ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · HINGHAM, MA · OCTOBER 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/hingham/october-2022-report
Monthly Traffic Safety Analysis
31 CRASHES IN
HINGHAM, MA
OCTOBER 2022
Total crashes in Hingham decreased by 26.2%, from 42 in October 2021 to 31 in October 2022. This period also saw a substantial 68.8% reduction in total injuries, falling from 16 to 5. No fatalities were reported in either period, indicating a general improvement in crash outcomes year-over-year.
31
▼ -26.2%was 42
Total Crash Events
0
Persons Killed
5
▼ -68.8%was 16
Persons Injured
0
▼ -100.0%was 1
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.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall, crash data for Hingham shows a declining trend year-over-year. The total number of crashes decreased from 42 in October 2021 to 31 in October 2022, representing a 26.2% reduction. This decline was accompanied by a significant 68.8% decrease in total injuries, from 16 to 5.
Vulnerable Road User Casualties
0
Motorists Killed
5
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · 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 notably between the two periods. The peak day for crashes moved from Friday with 11 incidents in October 2021 to Thursday with 7 incidents in October 2022. Similarly, the peak crash hour shifted from 2 PM with 7 crashes in the prior year to 6 PM with 4 crashes in the current year, reflecting a general reduction in crash frequency during peak times.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The severity distribution of crashes changed year-over-year, with no fatal crashes recorded in either period. Total injuries decreased significantly from 16 in October 2021 to 5 in October 2022. While minor injuries (B) decreased from 10 to 3, one serious injury (A) crash was reported in October 2022, compared to none in the prior year, and possible injuries (C) decreased from 2 to 0. The proportion of 'No Injury' crashes increased from 69% to 87.1% of all incidents.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Most severe injury per crash record
Top Contributing Factors
Several contributing factors showed shifts in their counts year-over-year. 'Followed too closely' decreased from 9 crashes in October 2021 to 3 crashes in October 2022, a 66.7% reduction in count. Conversely, 'Failed to yield right of way' doubled, increasing from 3 crashes to 6 crashes, a 100% increase in count. 'Inattention' also saw a decrease from 8 crashes to 6 crashes, representing a 25% reduction in count.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes under clear weather conditions decreased from 31 in October 2021 to 23 in October 2022. Incidents on dry road surfaces also saw a reduction, from 32 to 23 crashes. Crashes occurring during daylight hours decreased from 31 to 21, while those in 'Dark - lighted roadway' conditions remained stable at 8 incidents in both periods.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Road surface condition field
Vehicles & Demographics
The total number of vehicles involved in crashes decreased from 81 in October 2021 to 58 in October 2022. In terms of demographics, the 35-44 age group saw a notable decrease in persons involved, from 17 to 7. Among vehicle makes, FORD involvement decreased from 13 to 7, while TOYOTA remained stable at 7, consequently rising in ranking.
Top Vehicle Makes (58 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Vehicle unit records
4 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (70 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Person-level records linked to crash events
Speed Limit Zones
Crashes at 30 mph remained the most frequent speed zone for incidents, decreasing slightly from 13 crashes in October 2021 to 12 in October 2022. Higher speed zones generally saw reductions, with crashes at 35 mph falling from 9 to 4, 40 mph from 7 to 4, and 60 mph from 7 to 3. No fatal crashes were recorded in any speed zone during either period.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · 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: 2022-10-01 through 2022-10-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2022-10-01 through 2022-10-31 (31 days)
- Geographic scope: HINGHAM, MA
- Total crash records analyzed: 31
- Total persons involved: 75
- Total vehicles involved: 58
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). "HINGHAM, MA Crash Intelligence Report: October 2022." Published June 21, 2026. Reporting period: 2022-10-01 to 2022-10-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/hingham/october-2022-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: 2022-10-01 – 2022-10-31
Generated: June 21, 2026 · All rights reserved