Monthly Traffic Safety Analysis

76 CRASHES IN
RANDOLPH, MA
OCTOBER 2022

All metrics benchmarked againstOctober 2021

In October 2022, RANDOLPH, MA experienced a 22.45% decrease in total crashes, with 76 crashes compared to 98 in October 2021. Total injuries also saw a significant decrease of 46.67%, falling from 15 to 8. Fatalities remained at 0 in both periods.

76

-22.4%was 98

Total Crash Events

0

Persons Killed

8

-46.7%was 15

Persons Injured

3

-25.0%was 4

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

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crash data for RANDOLPH, MA indicates a downward trend year-over-year. Total crashes decreased by 22.45%, from 98 in the prior period to 76 in the current period. Similarly, total injuries decreased by 46.67%, from 15 to 8.

3

Hit-and-Run Crashes — October 2022

-25.0% vs prior (4)

The number of hit-and-run crashes decreased from 4 in the prior period to 3 in the current period. The hit-and-run rate slightly decreased from 4.1% in the prior period to 3.9% in the current period.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

8

Motorists Injured

Prior: 15-46.7%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · 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 Wednesday, with 17 crashes in the prior period, to Saturday, with 15 crashes in the current period. The peak hour also changed, moving from 2 PM with 10 crashes in the prior period to 3 PM with 9 crashes in the current period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Fatal crashes remained at 0 in both periods. The proportion of minor injury crashes decreased from 7.1% (7 crashes) in the prior period to 5.3% (4 crashes) in the current period. Possible injury crashes also decreased, from 3.1% (3 crashes) to 2.6% (2 crashes).

Outcome by Severity (Crash Events)

Minor Injury4minor injury crashes5.3%
-42.9%prior 7
Possible Injury2possible injury crashes2.6%
-33.3%prior 3
No Injury32no injury crashes42.1%
-22.0%prior 41

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Most severe injury per crash record

Top Contributing Factors

Among contributing factors, 'Failed to yield right of way' increased by 1 crash, from 18 in the prior period to 19 in the current period. 'Followed too closely' also increased by 1 crash, from 14 to 15. Conversely, 'No improper driving' decreased by 5 crashes, from 16 to 11, and 'Operating vehicle in erratic, reckless, careless, negligent or aggressive manner' decreased by 10 crashes, from 11 to 1.

Officer-Reported Primary Contributing Cause

Failed to yield right of way19 (25%)5.6%prior 18
Followed too closely15 (19.7%)7.1%prior 14
No improper driving11 (14.5%)-31.3%prior 16
Inattention6 (7.9%)0.0%prior 6
Other improper action3 (3.9%)
Disregarded traffic signs, signals, road markings2 (2.6%)
Distracted2 (2.6%)
Driving too fast for conditions2 (2.6%)
Failure to keep in proper lane or running off road2 (2.6%)-60.0%prior 5
Operating defective equipment2 (2.6%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in clear weather conditions (Clear or Clear/Clear) decreased slightly from 53 in the prior period to 50 in the current period. Crashes on wet road surfaces decreased from 30 in the prior period to 21 in the current period. Crashes during daylight hours decreased from 53 to 44, while those in dark-lighted roadways decreased from 24 to 18.

Weather

Clear30 (39.5%)
-14.3%prior 35
Clear/Clear20 (26.3%)
11.1%prior 18
Cloudy8 (10.5%)
-33.3%prior 12
Rain/Rain7 (9.2%)
-12.5%prior 8
Cloudy/Rain3 (3.9%)
Rain/Cloudy2 (2.6%)
-60.0%prior 5
Rain2 (2.6%)
-83.3%prior 12
Cloudy/Cloudy1 (1.3%)
Rain/Clear1 (1.3%)
Cloudy/Fog, smog, smoke1 (1.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Weather condition at time of crash

Lighting

Daylight44 (57.9%)
-17.0%prior 53
Dark - lighted roadway18 (23.7%)
-25.0%prior 24
Dark - roadway not lighted11 (14.5%)
-21.4%prior 14
Dusk2 (2.6%)
Dawn1 (1.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Lighting condition field

Road Surface

Dry55 (72.4%)
-19.1%prior 68
Wet21 (27.6%)
-30.0%prior 30

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Road surface condition field

Vehicles & Demographics

The total number of vehicles involved in crashes decreased from 215 in the prior period to 160 in the current period. The 26-34 age group saw a decrease from 61 persons involved in the prior period to 40 in the current period, while the 65+ age group increased from 18 to 26 persons involved. Toyota remained the top vehicle make, though its involvement decreased from 41 vehicles to 30.

Top Vehicle Makes (160 vehicles)

1
TOYOTA30 (18.8%)
-26.8%prior 41
2
FORD20 (12.5%)
5.3%prior 19
3
CHEVROLET15 (9.4%)
50.0%prior 10
4
HONDA15 (9.4%)
-37.5%prior 24
5
JEEP8 (5%)
0.0%prior 8
6
HYUNDAI7 (4.4%)
0.0%prior 7
7
NISSAN7 (4.4%)
-63.2%prior 19
8
DODGE4 (2.5%)
9
MERCEDES-BENZ4 (2.5%)
10
BMW4 (2.5%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Vehicle unit records

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

Sex Distribution (182 persons with recorded sex)

Male110 (60.4%)
-25.2%prior 147
Female72 (39.6%)
-29.4%prior 102

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Person-level records linked to crash events

Speed Limit Zones

Crashes in 25 mph zones decreased from 26 in the prior period to 19 in the current period. Crashes in 55 mph zones decreased from 23 to 16, and crashes in 65 mph zones decreased from 16 to 9. No fatal crashes were recorded in any speed limit zone during either period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · 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: 2022-10-01 through 2022-10-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2022-10-01 through 2022-10-31 (31 days)
  • Geographic scope: RANDOLPH, MA
  • Total crash records analyzed: 76
  • Total persons involved: 188
  • Total vehicles involved: 160

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 2022." Published June 21, 2026. Reporting period: 2022-10-01 to 2022-10-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/randolph/october-2022-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 2022 | ThatCarHitMe.com