Monthly Traffic Safety Analysis

88 CRASHES IN
RANDOLPH, MA
SEPTEMBER 2023

All metrics benchmarked againstSeptember 2022

In September 2023, Randolph experienced 88 total crashes, a substantial increase of 91.3% compared to the 46 crashes recorded in September 2022. This period saw a significant rise in overall crash incidents, with the most notable shift being the doubling of total crashes year-over-year.

88

91.3%was 46

Total Crash Events

0

Persons Killed

13

116.7%was 6

Persons Injured

3

50.0%was 2

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

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend indicates a significant increase in crashes year-over-year, with total crashes rising from 46 in September 2022 to 88 in September 2023. This represents a 91.3% increase in crash incidents.

3

Hit-and-Run Crashes — September 2023

50.0% vs prior (2)

The number of hit-and-run crashes increased from 2 in September 2022 to 3 in September 2023, representing a 50% increase in count. However, the hit-and-run rate decreased from 4.3% of total crashes in the prior period to 3.4% in the current period.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

13

Motorists Injured

Prior: 6116.7%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · 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 Monday with 10 incidents in September 2022 to Friday with 19 incidents in September 2023. Similarly, the peak hour changed from 9 AM with 6 crashes in the prior period to 4 PM with 9 crashes in the current period, indicating a shift in peak crash times towards the late afternoon.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

There were no fatal crashes or fatalities reported in either September 2022 or September 2023. Total injuries increased from 6 in September 2022 to 13 in September 2023. The current period also reported 2 serious injuries (2.3% of crashes), which were not present in the prior period's data.

Outcome by Severity (Crash Events)

Serious Injury2serious injury crashes2.3%
Minor Injury3minor injury crashes3.4%
-25.0%prior 4
Possible Injury3possible injury crashes3.4%
200.0%prior 1
No Injury29no injury crashes33%
81.3%prior 16

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Most severe injury per crash record

Top Contributing Factors

Among contributing factors, 'Failed to yield right of way' increased from 8 crashes to 16 crashes, a 100% increase in count. 'Followed too closely' also saw a rise from 8 crashes to 13 crashes, representing a 62.5% increase in count. Conversely, 'Exceeded authorized speed limit' decreased by 2 crashes, from 4 in the prior period to 2 in the current period.

Officer-Reported Primary Contributing Cause

Failed to yield right of way16 (18.2%)100.0%prior 8
No improper driving13 (14.8%)116.7%prior 6
Followed too closely13 (14.8%)62.5%prior 8
Failure to keep in proper lane or running off road11 (12.5%)
Inattention7 (8%)0.0%prior 7
Exceeded authorized speed limit2 (2.3%)
Fatigued/asleep2 (2.3%)
Disregarded traffic signs, signals, road markings2 (2.3%)
Made an improper turn2 (2.3%)
Over-correcting/over-steering2 (2.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in wet road conditions increased significantly, from 6 incidents in September 2022 to 30 incidents in September 2023. The proportion of crashes under rainy weather conditions also rose from 10.9% (5 of 46 crashes) to 30.7% (27 of 88 crashes) year-over-year.

Weather

Clear/Clear29 (33.3%)
93.3%prior 15
Clear19 (21.8%)
0.0%prior 19
Rain10 (11.5%)
Cloudy8 (9.2%)
60.0%prior 5
Cloudy/Rain6 (6.9%)
Rain/Rain5 (5.7%)
Rain/Cloudy5 (5.7%)
Cloudy/Cloudy2 (2.3%)
Cloudy/Clear1 (1.1%)
Clear/Rain1 (1.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Weather condition at time of crash

Lighting

Daylight58 (65.9%)
87.1%prior 31
Dark - lighted roadway18 (20.5%)
80.0%prior 10
Dark - roadway not lighted7 (8.0%)
Dusk5 (5.7%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Lighting condition field

Road Surface

Dry58 (65.9%)
45.0%prior 40
Wet30 (34.1%)
400.0%prior 6

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Road surface condition field

Vehicles & Demographics

The total number of persons involved in crashes increased from 112 in September 2022 to 230 in September 2023. All age groups saw an increase in the number of persons involved, with the 35-44 age group remaining the most represented in both periods. Toyota, Honda, and Ford were the top three vehicle makes involved in crashes for both periods, with Toyota becoming the leading make in the current period with 33 vehicles, up from 13 in the prior period.

Top Vehicle Makes (182 vehicles)

1
TOYOTA33 (18.1%)
153.8%prior 13
2
HONDA25 (13.7%)
92.3%prior 13
3
FORD23 (12.6%)
76.9%prior 13
4
NISSAN16 (8.8%)
100.0%prior 8
5
CHEVROLET8 (4.4%)
6
JEEP7 (3.8%)
7
HYUNDAI6 (3.3%)
8
KIA6 (3.3%)
9
LEXUS5 (2.7%)
10
SUBARU5 (2.7%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Vehicle unit records

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

Sex Distribution (221 persons with recorded sex)

Male139 (62.9%)
107.5%prior 67
Female82 (37.1%)
105.0%prior 40

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Person-level records linked to crash events

Speed Limit Zones

Crashes occurring in the 55 mph speed zone more than doubled, increasing from 9 incidents in September 2022 to 20 incidents in September 2023. Crashes in the 65 mph speed zone decreased from 9 to 4 incidents, while crashes in the 30 mph zone increased from 3 to 17 incidents. No fatal rates were recorded for any speed zone in either period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · 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: 2023-09-01 through 2023-09-30
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2023-09-01 through 2023-09-30 (30 days)
  • Geographic scope: RANDOLPH, MA
  • Total crash records analyzed: 88
  • Total persons involved: 230
  • Total vehicles involved: 182

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: September 2023." Published June 21, 2026. Reporting period: 2023-09-01 to 2023-09-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/randolph/september-2023-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 — September 2023 | ThatCarHitMe.com