Monthly Traffic Safety Analysis

23 CRASHES IN
OXFORD, MA
MAY 2023

All metrics benchmarked againstMay 2022

In May 2023, Oxford experienced 23 total crashes, a significant 50% decrease compared to the 46 crashes recorded in May 2022. However, total fatalities increased from 0 in May 2022 to 2 in May 2023, representing a critical year-over-year shift. Concurrently, total injuries fell by 75%, from 12 in May 2022 to 3 in May 2023.

23

▼ -50.0%was 46

Total Crash Events

2

Persons Killed

3

▼ -75.0%was 12

Persons Injured

2

Fatal Crash Events

Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) 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

Overall, crash incidents in Oxford saw a substantial decline year-over-year, with total crashes decreasing by 50% from 46 in May 2022 to 23 in May 2023. Despite this reduction in crash volume, the number of fatalities rose from 0 to 2, indicating a concerning increase in crash severity. Total injuries also decreased by 75%, from 12 to 3.

Vulnerable Road User Casualties

2

Motorists Killed

Prior: 0→%

3

Motorists Injured

Prior: 12↓-75.0%

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 shifted year-over-year, with the peak day moving from Friday with 10 crashes in May 2022 to Wednesday with 5 crashes in May 2023. The peak hour for crashes also changed, moving from 1 p.m. with 6 crashes in May 2022 to 5 p.m. with 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

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity profile of crashes saw a notable change, with fatal crashes increasing from 0 in May 2022 to 2 in May 2023, resulting in a fatal crash rate of 8.7% in the current period. Conversely, serious injury crashes, which accounted for 10.9% of crashes in May 2022 with 5 incidents, were not recorded in May 2023. Minor injury crashes remained at 8.7% of total crashes in both periods, with 2 incidents in May 2023 and 4 in May 2022.

Outcome by Severity (Crash Events)

Fatal2fatal crashes8.7%
Minor Injury2minor injury crashes8.7%
↓-50.0%prior 4
No Injury19no injury crashes82.6%
↓-47.2%prior 36

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

Several contributing factors showed shifts in crash counts year-over-year. Crashes attributed to 'No improper driving' decreased by 5 incidents, from 11 in May 2022 to 6 in May 2023, while 'Followed too closely' also decreased by 5 incidents, from 7 to 2. 'Failed to yield right of way' crashes saw a reduction of 4 incidents, from 6 to 2, and 'Inattention' remained constant with 2 crashes in both periods. Factors like 'Over-correcting/over-steering' and 'Driving too fast for conditions' appeared in May 2023 with 2 crashes each (8.7% share), but were not among the top factors listed in May 2022.

Officer-Reported Primary Contributing Cause

No improper driving6 (26.1%)↓-45.5%prior 11
Over-correcting/over-steering2 (8.7%)
Inattention2 (8.7%)
Driving too fast for conditions2 (8.7%)
Failed to yield right of way2 (8.7%)↓-66.7%prior 6
Followed too closely2 (8.7%)↓-71.4%prior 7
Disregarded traffic signs, signals, road markings1 (4.3%)
Other improper action1 (4.3%)
Visibility obstructed1 (4.3%)
Wrong side or wrong way1 (4.3%)

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

Regarding crash conditions, crashes occurring in 'Clear' weather decreased by 20 incidents, from 38 in May 2022 to 18 in May 2023. Crashes during 'Daylight' conditions also decreased by 18 incidents, from 35 to 17. The number of crashes on 'Dry' road surfaces saw a reduction of 24 incidents, falling from 45 in May 2022 to 21 in May 2023.

Weather

Clear18 (81.8%)
↓-52.6%prior 38
Cloudy3 (13.6%)
Rain1 (4.5%)

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

Lighting

Daylight17 (73.9%)
↓-51.4%prior 35
Dark - roadway not lighted5 (21.7%)
Dark - lighted roadway1 (4.3%)
↓-80.0%prior 5

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

Road Surface

Dry21 (95.5%)
↓-53.3%prior 45
Wet1 (4.5%)

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 (37 vehicles)

1
TOYOTA9 (24.3%)
↑28.6%prior 7
2
FORD5 (13.5%)
↓-28.6%prior 7
3
HONDA4 (10.8%)
↓-63.6%prior 11
4
MAZDA3 (8.1%)
5
HYUNDAI2 (5.4%)
6
GMC2 (5.4%)
7
CHEVROLET2 (5.4%)
8
NISSAN2 (5.4%)
↓-80.0%prior 10
9
RAM2 (5.4%)
10
JEEP1 (2.7%)

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

Sex Distribution (47 persons with recorded sex)

Female25 (53.2%)
↓-24.2%prior 33
Male22 (46.8%)
↓-52.2%prior 46

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

Crashes in 65 mph speed zones decreased by 10 incidents, from 17 in May 2022 to 7 in May 2023. Crashes in 50 mph zones also fell by 4 incidents, from 6 to 2, and 35 mph zones saw a decrease of 3 incidents, from 6 to 3. Notably, a fatal crash occurred in a 25 mph zone in May 2023, whereas no fatal crashes were recorded in any speed zone in May 2022.

Fatal crashes by zone: 25 mph: 1 of 1 (100%)

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: OXFORD, MA
  • Total crash records analyzed: 23
  • Total persons involved: 47
  • Total vehicles involved: 37

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). "OXFORD, 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/oxford/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

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