ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · UXBRIDGE, MA · 2023
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/uxbridge/2023-annual-report
Yearly Traffic Safety Analysis
292 CRASHES IN
UXBRIDGE, MA
2023
In 2023, Uxbridge recorded 292 total vehicle crashes, a 5.8% increase from the 276 crashes documented in 2022. While total injuries and fatalities decreased year-over-year, crashes where a driver was suspected of being under the influence of alcohol more than doubled, rising from 5 in 2022 to 11 in 2023.
292
▲ 5.8%was 276
Total Crash Events
1
▼ -50.0%was 2
Persons Killed
75
▼ -13.8%was 87
Persons Injured
16
▲ 23.1%was 13
Hit-and-Run Crashes
Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) 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 · 2023-01-01 to 2023-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall, total crashes in Uxbridge increased by 5.8% from 276 in 2022 to 292 in 2023. Despite the rise in total collisions, the number of people injured decreased from 87 to 75, and the number of fatalities fell from 2 to 1.
16
Hit-and-Run Crashes — 2023
▲ 23.1% vs prior (13)
The number of hit-and-run crashes increased from 13 in 2022 to 16 in 2023. This represents an upward trend in the hit-and-run rate, which rose from 4.7% of all crashes in the prior year to 5.5% in the current year.
Vulnerable Road User Casualties
0
Pedestrians Killed
1
Motorists Killed
2
Pedestrians Injured
73
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal patterns of crashes shifted between the two periods. In 2023, the highest number of crashes occurred on Fridays (51), with a peak hour at 6 p.m. (24 crashes). This contrasts with 2022, when Saturday was the peak day with 53 crashes and the most active hour was 2 p.m. with 28 incidents.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The overall severity of crashes appeared to lessen from 2022 to 2023. The number of fatal crashes dropped from 2 to 1, and the fatal crash rate per 100 crashes fell from 0.72 to 0.34. Crashes resulting in serious injuries also saw a notable decline, falling from 11 in 2022 to 4 in 2023. Consequently, the proportion of non-injury crashes increased from 73.6% of all incidents in 2022 to 77.1% in 2023.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Most severe injury per crash record
Top Contributing Factors
The leading contributing factors remained broadly consistent, with 'No improper driving' cited in 96 crashes in 2023 compared to 94 in 2022. The count for crashes involving 'Inattention' rose from 30 to 36, making it the second-most common factor in 2023. Conversely, crashes attributed to 'Failed to yield right of way' decreased in count from 25 in 2022 to 20 in 2023.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
In both 2022 and 2023, the majority of crashes occurred in clear weather, during daylight hours, and on dry roads. However, there was a notable increase in crashes on wet road surfaces, which rose from 36 incidents in 2022 to 59 in 2023. This represents a shift where wet-road crashes accounted for 20.2% of all collisions in 2023, up from a 13.0% share in the prior year.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Road surface condition field
Vehicles & Demographics
The top vehicle makes involved in crashes shifted, with Toyota taking the top spot in 2023 with 63 vehicles, up from 52 in the prior year. Ford, which was the top make in 2022 with 63 vehicles, saw its involvement decrease to 50 vehicles in 2023. Among persons involved in crashes, the 16-20 age group saw an increase from 59 to 72 individuals, and the 65+ age group increased from 56 to 74 individuals.
Top Vehicle Makes (488 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Vehicle unit records
54 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (538 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Person-level records linked to crash events
Speed Limit Zones
Crashes in 35 mph zones saw the largest increase, rising from 53 incidents in 2022 to become the most frequent zone in 2023 with 63 crashes. Collisions in 65 mph zones also increased from 39 to 48. The location of the single fatal crash in 2023 was in a 35 mph zone, whereas the single fatal crash recorded with a speed limit in 2022 occurred in a 65 mph zone.
Fatal crashes by zone: 35 mph: 1 of 63 (1.587%)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · 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: 2023-01-01 through 2023-12-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2023-01-01 through 2023-12-31 (365 days)
- Geographic scope: UXBRIDGE, MA
- Total crash records analyzed: 292
- Total persons involved: 597
- Total vehicles involved: 488
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). "UXBRIDGE, MA Crash Intelligence Report: 2023." Published June 21, 2026. Reporting period: 2023-01-01 to 2023-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/uxbridge/2023-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: 2023-01-01 – 2023-12-31
Generated: June 21, 2026 · All rights reserved