Fatal Crashes Only

This report covers fatal crashes only, from the NHTSA Fatality Analysis Reporting System — the federal census of every crash on a US public road that killed someone within 30 days. It does not include injury or property-damage-only crashes, and its totals are not comparable with the all-severity crash reports published elsewhere on this site.

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Yearly Traffic Safety Analysis

324 CRASHES IN
MASSACHUSETTS
2023

All metrics benchmarked against2022

Fatal crashes in Massachusetts decreased by 21.55% year-over-year, from 413 fatal crashes in the prior period to 324 fatal crashes in the current period. Fatalities also saw a significant decrease of 21.38%, falling from 435 to 342. However, DUI-related fatal crashes notably increased by 31.4%, from 86 to 113 fatal crashes.

324

-21.5%was 413

Total Crash Events

342

-21.4%was 435

Persons Killed

182

-42.6%was 317

Persons Injured

19

Hit-and-Run Crashes

Note: "Persons Killed" (342) counts individual fatalities across all crash events. "Fatal" in the severity table below (324) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2023-01-01 to 2023-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Fatal crashes and fatalities both experienced a significant downward trend year-over-year. The number of fatal crashes decreased by 21.55%, from 413 to 324, while the total number of fatalities decreased by 21.38%, from 435 to 342.

19

Hit-and-Run Crashes — 2023

0.0% vs prior (19)

The number of hit-and-run fatal crashes remained constant at 19 in both the current and prior periods. However, the hit-and-run rate among all fatal crashes increased slightly from 4.6% in the prior period to 5.9% in the current period.

Vulnerable Road User Casualties

67

Pedestrians Killed

Prior: 95-29.5%

9

Cyclists Killed

Prior: 90.0%

264

Motorists Killed

Prior: 327-19.3%

2

Other Killed

Prior: 4-50.0%

4

Pedestrians Injured

Prior: 26-84.6%

1

Cyclists Injured

Prior: 0%

176

Motorists Injured

Prior: 291-39.5%

1

Other Injured

Prior: 0%

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2023-01-01 to 2023-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The peak day for fatal crashes shifted from Saturday (71 fatal crashes) in the prior period to Sunday (57 fatal crashes) in the current period. The peak hour also changed, moving from 10 PM (29 fatal crashes) in the prior period to 9 PM (24 fatal crashes) in the current period.

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2023-01-01 to 2023-12-31 · Crash date field aggregated by weekday

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2023-01-01 to 2023-12-31 · Crash time field aggregated by hour (0-23)

Road & Environmental Conditions

Clear weather was a factor in 215 fatal crashes (66.4%) in the current period, a decrease from 300 fatal crashes (72.6%) in the prior period. Fatal crashes occurring during rain decreased from 47 (11.4%) to 30 (9.3%). Fatal crashes during daylight hours remained stable at approximately 43% of all fatal crashes in both periods. Road surface conditions data is not available for comparison.

Weather

Clear215 (66.8%)
-28.3%prior 300
Cloudy70 (21.7%)
40.0%prior 50
Rain30 (9.3%)
-36.2%prior 47
Fog, Smog, Smoke3 (0.9%)
Severe Crosswinds2 (0.6%)
Snow2 (0.6%)
-75.0%prior 8

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2023-01-01 to 2023-12-31 · Weather condition at time of crash

Lighting

Daylight141 (43.5%)
-21.2%prior 179
Dark - Lighted99 (30.6%)
-20.8%prior 125
Dark - Not Lighted68 (21.0%)
-16.0%prior 81
Dusk10 (3.1%)
-9.1%prior 11
Dawn6 (1.9%)
-45.5%prior 11

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2023-01-01 to 2023-12-31 · Lighting condition field

Vehicles & Demographics

In the current period, Toyota was the most frequent vehicle make involved in fatal crashes with 59 vehicles, followed by Ford with 54 and Honda with 48. This marks a shift from the prior period where Honda (80), Toyota (78), and Ford (62) were the top three. The 65+ age group had the highest number of persons involved in fatal crashes in both periods, with 126 in the current period and 177 in the prior period.

Top Vehicle Makes (477 vehicles)

1
TOYOTA59 (12.4%)
-24.4%prior 78
2
FORD54 (11.3%)
-12.9%prior 62
3
HONDA48 (10.1%)
-40.0%prior 80
4
CHEVROLET37 (7.8%)
-2.6%prior 38
5
NISSAN/DATSUN26 (5.5%)
-21.2%prior 33
6
JEEP / KAISER-JEEP / WILLYS- JEEP21 (4.4%)
23.5%prior 17
7
HYUNDAI20 (4.2%)
-4.8%prior 21
8
HARLEY-DAVIDSON15 (3.1%)
-31.8%prior 22
9
SUBARU15 (3.1%)
-11.8%prior 17
10
YAMAHA14 (2.9%)
75.0%prior 8

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2023-01-01 to 2023-12-31 · Vehicle unit records

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

Sex Distribution (734 persons with recorded sex)

Male491 (66.9%)
-25.6%prior 660
Female243 (33.1%)
-20.6%prior 306

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2023-01-01 to 2023-12-31 · Person-level records linked to crash events

Speed Limit Zones

The highest number of fatal crashes in the current period occurred in 30 mph zones (66 fatal crashes), followed by 25 mph zones (58 fatal crashes) and 65 mph zones (45 fatal crashes). Compared to the prior period, fatal crashes in 30 mph zones decreased from 102 to 66, and in 65 mph zones decreased from 62 to 45. Fatal crashes in 25 mph zones increased from 47 to 58.

Fatal crashes by zone: 10 mph: 1 of 1 (100%) · 15 mph: 2 of 2 (100%) · 20 mph: 6 of 6 (100%) · 25 mph: 58 of 58 (100%) · 30 mph: 66 of 66 (100%) · 35 mph: 47 of 47 (100%) · 40 mph: 39 of 39 (100%) · 45 mph: 18 of 18 (100%) · 50 mph: 12 of 12 (100%) · 55 mph: 19 of 19 (100%) · 60 mph: 5 of 5 (100%) · 65 mph: 45 of 45 (100%)

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2023-01-01 to 2023-12-31 · Posted speed limit at crash location

Manner of Collision

The dominant manner of collision in both periods was 'The First Harmful Event was Not a Collision with a Motor Vehicle in Transport,' accounting for 223 fatal crashes (68.8%) in the current period and 281 fatal crashes (68%) in the prior period. Angle collisions decreased from 51 in the prior period to 37 in the current period. Front-to-Front collisions also decreased from 52 to 28 fatal crashes year-over-year.

Manner of Collision

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2023-01-01 to 2023-12-31 · Crash-level records

Vehicle Type

The dominant vehicle body types involved in fatal crashes in the current period were 4-door sedans (133) and compact utility vehicles (110). Two-wheel motorcycles were involved in 52 fatal crashes, a slight decrease from 53 in the prior period. Large trucks (truck-tractors and single-unit straight trucks) were involved in 23 fatal crashes in the current period, a decrease from 38 in the prior period.

Vehicle Type

1
4-door sedan, hardtop133 (27.9%)
2
Compact Utility (Utility Vehicle Categories "Small" and "Midsize")110 (23.1%)
3
Two Wheel Motorcycle (excluding motor scooters)52 (10.9%)
4
Light Pickup46 (9.6%)
5
Station Wagon (excluding van and truck based)27 (5.7%)
6
Large utility (ANSI D16.1 Utility Vehicle Categories and "Full Size" and "Large")27 (5.7%)
7
5-door/4-door hatchback17 (3.6%)
8
Single-unit straight truck or Cab-Chassis (GVWR range 10,001 to 19,500 lbs.)9 (1.9%)
9
Truck-tractor (Cab only, or with any number of trailing unit; any weight)8 (1.7%)

Showing top 9 of 28 reported. 19 additional (48 total) not shown: Large Van-Includes van-based buses (B150-B350, Sportsman, Royal Maxiwagon, Ram, Tradesman,...), Minivan (Chrysler Town and Country, Caravan, Grand Caravan, Voyager, Voyager, Honda-Odyssey, ...), 2-door sedan,hardtop,coupe, Motor Scooter, Single-unit straight truck or Cab-Chassis (GVWR range 19,501 to 26,000 lbs.), 3-door/2-door hatchback, Medium/heavy Pickup (GVWR greater than 10,000 lbs.), Off-road Motorcycle, Unknown motored cycle type, Utility station wagon (includes suburban limousines, Suburban, Travellall, Grand Wagoneer), Cross Country/Intercity Bus, Step van (GVWR greater than 10,000 lbs.), Transit Bus (City Bus), Convertible(excludes sun-roof,t-bar), Medium/Heavy Vehicle Based Motor Home, Moped, Other Bus Type, Other or Unknown automobile type, Single-unit straight truck or Cab-Chassis (GVWR greater than 26,000 lbs.).

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2023-01-01 to 2023-12-31 · Vehicle unit records

Rural vs Urban

In the current period, 309 fatal crashes (95.4%) occurred in urban areas and 15 fatal crashes (4.6%) in rural areas. This distribution is similar to the prior period, which saw 385 fatal crashes (94.1%) in urban areas and 24 fatal crashes (5.9%) in rural areas, indicating that the majority of fatal crashes occurred in urban settings.

Rural vs Urban

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2023-01-01 to 2023-12-31 · Crash-level records

Roadway Functional Class

In the current period, Minor Arterial (93 fatal crashes) and Other Principal Arterial (91 fatal crashes) roadways accounted for the highest number of fatal crashes. Interstate fatal crashes decreased from 68 in the prior period to 50 in the current period. Fatal crashes on Major Collector roads also decreased from 41 to 38.

Roadway Functional Class

1
Minor Arterial93 (28.7%)
2
Other Principal Arterial91 (28.1%)
3
Interstate50 (15.4%)
4
Major Collector38 (11.7%)
5
Local32 (9.9%)
6
Other Freeways and Expressways19 (5.9%)
7
Minor Collector1 (0.3%)

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2023-01-01 to 2023-12-31 · Crash-level records

Roadway Ownership

State Highway Agencies were responsible for the largest share of fatal crashes in both periods, with 139 fatal crashes in the current period and 196 in the prior period. Town or Township Highway Agencies saw a decrease in fatal crashes from 121 in the prior period to 106 in the current period. City or Municipal Highway Agencies also experienced a decrease from 81 to 75 fatal crashes.

Roadway Ownership

1
State Highway Agency139 (43%)
2
Town or Township Highway Agency106 (32.8%)
3
City or Municipal Highway Agency75 (23.2%)
4
Other State Agency2 (0.6%)
5
Other Public Instrumentality (i.e., Airport)1 (0.3%)

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2023-01-01 to 2023-12-31 · Crash-level records

Person Type

Drivers constituted the largest group of persons involved in fatal crashes in both periods, with 473 drivers in the current period and 595 in the prior period. Pedestrians involved in fatal crashes decreased significantly from 121 in the prior period to 72 in the current period. The number of bicyclists involved remained stable at 10 in the current period compared to 9 in the prior period.

Person Type

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2023-01-01 to 2023-12-31 · Crash-level records

Person Injury Severity

In the current period, there were 342 fatalities and 174 injured persons across all injury severity categories (A, B, C, U). This represents a decrease from the prior period, which recorded 435 fatalities and 297 injured persons. The number of persons with serious injuries (A) decreased from 121 in the prior period to 56 in the current period.

Person Injury Severity

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2023-01-01 to 2023-12-31 · Crash-level records

Occupant Safety Equipment

In the current period, 310 persons used shoulder and lap belts, while 217 persons used no restraints or had them not applicable. This compares to the prior period where 386 persons used shoulder and lap belts and 294 used no restraints or had them not applicable. The proportion of persons using no restraints remained substantial in both periods.

Occupant Safety Equipment

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2023-01-01 to 2023-12-31 · Person-level records linked to crash events

Point of Impact

The 12 Clock Point (front of vehicle) was the most common point of impact in both periods, accounting for 264 instances in the current period and 341 in the prior period. The 6 Clock Point (rear of vehicle) also saw a decrease from 28 instances in the prior period to 28 in the current period. Non-collision incidents decreased from 23 to 9 instances.

Point of Impact

"Other" combines 13 smaller categories (51 records): Non-Collision (9), Left-Front Side (7), 10 Clock Point (4), 3 Clock Point (4), 7 Clock Point (4), Right-Back Side (4), Undercarriage (4), 8 Clock Point (3), 5 Clock Point (3), 4 Clock Point (3), Left-Back Side (3), 2 Clock Point (2), 9 Clock Point (1).

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2023-01-01 to 2023-12-31 · Vehicle unit records

Vehicles Per Crash

Single-vehicle fatal crashes were the most common type in both periods, with 192 in the current period and 257 in the prior period. Two-vehicle fatal crashes decreased from 125 in the prior period to 101 in the current period. The number of multi-vehicle fatal crashes (3+ vehicles) remained relatively low, decreasing from 31 in the prior period to 31 in the current period.

Vehicles Per Crash

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2023-01-01 to 2023-12-31 · Crash-level records

Data Sources & Methodology

Primary Data Source

All crash data in this report is sourced from NHTSA FARS (Fatal Crashes), accessed programmatically via the NHTSA FARS 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: NHTSA FARS 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: 2023-01-01 through 2023-12-31
  • Report generated: August 5, 2026

Data Coverage

  • Reporting period: 2023-01-01 through 2023-12-31 (365 days)
  • Geographic scope: Massachusetts
  • Total crash records analyzed: 324
  • Total persons involved: 737
  • Total vehicles involved: 477

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 Crash Intelligence Report: 2023." Published August 5, 2026. Reporting period: 2023-01-01 to 2023-12-31. Data source: NHTSA FARS (Fatal Crashes), NHTSA FARS Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/fatal/statewide/2023-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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