Monthly Traffic Safety Analysis

12,858 CRASHES IN
MASSACHUSETTS, MA
JANUARY 2024

All metrics benchmarked againstJanuary 2023

In January 2024, there were 12,858 total crashes, a 16.0% increase from the 11,081 crashes recorded in January 2023. This year-over-year rise was accompanied by a significant shift in environmental conditions, with the number of crashes on snowy road surfaces increasing by 99.8%, from 1,213 to 2,424.

12,858

16.0%was 11,081

Total Crash Events

28

33.3%was 21

Persons Killed

3,209

1.7%was 3,155

Persons Injured

1,087

28.3%was 847

Hit-and-Run Crashes

Note: "Persons Killed" (28) counts individual fatalities across all crash events. "Fatal" in the severity table below (26) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 720 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

Crash data from January 2024 indicates an upward trend compared to the same month in the prior year. Total crashes increased by 16.0%, from 11,081 to 12,858. While total injuries saw a modest rise of 1.7%, the number of fatalities increased by 33.3%, from 21 to 28.

1,087

Hit-and-Run Crashes — January 2024

28.3% vs prior (847)

Hit-and-run incidents increased both in absolute numbers and as a proportion of total crashes. The count of hit-and-run crashes rose from 847 in January 2023 to 1,087 in January 2024. This represents an increase in the hit-and-run rate from 7.6% to 8.5% of all crashes, indicating an upward trend for this type of incident.

Vulnerable Road User Casualties

9

Pedestrians Killed

Prior: 4125.0%

0

Cyclists Killed

Prior: 1-100.0%

19

Motorists Killed

Prior: 1618.8%

0

Other Killed

Prior: 00.0%

145

Pedestrians Injured

Prior: 179-19.0%

30

Cyclists Injured

Prior: 300.0%

3,028

Motorists Injured

Prior: 2,9353.2%

6

Other Injured

Prior: 11-45.5%

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

A comparison of crash timing shows a shift in the most frequent day for collisions, moving from Monday (2,242 crashes) in January 2023 to Tuesday (2,551 crashes) in January 2024. The peak hour for crashes remained consistent at 5 PM in both periods, though the volume of crashes during this hour increased from 957 to 1,077.

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

The severity of crashes shifted slightly between January 2023 and January 2024. The fatal crash rate increased from 0.19% to 0.20% of all crashes. However, the overall proportion of crashes resulting in an injury decreased, with serious injury crashes falling from 1.6% to 1.2% and minor injury crashes decreasing from 12.5% to 11.4% of the total. Correspondingly, the share of crashes with no reported injuries rose from 72.6% to 75.2%.

Severity is per crash event (most severe injury). 26 fatal crash events resulted in 28 persons killed.

Outcome by Severity (Crash Events)

Fatal26fatal crashes0.2%
23.8%prior 21
Serious Injury157serious injury crashes1.2%
-10.8%prior 176
Minor Injury1,465minor injury crashes11.4%
5.7%prior 1,386
Possible Injury819possible injury crashes6.4%
2.2%prior 801
No Injury9,671no injury crashes75.2%
20.2%prior 8,044

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 leading contributing factors remained consistent year-over-year, with 'No improper driving,' 'Inattention,' and 'Failed to yield right of way' as the top reported factors in both periods. However, the count for several factors changed notably. Crashes attributed to 'Driving too fast for conditions' saw a 66.1% increase in count, rising from 610 to 1,013. Similarly, crashes involving 'Inattention' increased by 11.9% (from 1,324 to 1,481) and 'Followed too closely' grew by 12.1% (from 879 to 985).

Officer-Reported Primary Contributing Cause

No improper driving3,602 (28%)28.2%prior 2,809
Inattention1,481 (11.5%)11.9%prior 1,324
Failed to yield right of way1,123 (8.7%)-2.1%prior 1,147
Driving too fast for conditions1,013 (7.9%)66.1%prior 610
Followed too closely985 (7.7%)12.1%prior 879
Failure to keep in proper lane or running off road553 (4.3%)9.1%prior 507
Other improper action338 (2.6%)20.7%prior 280
Disregarded traffic signs, signals, road markings300 (2.3%)2.4%prior 293
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner296 (2.3%)-14.9%prior 348
Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway208 (1.6%)30.0%prior 160

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

Environmental conditions differed significantly between the two periods, particularly regarding road surface. In January 2024, crashes on snowy roads accounted for 18.8% of all incidents (2,424 crashes), up from 11.0% (1,213 crashes) in January 2023. Crashes on icy surfaces also increased, representing 6.0% of the total compared to 3.1% previously. Conversely, the proportion of crashes on wet roads decreased from 34.1% to 18.6%.

Weather

Clear5,917 (46.9%)
31.8%prior 4,488
Snow1,510 (12.0%)
62.9%prior 927
Cloudy1,439 (11.4%)
13.7%prior 1,266
Rain675 (5.3%)
-45.9%prior 1,248
Clear/Clear518 (4.1%)
11.4%prior 465
Snow/Sleet, hail (freezing rain or drizzle)454 (3.6%)
59.9%prior 284
Sleet, hail (freezing rain or drizzle)212 (1.7%)
1.4%prior 209
Cloudy/Snow203 (1.6%)
22.3%prior 166
Cloudy/Rain168 (1.3%)
-60.7%prior 427
Clear/Cloudy153 (1.2%)
15.0%prior 133

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

Lighting

Daylight7,145 (56.2%)
23.9%prior 5,767
Dark - lighted roadway3,631 (28.6%)
3.0%prior 3,525
Dark - roadway not lighted1,050 (8.3%)
14.1%prior 920
Dusk445 (3.5%)
2.1%prior 436
Dawn291 (2.3%)
30.5%prior 223
Dark - unknown roadway lighting113 (0.9%)
27.0%prior 89
Other28 (0.2%)
133.3%prior 12

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

Road Surface

Dry6,593 (52.2%)
21.7%prior 5,416
Snow2,424 (19.2%)
99.8%prior 1,213
Wet2,387 (18.9%)
-36.9%prior 3,783
Ice776 (6.1%)
126.9%prior 342
Slush380 (3.0%)
134.6%prior 162
Sand, mud, dirt, oil, gravel39 (0.3%)
160.0%prior 15
Water (standing, moving)23 (0.2%)
130.0%prior 10
Other18 (0.1%)
200.0%prior 6
Reported but invalid1 (0.0%)

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

Vehicles & Demographics

The composition of vehicles and persons involved in crashes showed high stability year-over-year. The top five vehicle makes involved in collisions were identical in both January 2024 and January 2023: Toyota, Honda, Ford, Chevrolet, and Nissan, in that order. Similarly, the age distribution of persons involved in crashes remained consistent, with no significant shifts in the proportional representation of any age group between the two periods.

Top Vehicle Makes (22,736 vehicles)

1
TOYOTA3,771 (16.6%)
16.2%prior 3,246
2
HONDA2,932 (12.9%)
8.4%prior 2,704
3
FORD2,421 (10.6%)
16.3%prior 2,082
4
CHEVROLET1,614 (7.1%)
16.5%prior 1,385
5
NISSAN1,481 (6.5%)
13.6%prior 1,304
6
JEEP1,080 (4.8%)
15.3%prior 937
7
SUBARU951 (4.2%)
27.5%prior 746
8
HYUNDAI917 (4%)
18.2%prior 776
9
KIA581 (2.6%)
26.3%prior 460
10
GMC502 (2.2%)
16.2%prior 432

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

2,842 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (24,534 persons with recorded sex)

Male14,296 (58.3%)
17.0%prior 12,222
Female10,224 (41.7%)
8.1%prior 9,456
X / Unspecified14 (0.1%)
7.7%prior 13

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 distribution of crashes across different speed zones remained stable, with no significant proportional shifts toward higher or lower speed limit areas. However, the rate of fatal crashes within certain zones changed. For instance, in 35 mph zones, the fatal crash rate increased from 0.133% to 0.239%. Similarly, the fatal crash rate in 55 mph zones rose from 0.187% in January 2023 to 0.490% in January 2024.

Fatal crashes by zone: 20 mph: 1 of 348 (0.287%) · 25 mph: 4 of 2,431 (0.165%) · 30 mph: 5 of 3,497 (0.143%) · 35 mph: 4 of 1,672 (0.239%) · 40 mph: 2 of 941 (0.213%) · 45 mph: 3 of 457 (0.656%) · 50 mph: 1 of 274 (0.365%) · 55 mph: 3 of 612 (0.49%) · 65 mph: 2 of 913 (0.219%)

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: massachusetts, MA
  • Total crash records analyzed: 12,858
  • Total persons involved: 27,775
  • Total vehicles involved: 22,736

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). "massachusetts, 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/statewide/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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Massachusetts (Statewide) Crash Report — January 2024 | ThatCarHitMe.com