ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · OXFORD, MA · 2024
Purpose: Machine-readable JSON endpoint for AI agents, LLMs, researchers, and programmatic consumers. Returns all underlying crash data and AI-generated commentary without HTML.
Authentication: None required. Public endpoint.
GET: https://thatcarhitme.com/api/crash-data/reports/data/massachusetts/oxford/2024-annual-report
Yearly Traffic Safety Analysis
457 CRASHES IN
OXFORD, MA
2024
In Oxford, total traffic crashes increased by 68% from 272 in 2023 to 457 in 2024. Despite this significant rise in collisions and a corresponding 82% increase in injuries from 73 to 133, the number of fatalities fell from three to zero. The most notable shift was the sharp increase in the count of crashes attributed to 'Failed to yield right of way,' which grew by 191% year-over-year.
457
▲ 68.0%was 272
Total Crash Events
0
▼ -100.0%was 3
Persons Killed
133
▲ 82.2%was 73
Persons Injured
25
▲ 92.3%was 13
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. 8 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
The overall trend in traffic collisions shows a significant year-over-year increase. Total crashes rose from 272 to 457, an increase of 68%. Similarly, the number of people injured in these incidents increased by 82%, from 73 in the prior period to 133 in the current period.
25
Hit-and-Run Crashes — 2024
▲ 92.3% vs prior (13)
Hit-and-run incidents increased both in absolute numbers and as a percentage of total crashes. The count of hit-and-run crashes rose by 92%, from 13 in 2023 to 25 in 2024. Consequently, the hit-and-run rate increased from 4.8% to 5.5% of all crashes.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Cyclists Killed
0
Motorists Killed
1
Pedestrians Injured
1
Cyclists Injured
131
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The timing of crashes shifted between the two periods. The peak day for collisions moved from Tuesday (48 crashes) in the prior year to Friday (82 crashes) in the current year. The peak hour for crashes also shifted two hours earlier, from 5 PM in 2023 to 3 PM in 2024.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
While total crashes increased, their severity profile changed notably. The number of fatal crashes dropped from three in 2023 to zero in 2024. The proportion of crashes resulting in serious injury remained stable at 1.1% in both periods, while the share of minor injury crashes was also consistent at 14.0% in 2024 compared to 14.3% in 2023.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Most severe injury per crash record
Top Contributing Factors
The leading contributing factors saw shifts in volume year-over-year. Crashes attributed to 'Inattention' increased in count from 54 to 75, a 39% rise. More dramatically, collisions involving 'Failed to yield right of way' grew from 22 to 64, a 191% increase in count, moving it from the fourth to the third most common factor. Crashes involving 'Following too closely' also increased from 47 to 55.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
The distribution of crashes across environmental conditions remained proportionally stable despite the overall increase in incidents. Crashes on dry roads accounted for 78.8% of the total in 2024, nearly identical to the 79.0% share in 2023. Similarly, daylight crashes made up 69.1% of incidents in the current period, compared to 71.3% in the prior period, indicating that the overall increase in crashes was not driven by a disproportionate rise in adverse condition incidents.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Road surface condition field
Vehicles & Demographics
The top vehicle makes involved in crashes remained consistent, with Toyota and Ford holding the first and second positions in both years. Chevrolet replaced Honda as the third most frequently involved make in 2024. The age distribution of all persons involved in crashes remained broadly similar, with the 26-34 age group representing the largest cohort in both periods, accounting for 18.3% of persons in 2023 and 15.7% in 2024.
Top Vehicle Makes (857 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Vehicle unit records
60 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (1,009 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Person-level records linked to crash events
Speed Limit Zones
Year-over-year, crashes increased significantly in the 35 mph speed zone, rising 193% from 45 to 132 incidents, making it the zone with the most crashes in 2024. Collisions in the 65 mph zone saw a more modest 23% increase from 86 to 106. While the prior period recorded fatalities in 25 mph and 65 mph zones, the current period had zero fatalities across all speed zones.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · 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: 2024-01-01 through 2024-12-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2024-01-01 through 2024-12-31 (366 days)
- Geographic scope: OXFORD, MA
- Total crash records analyzed: 457
- Total persons involved: 1,073
- Total vehicles involved: 857
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: 2024." Published June 21, 2026. Reporting period: 2024-01-01 to 2024-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/oxford/2024-annual-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
ThatCarHitMe.com
An Injuria.ai Company
Crash Data Intelligence
Data: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly
Period: 2024-01-01 – 2024-12-31
Generated: June 21, 2026 · All rights reserved