ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · MARION, MA · 2022
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/marion/2022-annual-report
Yearly Traffic Safety Analysis
145 CRASHES IN
MARION, MA
2022
In Marion, total vehicle crashes increased by 17.9%, from 123 incidents in 2021 to 145 in 2022. While the number of fatalities remained stable at one death in each year, the number of persons injured rose from 28 to 44, a 57.1% increase. The most notable shift was a significant change in the time of day crashes occurred, with the peak hour moving from mid-morning in 2021 to the afternoon commute in 2022.
145
▲ 17.9%was 123
Total Crash Events
1
Persons Killed
44
▲ 57.1%was 28
Persons Injured
3
▲ 200.0%was 1
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. 3 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
The overall trend in traffic crashes is upward. Total crashes rose from 123 in 2021 to 145 in 2022, representing a 17.9% year-over-year increase. This increase was accompanied by a 57.1% rise in total injuries, from 28 to 44, while fatalities held steady with one person killed in each period.
3
Hit-and-Run Crashes — 2022
▲ 200.0% vs prior (1)
Hit-and-run crashes increased compared to the previous year. The number of hit-and-run incidents rose from 1 in 2021 to 3 in 2022. The hit-and-run rate, as a percentage of total crashes, also trended upward, increasing from 0.8% in 2021 to 2.1% in 2022.
Vulnerable Road User Casualties
1
Motorists Killed
44
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The timing of crashes shifted notably between the two periods. In 2021, the peak day for crashes was Friday with 29 incidents, and the peak hour was 11 a.m. with 12 incidents. In 2022, the peak day moved to Wednesday with 28 crashes, and the peak hour shifted to 4 p.m., which saw 17 crashes.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
Crash severity saw a slight increase in the proportion of injury-involved incidents. While both 2021 and 2022 recorded one fatal crash, the fatal crash rate per 100 crashes decreased from 0.81 to 0.69 due to the overall increase in collisions. The number of minor injury crashes grew from 12 to 16, and the number of possible injury crashes increased from 6 to 10. Consequently, the share of all crashes resulting in any level of injury (fatal, serious, minor, or possible) rose from 18.7% in 2021 to 20.7% in 2022.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Most severe injury per crash record
Top Contributing Factors
The leading contributing factors remained consistent, though their counts shifted. "No improper driving" was the most common finding in both years, increasing from 56 to 60 incidents. The count for crashes attributed to "Inattention" decreased from 10 to 8, while "Failed to yield right of way" held steady at 8 incidents. Crashes involving "Followed too closely" saw a notable decrease, with the count falling from 8 in 2021 to 3 in 2022.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes occurring in dark conditions increased both in number and proportion, rising from 24 incidents (19.5% of total) in 2021 to 43 incidents (29.7% of total) in 2022. The distribution of crashes by road surface condition remained similar, with dry roads accounting for 77.2% of crashes in 2022 compared to 77.2% in 2021. Weather conditions were also consistent, with 'Clear' weather present in 61% of crashes in 2022 and 67% in 2021.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Road surface condition field
Vehicles & Demographics
The makes of vehicles involved in crashes saw a shift in ranking. Ford-involved vehicles increased from 17 to 37, moving from the fourth most common make in 2021 to the most common in 2022. Regarding driver and passenger demographics, the number of persons aged 65 and older involved in crashes increased from 37 to 43. The 16-20 and 35-44 age groups also saw increases in involvement, rising from 31 to 42 and 25 to 39, respectively.
Top Vehicle Makes (228 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Vehicle unit records
16 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (276 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Person-level records linked to crash events
Speed Limit Zones
The distribution of crashes across speed zones was largely stable year-over-year, with the 50 mph and 65 mph zones seeing the most incidents in both periods. Crashes in 65 mph zones increased from 25 to 28, while those in 50 mph zones remained nearly unchanged at 31, up from 30. The location of the single fatal crash shifted from a 25 mph zone in 2021 to a 65 mph zone in 2022.
Fatal crashes by zone: 65 mph: 1 of 28 (3.571%)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · 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: 2022-01-01 through 2022-12-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2022-01-01 through 2022-12-31 (365 days)
- Geographic scope: MARION, MA
- Total crash records analyzed: 145
- Total persons involved: 301
- Total vehicles involved: 228
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). "MARION, MA Crash Intelligence Report: 2022." Published June 21, 2026. Reporting period: 2022-01-01 to 2022-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/marion/2022-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: 2022-01-01 – 2022-12-31
Generated: June 21, 2026 · All rights reserved