29.6%prior 27
6
JEEP12 (3.8%)
50.0%prior 8
7
HYUNDAI12 (3.8%)
0.0%prior 12
8
SUBARU9 (2.9%)
-59.1%prior 22
9
BMW8 (2.6%)
0.0%prior 8
10
LEXUS7 (2.2%)
-30.0%prior 10

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-01-31 · Vehicle unit records

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

Sex Distribution (237 persons with recorded sex)

Male137 (57.8%)
-50.4%prior 276
Female100 (42.2%)
-50.2%prior 201

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-01-31 · Person-level records linked to crash events

Speed Limit Zones

The highest number of crashes in both periods occurred in the 30 mph speed zone, increasing from 43 crashes in January 2023 to 105 crashes in January 2024. Crashes in the 25 mph zone nearly doubled from 16 to 30. There were no fatal crashes recorded in any speed zone during either period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-01-31 · 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: 2024-01-01 through 2024-01-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2024-01-01 through 2024-01-31 (31 days)
  • Geographic scope: LOWELL, MA
  • Total crash records analyzed: 168
  • Total persons involved: 376
  • Total vehicles involved: 313

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). "LOWELL, MA Crash Intelligence Report: January 2024." Published June 21, 2026. Reporting period: 2024-01-01 to 2024-01-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/lowell/january-2024-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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Lowell, MA Crash Report — January 2024 | ThatCarHitMe.com