Yearly Traffic Safety Analysis

192 CRASHES IN
BELMONT, MA
2025

All metrics benchmarked against2024

In 2025, Belmont recorded 192 total crashes, a 41.5% decrease from the 328 crashes reported in 2024. Total injuries also fell by 50.6%, from 89 to 44. Despite this overall reduction in collisions, the most notable change was the occurrence of one fatal crash in 2025, whereas none were recorded in the prior year.

192

-41.5%was 328

Total Crash Events

1

Persons Killed

44

-50.6%was 89

Persons Injured

23

-41.0%was 39

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

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall traffic collisions in Belmont saw a significant downward trend year-over-year, with total crashes falling by 41.5% from 328 in 2024 to 192 in 2025. This trend included a 50.6% reduction in total injuries, from 89 to 44. However, the city recorded one fatality in 2025 after having zero in the previous year.

23

Hit-and-Run Crashes — 2025

-41.0% vs prior (39)

The number of hit-and-run incidents decreased from 39 in 2024 to 23 in 2025, a reduction of 41.0%. Despite this drop in absolute numbers, the hit-and-run rate as a percentage of total crashes remained virtually unchanged. The rate was 12.0% in 2025, compared to 11.9% in the prior year, indicating that the frequency of drivers leaving the scene relative to the total number of crashes has been stable.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

0

Cyclists Killed

Prior: 00.0%

1

Motorists Killed

Prior: 0%

0

Other Killed

Prior: 00.0%

4

Pedestrians Injured

Prior: 8-50.0%

4

Cyclists Injured

Prior: 7-42.9%

35

Motorists Injured

Prior: 72-51.4%

1

Other Injured

Prior: 2-50.0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · 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 Tuesday was a high-frequency day for crashes in both 2024 (64 crashes) and 2025 (38 crashes), the prior year also had a co-peak on Fridays (64 crashes) which was not observed in the current period. The peak hour for collisions shifted an hour later in the evening, moving from 5 PM in 2024 (32 crashes) to 6 PM in 2025 (20 crashes).

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity of crashes shifted with the appearance of one fatal crash in 2025, compared to zero in 2024. The proportion of crashes resulting in serious injury increased slightly from 1.8% to 2.1% of all crashes, even as the absolute number of such incidents fell from 6 to 4. Conversely, the share of crashes involving minor injuries decreased from 13.1% of the total in 2024 to 10.4% in 2025.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.5%
Serious Injury4serious injury crashes2.1%
-33.3%prior 6
Minor Injury20minor injury crashes10.4%
-53.5%prior 43
Possible Injury16possible injury crashes8.3%
-42.9%prior 28
No Injury135no injury crashes70.3%
-41.0%prior 229

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Most severe injury per crash record

Top Contributing Factors

Comparing contributing factors, the number of crashes attributed to 'Inattention' increased from 28 to 30 year-over-year, a 7.1% rise in count, causing it to climb from the third to the second most-cited factor. In contrast, crashes linked to 'Failed to yield right of way' decreased in count from 42 to 28, a 33.3% reduction. The count for 'Followed too closely' also saw a significant drop of 51.9%, from 27 incidents in 2024 to 13 in 2025.

Officer-Reported Primary Contributing Cause

No improper driving48 (25%)-47.8%prior 92
Inattention30 (15.6%)7.1%prior 28
Failed to yield right of way28 (14.6%)-33.3%prior 42
Followed too closely13 (6.8%)-51.9%prior 27
Failure to keep in proper lane or running off road7 (3.6%)-46.2%prior 13
Visibility obstructed7 (3.6%)-36.4%prior 11
Other improper action6 (3.1%)-33.3%prior 9
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner5 (2.6%)-16.7%prior 6
Disregarded traffic signs, signals, road markings4 (2.1%)-33.3%prior 6
Distracted3 (1.6%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The proportion of crashes occurring in daylight conditions remained stable, accounting for 75.0% of crashes in 2025 compared to 77.4% in 2024. There was a slight increase in the share of crashes on wet roads, rising from 11.3% of all crashes in the prior year to 14.6% in the current year. Similarly, the proportion of collisions happening in weather conditions other than 'Clear' grew from 28.7% in 2024 to 32.8% in 2025.

Weather

Clear105 (55.9%)
-42.9%prior 184
Cloudy29 (15.4%)
-29.3%prior 41
Clear/Clear24 (12.8%)
-52.0%prior 50
Snow7 (3.7%)
40.0%prior 5
Rain/Cloudy6 (3.2%)
Rain5 (2.7%)
-50.0%prior 10
Cloudy/Cloudy4 (2.1%)
-55.6%prior 9
Rain/Rain2 (1.1%)
Unknown/Unknown1 (0.5%)
Cloudy/Other1 (0.5%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Weather condition at time of crash

Lighting

Daylight144 (76.6%)
-43.3%prior 254
Dark - lighted roadway28 (14.9%)
-51.7%prior 58
Dusk14 (7.4%)
55.6%prior 9
Dark - roadway not lighted1 (0.5%)
Dawn1 (0.5%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Lighting condition field

Road Surface

Dry151 (80.7%)
-46.1%prior 280
Wet28 (15.0%)
-24.3%prior 37
Snow4 (2.1%)
-20.0%prior 5
Ice2 (1.1%)
Sand, mud, dirt, oil, gravel2 (1.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Road surface condition field

Vehicles & Demographics

The top three vehicle makes involved in crashes—Toyota, Honda, and Ford—maintained their rankings in both 2024 and 2025, with involvement counts for each make decreasing in line with the overall crash reduction. When analyzing the age distribution of all persons involved in crashes, the 35-44 age group saw its representation increase slightly, accounting for 18.0% of individuals in 2025, up from 16.5% in 2024. The proportional involvement of other age groups remained largely consistent between the two years.

Top Vehicle Makes (349 vehicles)

1
TOYOTA65 (18.6%)
-52.9%prior 138
2
HONDA49 (14%)
-32.9%prior 73
3
FORD30 (8.6%)
-46.4%prior 56
4
CHEVROLET22 (6.3%)
-15.4%prior 26
5
SUBARU20 (5.7%)
-53.5%prior 43
6
NISSAN18 (5.2%)
-5.3%prior 19
7
AUDI12 (3.4%)
-7.7%prior 13
8
MAZDA11 (3.2%)
-15.4%prior 13
9
BMW11 (3.2%)
-31.3%prior 16
10
MERCEDES-BENZ10 (2.9%)
66.7%prior 6

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Vehicle unit records

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

Sex Distribution (369 persons with recorded sex)

Male205 (55.6%)
-38.8%prior 335
Female164 (44.4%)
-43.3%prior 289

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · Person-level records linked to crash events

Speed Limit Zones

Crashes remained highly concentrated in 25 mph speed zones in both periods, accounting for 92.7% of collisions in 2025 and 91.8% in 2024. The total number of crashes in 25 mph zones decreased from 301 to 178. Notably, the single fatal crash recorded in 2025 occurred within a 25 mph zone, a zone that had zero fatal crashes in the prior year.

Fatal crashes by zone: 25 mph: 1 of 178 (0.562%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-01-01 to 2025-12-31 · 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: 2025-01-01 through 2025-12-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2025-01-01 through 2025-12-31 (365 days)
  • Geographic scope: BELMONT, MA
  • Total crash records analyzed: 192
  • Total persons involved: 400
  • Total vehicles involved: 349

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). "BELMONT, MA Crash Intelligence Report: 2025." Published June 21, 2026. Reporting period: 2025-01-01 to 2025-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/belmont/2025-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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Belmont, MA Crash Report — 2025 | ThatCarHitMe.com