Monthly Traffic Safety Analysis

4 CRASHES IN
CHATHAM, MA
MAY 2023

All metrics benchmarked againstMay 2022

Total crashes in Chatham increased by 33.33% year-over-year, rising from 3 crashes in May 2022 to 4 crashes in May 2023. While fatalities remained at zero in both periods, the peak hour for crashes shifted significantly from 6 PM in the prior year to 8 AM in the current year, indicating a notable change in temporal patterns.

4

33.3%was 3

Total Crash Events

0

Persons Killed

1

Persons Injured

0

Fatal Crash Events

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.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend shows an increase in total crashes, rising from 3 in May 2022 to 4 in May 2023, which represents a 33.33% increase. Fatalities remained stable at 0 in both periods, and the total number of injuries also remained consistent at 1 for both May 2022 and May 2023.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

1

Motorists Injured

Prior: 10.0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal distribution of crashes showed shifts between the two periods. The peak day for crashes changed from Wednesday in May 2022 to Friday in May 2023, with both days recording 1 crash. More notably, the peak hour for crashes shifted from 6 PM with 1 crash in May 2022 to 8 AM, which recorded 3 crashes in May 2023.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Crash date field aggregated by weekday

Crash Severity Breakdown

The severity of crashes remained largely consistent, with 0 fatalities and 1 injury reported in both May 2022 and May 2023. Crashes resulting in possible injury accounted for 33.3% (1 of 3 crashes) in May 2022, decreasing to 25% (1 of 4 crashes) in May 2023. Conversely, crashes with no injuries increased their share from 66.7% (2 of 3 crashes) in May 2022 to 75% (3 of 4 crashes) in May 2023.

Outcome by Severity (Crash Events)

Possible Injury1possible injury crashes25%
0.0%prior 1
No Injury3no injury crashes75%
50.0%prior 2

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Most severe injury per crash record

Top Contributing Factors

The top contributing factors observed in May 2023 differed from those in May 2022. In May 2023, "Inattention" was the most common factor, involved in 2 crashes (50% share of crashes). In contrast, May 2022's top factors included "Over-correcting/over-steering," "Swerving or avoiding," and "Visibility obstructed," each contributing to 1 crash (33.3% share of crashes).

Officer-Reported Primary Contributing Cause

Inattention2 (50%)
Failed to yield right of way1 (25%)
No improper driving1 (25%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Weather

Clear3 (75.0%)
Cloudy1 (25.0%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Weather condition at time of crash

Road Surface

Dry3 (75.0%)
Wet1 (25.0%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Road surface condition field

Vehicles & Demographics

Top Vehicle Makes (7 vehicles)

1
FORD2 (28.6%)
2
CHEVROLET1 (14.3%)
3
INFI1 (14.3%)
4
LINC1 (14.3%)
5
RAM1 (14.3%)
6
TOYOTA1 (14.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Vehicle unit records

Sex Distribution (9 persons with recorded sex)

Male6 (66.7%)
50.0%prior 4
Female3 (33.3%)
50.0%prior 2

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Person-level records linked to crash events

Speed Limit Zones

There was a shift in the speed zones where crashes predominantly occurred. In May 2022, all 3 crashes were reported in 30 mph speed zones. In May 2023, the majority of crashes (3 of 4) occurred in 40 mph speed zones, with 1 crash reported in a 5 mph zone, indicating a shift towards higher speed limit areas for most crashes.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · 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: 2023-05-01 through 2023-05-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2023-05-01 through 2023-05-31 (31 days)
  • Geographic scope: CHATHAM, MA
  • Total crash records analyzed: 4
  • Total persons involved: 9
  • Total vehicles involved: 7

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). "CHATHAM, MA Crash Intelligence Report: May 2023." Published June 21, 2026. Reporting period: 2023-05-01 to 2023-05-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/chatham/may-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

ThatCarHitMe.com · An Injuria.ai Company

Chatham, MA Crash Report — May 2023 | ThatCarHitMe.com