Yearly Traffic Safety Analysis

233 CRASHES IN
PLAINVILLE, MA
2024

All metrics benchmarked against2023

In Plainville, total traffic crashes decreased by 18% from 284 in 2023 to 233 in 2024. While total fatalities remained unchanged at two, the number of injuries fell slightly from 86 to 83. The most notable year-over-year shift was a significant reduction in crashes attributed to 'Inattention,' which fell from 57 incidents to 27.

233

-18.0%was 284

Total Crash Events

2

Persons Killed

83

-3.5%was 86

Persons Injured

12

-14.3%was 14

Hit-and-Run Crashes

Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 2 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 2024-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend in traffic incidents shows a notable decrease year-over-year. Total crashes fell by 18%, from 284 in the prior period to 233 in the current period. This downward trend was accompanied by a slight 3.5% decrease in total injuries (from 86 to 83), while the number of fatalities held steady at two for both years.

12

Hit-and-Run Crashes — 2024

-14.3% vs prior (14)

The absolute number of hit-and-run incidents decreased from 14 in 2023 to 12 in 2024. Despite this drop in count, the hit-and-run rate as a proportion of total crashes saw a slight increase, rising from 4.9% in the prior year to 5.2% in the current year. This indicates that hit-and-runs declined at a slower pace than overall crashes.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 00.0%

2

Motorists Killed

Prior: 1100.0%

0

Other Killed

Prior: 00.0%

4

Cyclists Injured

Prior: 2100.0%

78

Motorists Injured

Prior: 83-6.0%

1

Other Injured

Prior: 0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

Temporal crash patterns shifted between the two periods. The peak day for crashes moved from Monday (47 crashes) in 2023 to Tuesday (45 crashes) in 2024. More significantly, the peak hour for collisions shifted from 4 p.m. in the prior year (29 crashes) to 3 p.m. in the current year, with a higher concentration of incidents during that hour (41 crashes).

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While the number of fatal crashes remained constant at two, the fatal crash rate increased from 0.7% to 0.9% due to the lower overall crash total in 2024. The proportion of crashes involving any injury rose from 23.3% in 2023 to 26.5% in 2024. This was driven by an increase in the share of 'Minor Injury' crashes, which grew from 9.9% to 12.4% of all incidents.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.9%
0.0%prior 2
Serious Injury4serious injury crashes1.7%
-20.0%prior 5
Minor Injury29minor injury crashes12.4%
3.6%prior 28
Possible Injury29possible injury crashes12.4%
-12.1%prior 33
No Injury167no injury crashes71.7%
-20.9%prior 211

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Most severe injury per crash record

Top Contributing Factors

The ranking of top contributing factors changed year-over-year. 'Failed to yield right of way' became the leading factor in 2024 with 54 crashes, an increase from 49 in 2023. Conversely, crashes attributed to 'Inattention' saw a 53% reduction in count, falling from 57 to 27, and crashes with 'No improper driving' cited also decreased from 57 to 26.

Officer-Reported Primary Contributing Cause

Failed to yield right of way54 (23.2%)10.2%prior 49
Inattention27 (11.6%)-52.6%prior 57
No improper driving26 (11.2%)-54.4%prior 57
Followed too closely25 (10.7%)-13.8%prior 29
Failure to keep in proper lane or running off road22 (9.4%)100.0%prior 11
Disregarded traffic signs, signals, road markings13 (5.6%)18.2%prior 11
Made an improper turn10 (4.3%)11.1%prior 9
Driving too fast for conditions10 (4.3%)42.9%prior 7
Other improper action9 (3.9%)28.6%prior 7
Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway4 (1.7%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Most crashes in both years occurred during daylight on dry roads. However, the proportion of crashes on dry surfaces decreased from 79.9% in 2023 to 73.8% in 2024. Concurrently, the share of crashes occurring on roads with snow or ice increased from 1.1% of all incidents in the prior year to 5.6% in the current year.

Weather

Clear121 (57.3%)
8.0%prior 112
Clear/Clear39 (18.5%)
-56.7%prior 90
Cloudy19 (9.0%)
18.8%prior 16
Snow6 (2.8%)
Rain5 (2.4%)
-50.0%prior 10
Rain/Cloudy4 (1.9%)
Cloudy/Cloudy3 (1.4%)
-70.0%prior 10
Rain/Rain3 (1.4%)
-66.7%prior 9
Snow/Snow2 (0.9%)
Fog, smog, smoke/Rain1 (0.5%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Weather condition at time of crash

Lighting

Daylight182 (78.1%)
-20.2%prior 228
Dark - lighted roadway31 (13.3%)
-3.1%prior 32
Dark - roadway not lighted10 (4.3%)
-9.1%prior 11
Dawn4 (1.7%)
Dusk4 (1.7%)
-60.0%prior 10
Dark - unknown roadway lighting2 (0.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Lighting condition field

Road Surface

Dry172 (83.1%)
-24.2%prior 227
Wet21 (10.1%)
-48.8%prior 41
Snow10 (4.8%)
Ice3 (1.4%)
Water (standing, moving)1 (0.5%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Road surface condition field

Vehicles & Demographics

The top three vehicle makes involved in crashes—Toyota, Honda, and Ford—remained the same across both periods, though the number of crashes for each make decreased in line with the overall trend. For instance, Toyota-involved crashes fell from 91 to 72. Analysis of persons involved shows a decrease across most age groups, with the 26-34 age bracket seeing a drop from 112 individuals involved in 2023 to 87 in 2024.

Top Vehicle Makes (453 vehicles)

1
TOYOTA72 (15.9%)
-20.9%prior 91
2
FORD44 (9.7%)
-32.3%prior 65
3
HONDA41 (9.1%)
-22.6%prior 53
4
NISSAN38 (8.4%)
5.6%prior 36
5
CHEVROLET34 (7.5%)
-17.1%prior 41
6
HYUNDAI28 (6.2%)
0.0%prior 28
7
SUBARU24 (5.3%)
41.2%prior 17
8
JEEP18 (4%)
-43.8%prior 32
9
KIA17 (3.8%)
-19.0%prior 21
10
GMC14 (3.1%)
7.7%prior 13

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Vehicle unit records

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

Sex Distribution (533 persons with recorded sex)

Male298 (55.9%)
-6.6%prior 319
Female234 (43.9%)
-23.8%prior 307
X / Unspecified1 (0.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Person-level records linked to crash events

Speed Limit Zones

Crashes decreased across most speed zones, with notable reductions in 30 mph zones (from 51 to 25 crashes) and 40 mph zones (from 42 to 26 crashes). The locations of fatal crashes also shifted; in 2024, fatalities occurred in 50 mph and 55 mph zones, whereas in 2023 they were recorded in 40 mph and 65 mph zones. No fatalities occurred in the 55 mph zone in 2023, compared to one in 2024.

Fatal crashes by zone: 50 mph: 1 of 5 (20%) · 55 mph: 1 of 17 (5.882%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · 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: 2024-01-01 through 2024-12-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2024-01-01 through 2024-12-31 (366 days)
  • Geographic scope: PLAINVILLE, MA
  • Total crash records analyzed: 233
  • Total persons involved: 581
  • Total vehicles involved: 453

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). "PLAINVILLE, MA Crash Intelligence Report: 2024." Published June 21, 2026. Reporting period: 2024-01-01 to 2024-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/plainville/2024-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

ThatCarHitMe.com · An Injuria.ai Company

Plainville, MA Crash Report — 2024 | ThatCarHitMe.com