Yearly Traffic Safety Analysis

100 CRASHES IN
NORFOLK, MA
2023

All metrics benchmarked against2022

In Norfolk, total traffic crashes remained nearly stable, with 100 incidents recorded in 2023 compared to 99 in 2022, an increase of approximately 1%. The most significant year-over-year change was the increase in crash severity, marked by one fatality in 2023 where none occurred in the prior year. Additionally, the total number of persons injured rose by 50%, from 18 in 2022 to 27 in 2023.

100

1.0%was 99

Total Crash Events

1

Persons Killed

27

50.0%was 18

Persons Injured

1

-66.7%was 3

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

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall crash volume in Norfolk saw a marginal increase of 1% from 99 crashes in 2022 to 100 in 2023. However, the severity of these incidents worsened, with total injuries increasing by 50% from 18 to 27, and one fatality recorded in 2023 compared to none in the previous year.

1

Hit-and-Run Crashes — 2023

-66.7% vs prior (3)

Hit-and-run incidents in Norfolk decreased from 2022 to 2023. The number of hit-and-run crashes fell from 3 to 1. Consequently, the hit-and-run rate, representing the percentage of all crashes that were hit-and-runs, declined from 3% in 2022 to 1% in 2023.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 00.0%

1

Motorists Killed

Prior: 0%

1

Cyclists Injured

Prior: 0%

26

Motorists Injured

Prior: 1752.9%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 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 temporal patterns of crashes showed some shifts between the two periods. While Friday remained the peak day for crashes in both 2022 (16 crashes) and 2023 (17 crashes), the peak hour for incidents moved an hour earlier to 4 PM in 2023 from 5 PM in 2022. The morning commute hours of 7 AM and 8 AM saw a combined increase in crashes from 11 in 2022 to 18 in 2023.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity increased notably in 2023 compared to the prior year. One fatal crash and one serious injury crash occurred in 2023, accounting for 1% of crashes each; no crashes of these severities were recorded in 2022. The share of crashes resulting in any level of injury rose from approximately 18% in 2022 to 22% in 2023, while the proportion of no-injury crashes decreased from 81.8% to 75%.

Outcome by Severity (Crash Events)

Fatal1fatal crashes1%
Serious Injury1serious injury crashes1%
Minor Injury14minor injury crashes14%
40.0%prior 10
Possible Injury6possible injury crashes6%
-25.0%prior 8
No Injury75no injury crashes75%
-7.4%prior 81

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factors remained broadly consistent, though counts for specific behaviors shifted year-over-year. Crashes attributed to 'Failed to yield right of way' were nearly unchanged, with 13 incidents in 2023 compared to 14 in 2022. However, the count of crashes involving 'Distracted' driving increased from 2 to 5, and incidents of 'Followed too closely' increased from 1 to 4. Conversely, crashes linked to 'Inattention' decreased in count from 11 to 7.

Officer-Reported Primary Contributing Cause

No improper driving32 (32%)-13.5%prior 37
Failed to yield right of way13 (13%)-7.1%prior 14
Failure to keep in proper lane or running off road7 (7%)-22.2%prior 9
Inattention7 (7%)-36.4%prior 11
Disregarded traffic signs, signals, road markings5 (5%)0.0%prior 5
Distracted5 (5%)
Followed too closely4 (4%)
Exceeded authorized speed limit4 (4%)
Other improper action2 (2%)
Illness2 (2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

While most crashes in both years occurred in daylight on dry roads, there were notable shifts in crashes under adverse conditions. The count of crashes on wet roads more than doubled from 7 in 2022 to 15 in 2023, increasing their share of total crashes from 7.1% to 15%. In a contrasting trend, the proportion of crashes occurring during daylight hours increased from 60.6% in 2022 to 74% in 2023, while the share of incidents on dark, unlit roadways fell from 21.2% to 11%.

Weather

Clear/Clear55 (55.0%)
-1.8%prior 56
Clear23 (23.0%)
-4.2%prior 24
Rain/Cloudy5 (5.0%)
Cloudy/Cloudy4 (4.0%)
-33.3%prior 6
Rain3 (3.0%)
Rain/Rain3 (3.0%)
Snow/Sleet, hail (freezing rain or drizzle)2 (2.0%)
Clear/Cloudy1 (1.0%)
Cloudy/Rain1 (1.0%)
Rain/Severe crosswinds1 (1.0%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Weather condition at time of crash

Lighting

Daylight74 (74.7%)
23.3%prior 60
Dark - roadway not lighted11 (11.1%)
-47.6%prior 21
Dark - lighted roadway6 (6.1%)
-50.0%prior 12
Dusk4 (4.0%)
Dawn3 (3.0%)
Dark - unknown roadway lighting1 (1.0%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Lighting condition field

Road Surface

Dry81 (81.0%)
-4.7%prior 85
Wet15 (15.0%)
114.3%prior 7
Snow4 (4.0%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Road surface condition field

Vehicles & Demographics

Toyota remained the most common vehicle make involved in crashes in both years, with 26 vehicles in 2023 and 27 in 2022. A notable shift occurred with GMC vehicles, which increased from 1 involved in 2022 to 11 in 2023. Regarding persons involved, the 65+ age group saw a significant increase from 24 individuals in 2022 to 33 in 2023, becoming the largest cohort. Conversely, the number of persons aged 0-15 involved in crashes was halved, dropping from 20 to 10.

Top Vehicle Makes (157 vehicles)

1
TOYOTA26 (16.6%)
-3.7%prior 27
2
FORD18 (11.5%)
20.0%prior 15
3
HONDA17 (10.8%)
112.5%prior 8
4
GMC11 (7%)
5
HYUNDAI8 (5.1%)
33.3%prior 6
6
CHEVROLET8 (5.1%)
-42.9%prior 14
7
NISSAN8 (5.1%)
-42.9%prior 14
8
SUBARU6 (3.8%)
-25.0%prior 8
9
JEEP6 (3.8%)
-50.0%prior 12
10
VOLVO4 (2.5%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Vehicle unit records

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

Sex Distribution (176 persons with recorded sex)

Male99 (56.3%)
-5.7%prior 105
Female77 (43.8%)
-23.8%prior 101

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Person-level records linked to crash events

Speed Limit Zones

The 35 mph speed zone continued to be the site of the most crashes in both periods, though the count decreased from 54 in 2022 to 42 in 2023. The single fatal crash recorded in 2023 occurred within a 35 mph zone. There was a significant shift in lower speed zones, with crashes in 25 mph zones increasing from 2 in 2022 to 12 in 2023.

Fatal crashes by zone: 35 mph: 1 of 42 (2.381%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · 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: 2023-01-01 through 2023-12-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2023-01-01 through 2023-12-31 (365 days)
  • Geographic scope: NORFOLK, MA
  • Total crash records analyzed: 100
  • Total persons involved: 182
  • Total vehicles involved: 157

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). "NORFOLK, MA Crash Intelligence Report: 2023." Published June 21, 2026. Reporting period: 2023-01-01 to 2023-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/norfolk/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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Norfolk, MA Crash Report — 2023 | ThatCarHitMe.com