Monthly Traffic Safety Analysis

9 CRASHES IN
CLINTON, MA
SEPTEMBER 2024

All metrics benchmarked againstSeptember 2023

Total crashes in September 2024 were 9, marking a 25% decrease from the 12 crashes recorded in September 2023. The most notable year-over-year shift is this reduction in total crash incidents by 3. Additionally, DUI crashes increased from 0 in the prior period to 1 in the current period.

9

-25.0%was 12

Total Crash Events

0

Persons Killed

3

-50.0%was 6

Persons Injured

0

Fatal Crash Events

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.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crash incidents in September 2024 decreased compared to September 2023. Total crashes fell by 3, from 12 crashes in the prior period to 9 crashes in the current period, representing a 25% reduction year-over-year. This indicates a downward trend in overall crash frequency for the month.

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%

1

Motorists Injured

Prior: 6-83.3%

1

Other Injured

Prior: 0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal distribution of crashes shifted year-over-year. In September 2023, the peak day for crashes was Monday with 3 incidents, and the peak hour was 4 PM with 4 incidents. In September 2024, the peak day shifted to Thursday with 2 incidents, and the peak hour moved to 7 PM, also with 2 incidents.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

There were no fatal crashes or fatalities in either September 2023 or September 2024. Total injuries decreased from 6 in the prior period to 3 in the current period. The prior period recorded 1 serious injury crash, which was not present in the current period, while minor injury crashes remained at 3 in both periods, though their share of total crashes increased from 25% to 33.3%.

Outcome by Severity (Crash Events)

Minor Injury3minor injury crashes33.3%
0.0%prior 3
No Injury6no injury crashes66.7%
-25.0%prior 8

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factors observed significant changes year-over-year. "Failed to yield right of way," which accounted for 5 crashes (41.7% share) in the prior period, is not among the top factors in the current period. "No improper driving" increased from 1 crash in the prior period to 2 crashes in the current period, representing a 100% increase in count. In the current period, "Disregarded traffic signs, signals, road markings" and "Other improper action" each contributed to 2 crashes, becoming notable factors not prominently listed in the prior period.

Officer-Reported Primary Contributing Cause

Disregarded traffic signs, signals, road markings2 (22.2%)
No improper driving2 (22.2%)
Other improper action2 (22.2%)
Visibility obstructed1 (11.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Conditions during crashes showed some shifts. Crashes occurring in "Clear/Clear" weather remained constant at 7 incidents in both periods. However, crashes on "Wet" road surfaces decreased significantly from 4 incidents in the prior period to 1 incident in the current period. Crashes during "Daylight" conditions decreased from 7 to 6, and "Dark - lighted roadway" crashes decreased from 3 to 2.

Weather

Clear/Clear7 (77.8%)
0.0%prior 7
Rain/Rain1 (11.1%)
Unknown/Unknown1 (11.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · Weather condition at time of crash

Lighting

Daylight6 (66.7%)
-14.3%prior 7
Dark - lighted roadway2 (22.2%)
Dusk1 (11.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · Lighting condition field

Road Surface

Dry8 (88.9%)
0.0%prior 8
Wet1 (11.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · Road surface condition field

Vehicles & Demographics

Top Vehicle Makes (14 vehicles)

1
CHEVROLET2 (14.3%)
2
FORD2 (14.3%)
3
MITS2 (14.3%)
4
MAZDA1 (7.1%)
5
MERCEDES-BENZ1 (7.1%)
6
NISSAN1 (7.1%)
7
TESL1 (7.1%)
8
BUIC1 (7.1%)
9
TOYOTA1 (7.1%)
10
HONDA1 (7.1%)
-80.0%prior 5

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · Vehicle unit records

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

Sex Distribution (24 persons with recorded sex)

Male13 (54.2%)
-23.5%prior 17
Female11 (45.8%)
-57.7%prior 26

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · Person-level records linked to crash events

Speed Limit Zones

The distribution of crashes by speed limit zones changed between the two periods. Crashes occurring in 30 mph zones decreased from 12 incidents in the prior period to 8 incidents in the current period. Additionally, 1 crash occurred in a 20 mph zone in the current period, a speed zone not represented in the prior period's crash data. Neither period recorded any fatal crashes within these speed limit zones.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · 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: 2024-09-01 through 2024-09-30
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2024-09-01 through 2024-09-30 (30 days)
  • Geographic scope: CLINTON, MA
  • Total crash records analyzed: 9
  • Total persons involved: 26
  • Total vehicles involved: 14

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). "CLINTON, MA Crash Intelligence Report: September 2024." Published June 21, 2026. Reporting period: 2024-09-01 to 2024-09-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/clinton/september-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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Clinton, MA Crash Report — September 2024 | ThatCarHitMe.com