Monthly Traffic Safety Analysis

113 CRASHES IN
MARLBOROUGH, MA
JULY 2024

All metrics benchmarked againstJuly 2023

In July 2024, MARLBOROUGH experienced 113 total crashes, an increase from 103 crashes in July 2023, representing a 9.71% rise. A notable year-over-year shift was the 200% increase in speeding-related crashes, which rose from 2 to 6 incidents.

113

9.7%was 103

Total Crash Events

0

Persons Killed

27

3.8%was 26

Persons Injured

6

-33.3%was 9

Hit-and-Run Crashes

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

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-07-01 to 2024-07-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crash data for MARLBOROUGH indicates a rising trend in July year-over-year. Total crashes increased by 10, from 103 in July 2023 to 113 in July 2024. Total injuries also saw a slight increase, rising from 26 to 27.

6

Hit-and-Run Crashes — July 2024

-33.3% vs prior (9)

Hit-and-run crashes decreased from 9 incidents in July 2023 to 6 in July 2024, representing a 33.33% reduction. The corresponding hit-and-run rate decreased from 8.7% to 5.3% year-over-year, indicating a downward trend.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

0

Cyclists Killed

Prior: 00.0%

0

Motorists Killed

Prior: 00.0%

1

Pedestrians Injured

Prior: 2-50.0%

1

Cyclists Injured

Prior: 10.0%

25

Motorists Injured

Prior: 238.7%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-07-01 to 2024-07-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The peak day for crashes remained Wednesday in both periods, though the count decreased from 25 in July 2023 to 20 in July 2024. The peak hour for crashes shifted from 4 PM (14 crashes) in the prior period to 2 PM (11 crashes) in the current period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-07-01 to 2024-07-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-07-01 to 2024-07-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Fatal crashes remained at 0 in both July periods. Serious injuries remained constant at 3 incidents, representing 2.9% of crashes in the prior period and 2.7% in the current period. Minor injuries increased from 14 to 16, while possible injuries remained at 4.

Outcome by Severity (Crash Events)

Serious Injury3serious injury crashes2.7%
0.0%prior 3
Minor Injury16minor injury crashes14.2%
14.3%prior 14
Possible Injury4possible injury crashes3.5%
0.0%prior 4
No Injury87no injury crashes77%
14.5%prior 76

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-07-01 to 2024-07-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-07-01 to 2024-07-31 · Most severe injury per crash record

Top Contributing Factors

Crashes attributed to 'No improper driving' decreased by 7, from 30 to 23 incidents. Conversely, 'Followed too closely' incidents increased by 4, from 10 to 14 crashes. Both 'Exceeded authorized speed limit' and 'Driving too fast for conditions' factors each saw an increase of 2 crashes, rising from 1 to 3 incidents respectively.

Officer-Reported Primary Contributing Cause

No improper driving23 (20.4%)-23.3%prior 30
Inattention23 (20.4%)-4.2%prior 24
Followed too closely14 (12.4%)40.0%prior 10
Failed to yield right of way10 (8.8%)42.9%prior 7
Failure to keep in proper lane or running off road6 (5.3%)
Distracted4 (3.5%)
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner4 (3.5%)
Exceeded authorized speed limit3 (2.7%)
Driving too fast for conditions3 (2.7%)
Other improper action3 (2.7%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-07-01 to 2024-07-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in 'Clear' weather conditions increased from 86 to 93, while those in 'Rain' decreased from 10 to 6. Crashes in 'Dark - roadway not lighted' conditions doubled, rising from 5 incidents in July 2023 to 10 in July 2024.

Weather

Clear93 (83.0%)
8.1%prior 86
Cloudy7 (6.3%)
16.7%prior 6
Rain6 (5.4%)
-40.0%prior 10
Cloudy/Rain3 (2.7%)
Clear/Other2 (1.8%)
Clear/Unknown1 (0.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-07-01 to 2024-07-31 · Weather condition at time of crash

Lighting

Daylight88 (77.9%)
8.6%prior 81
Dark - lighted roadway12 (10.6%)
0.0%prior 12
Dark - roadway not lighted10 (8.8%)
100.0%prior 5
Dawn2 (1.8%)
Dusk1 (0.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-07-01 to 2024-07-31 · Lighting condition field

Road Surface

Dry100 (89.3%)
11.1%prior 90
Wet12 (10.7%)
0.0%prior 12

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-07-01 to 2024-07-31 · Road surface condition field

Vehicles & Demographics

The total number of vehicles involved in crashes increased from 190 to 209 year-over-year. Among specific makes, Chevrolet saw the largest increase, with 6 more vehicles involved (from 13 to 19), while Jeep saw a decrease of 4 vehicles (from 10 to 6). The age group 65+ saw a notable increase in persons involved, rising from 20 to 30.

Top Vehicle Makes (209 vehicles)

1
TOYOTA34 (16.3%)
6.3%prior 32
2
FORD24 (11.5%)
9.1%prior 22
3
HONDA24 (11.5%)
14.3%prior 21
4
CHEVROLET19 (9.1%)
46.2%prior 13
5
NISSAN18 (8.6%)
28.6%prior 14
6
SUBARU8 (3.8%)
14.3%prior 7
7
KIA7 (3.3%)
8
JEEP6 (2.9%)
-40.0%prior 10
9
DODGE6 (2.9%)
10
GMC5 (2.4%)
0.0%prior 5

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-07-01 to 2024-07-31 · Vehicle unit records

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

Sex Distribution (228 persons with recorded sex)

Male126 (55.3%)
12.5%prior 112
Female102 (44.7%)
9.7%prior 93

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-07-01 to 2024-07-31 · Person-level records linked to crash events

Speed Limit Zones

Crashes in the 30 mph speed zone decreased by 8 incidents, from 37 to 29. In contrast, crashes in the 40 mph speed zone increased by 7, from 6 to 13. Crashes in the 65 mph zone also increased by 4, rising from 13 to 17.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-07-01 to 2024-07-31 · 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: 2024-07-01 through 2024-07-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2024-07-01 through 2024-07-31 (31 days)
  • Geographic scope: MARLBOROUGH, MA
  • Total crash records analyzed: 113
  • Total persons involved: 251
  • Total vehicles involved: 209

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). "MARLBOROUGH, MA Crash Intelligence Report: July 2024." Published June 21, 2026. Reporting period: 2024-07-01 to 2024-07-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/marlborough/july-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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Marlborough, MA Crash Report — July 2024 | ThatCarHitMe.com