Monthly Traffic Safety Analysis

92 CRASHES IN
RANDOLPH, MA
OCTOBER 2024

All metrics benchmarked againstOctober 2023

Total crashes in Randolph increased by 12.19%, from 82 in October 2023 to 92 in October 2024. The most notable year-over-year shift was the significant increase in total injuries, which rose from 11 to 33, representing a 200% increase.

92

12.2%was 82

Total Crash Events

0

-100.0%was 1

Persons Killed

33

200.0%was 11

Persons Injured

9

28.6%was 7

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

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-10-01 to 2024-10-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crash activity in Randolph showed an upward trend, with total crashes increasing by 10 incidents, from 82 to 92. This 12.19% rise was accompanied by a substantial 200% increase in injuries, from 11 to 33, while fatalities decreased from 1 to 0.

9

Hit-and-Run Crashes — October 2024

28.6% vs prior (7)

Hit-and-run crashes increased in count from 7 in October 2023 to 9 in October 2024. Concurrently, the hit-and-run rate rose from 8.5% to 9.8% of all crashes, indicating an upward trend in these incidents.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

0

Cyclists Killed

Prior: 00.0%

0

Motorists Killed

Prior: 1-100.0%

1

Pedestrians Injured

Prior: 0%

1

Cyclists Injured

Prior: 0%

31

Motorists Injured

Prior: 11181.8%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-10-01 to 2024-10-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The peak day for crashes shifted from Tuesday in October 2023 (16 crashes) to Wednesday in October 2024 (22 crashes). The peak crash hour also moved, from 1 PM in October 2023 (8 crashes) to 3 PM in October 2024 (9 crashes). Notably, crashes on Wednesdays saw a 266.67% increase in count, rising from 6 to 22, while crashes on Sundays decreased from 16 to 9.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-10-01 to 2024-10-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-10-01 to 2024-10-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Fatal crashes decreased from 1 in October 2023 to 0 in October 2024. Minor injuries (severity 'B') increased from 8 crashes (9.8% share) to 12 crashes (13% share), while possible injuries (severity 'C') saw a substantial rise from 1 crash (1.2% share) to 13 crashes (14.1% share). The proportion of crashes resulting in no injury decreased from 46.3% to 70.7% due to the overall increase in total crashes and injuries.

Outcome by Severity (Crash Events)

Minor Injury12minor injury crashes13%
50.0%prior 8
Possible Injury13possible injury crashes14.1%
1200.0%prior 1
No Injury65no injury crashes70.7%
71.1%prior 38

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-10-01 to 2024-10-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-10-01 to 2024-10-31 · Most severe injury per crash record

Top Contributing Factors

Among contributing factors, 'No improper driving' increased in count by 6 crashes, from 14 in October 2023 to 20 in October 2024. Conversely, 'Followed too closely' decreased by 3 crashes, from 22 to 19, and 'Failed to yield right of way' decreased by 4 crashes, from 17 to 13. 'Failure to keep in proper lane or running off road' also saw a decrease of 2 crashes, from 8 to 6.

Officer-Reported Primary Contributing Cause

No improper driving20 (21.7%)42.9%prior 14
Followed too closely19 (20.7%)-13.6%prior 22
Failed to yield right of way13 (14.1%)-23.5%prior 17
Failure to keep in proper lane or running off road6 (6.5%)-25.0%prior 8
Inattention5 (5.4%)0.0%prior 5
Disregarded traffic signs, signals, road markings5 (5.4%)
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner2 (2.2%)
Made an improper turn2 (2.2%)
Driving too fast for conditions2 (2.2%)
Exceeded authorized speed limit2 (2.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-10-01 to 2024-10-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in 'Clear/Clear' weather conditions increased by 19, from 23 in October 2023 to 42 in October 2024. Crashes on 'Wet' road surfaces significantly decreased by 13, from 16 to 3. There was also a notable reduction in crashes during 'Dark - roadway not lighted' conditions, which decreased from 12 to 9.

Weather

Clear/Clear42 (50.6%)
82.6%prior 23
Clear35 (42.2%)
-7.9%prior 38
Cloudy3 (3.6%)
Cloudy/Rain2 (2.4%)
Cloudy/Clear1 (1.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-10-01 to 2024-10-31 · Weather condition at time of crash

Lighting

Daylight61 (66.3%)
19.6%prior 51
Dark - lighted roadway15 (16.3%)
-6.3%prior 16
Dark - roadway not lighted9 (9.8%)
-25.0%prior 12
Dawn5 (5.4%)
Dusk2 (2.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-10-01 to 2024-10-31 · Lighting condition field

Road Surface

Dry77 (96.3%)
16.7%prior 66
Wet3 (3.8%)
-81.3%prior 16

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-10-01 to 2024-10-31 · Road surface condition field

Vehicles & Demographics

The total number of persons involved in crashes increased from 212 to 241, and total vehicles increased from 173 to 192. Toyota remained the top vehicle make, increasing its count from 35 to 38, while Honda moved from third to second place with an increase from 18 to 25. The 0-15 age group saw an increase of 5 persons, from 6 to 11, and the 65+ age group increased by 11 persons, from 17 to 28.

Top Vehicle Makes (192 vehicles)

1
TOYOTA38 (19.8%)
8.6%prior 35
2
HONDA25 (13%)
38.9%prior 18
3
FORD20 (10.4%)
5.3%prior 19
4
JEEP10 (5.2%)
11.1%prior 9
5
SUBARU8 (4.2%)
6
NISSAN8 (4.2%)
-38.5%prior 13
7
LEXUS8 (4.2%)
8
HYUNDAI7 (3.6%)
-30.0%prior 10
9
KIA6 (3.1%)
0.0%prior 6
10
MAZDA6 (3.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-10-01 to 2024-10-31 · Vehicle unit records

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

Sex Distribution (214 persons with recorded sex)

Male112 (52.3%)
10.9%prior 101
Female102 (47.7%)
7.4%prior 95

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-10-01 to 2024-10-31 · Person-level records linked to crash events

Speed Limit Zones

Crashes in 25 mph speed zones increased by 15, from 17 in October 2023 to 32 in October 2024. Crashes in 55 mph zones decreased by 6, from 21 to 15, and those in 65 mph zones decreased by 2, from 14 to 12. The single fatal crash reported in October 2023 occurred in a 35 mph zone, while no fatal crashes were recorded in any speed zone in October 2024.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-10-01 to 2024-10-31 · 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: 2024-10-01 through 2024-10-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2024-10-01 through 2024-10-31 (31 days)
  • Geographic scope: RANDOLPH, MA
  • Total crash records analyzed: 92
  • Total persons involved: 241
  • Total vehicles involved: 192

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). "RANDOLPH, MA Crash Intelligence Report: October 2024." Published June 21, 2026. Reporting period: 2024-10-01 to 2024-10-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/randolph/october-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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Randolph, MA Crash Report — October 2024 | ThatCarHitMe.com