ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · FALMOUTH, MA · JANUARY 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/falmouth/january-2023-report
Monthly Traffic Safety Analysis
38 CRASHES IN
FALMOUTH, MA
JANUARY 2023
In January 2023, Falmouth experienced 38 total crashes, a decrease of 9.52% compared to the 42 crashes in January 2022. The most notable year-over-year shift was a significant 57.14% reduction in total injuries, falling from 14 in the prior period to 6 in the current period. Fatalities remained at zero in both periods.
38
▼ -9.5%was 42
Total Crash Events
0
Persons Killed
6
▼ -57.1%was 14
Persons Injured
3
▲ 50.0%was 2
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. 2 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 2023-01-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall, crash trends in Falmouth show a decrease year-over-year. Total crashes fell by 9.52%, from 42 crashes in January 2022 to 38 crashes in January 2023. A more substantial decline was observed in total injuries, which decreased by 57.14% from 14 to 6.
3
Hit-and-Run Crashes — January 2023
▲ 50.0% vs prior (2)
Hit-and-run crashes increased year-over-year, rising by 1 incident from 2 in January 2022 to 3 in January 2023, representing a 50% increase in count. The hit-and-run rate also increased from 4.8% of total crashes in the prior period to 7.9% in the current period.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Cyclists Killed
0
Motorists Killed
1
Pedestrians Injured
1
Cyclists Injured
4
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · 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 year-over-year. In January 2022, Saturday was the peak day with 9 crashes, but in January 2023, Sunday and Tuesday shared the highest count with 7 crashes each. The peak crash hour also shifted from 2 PM with 6 crashes in the prior period to 1 PM, 5 PM, and 6 PM, each with 5 crashes, in the current period.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
Crash severity saw a significant reduction in injuries year-over-year. Serious injuries (Severity A) decreased by 100%, from 2 in January 2022 to 0 in January 2023. Minor injuries (Severity B) also saw a 57.14% decrease, falling from 7 to 3, while possible injuries (Severity C) remained constant at 3 for both periods.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Most severe injury per crash record
Top Contributing Factors
The top contributing factor, 'Inattention,' increased by 3 crashes (a 30% increase) from 10 in January 2022 to 13 in January 2023, and was the leading factor in the current period. Crashes attributed to 'No improper driving' decreased by 3, from 10 to 7, while 'Operating vehicle in erratic, reckless, careless, negligent or aggressive manner' decreased by 2 crashes, from 5 to 3. Additionally, 'Exceeded authorized speed limit,' which accounted for 3 crashes in the prior period, was not a reported factor in the current period.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
There were shifts in reported crash conditions. Crashes occurring in 'Daylight' decreased by 5, from 24 in January 2022 to 19 in January 2023, while those in 'Dark - lighted roadway' increased by 4, from 9 to 13. Crashes on 'Wet' road surfaces increased by 4, from 6 to 10, whereas crashes on 'Dry' surfaces decreased by 5, from 28 to 23.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Road surface condition field
Vehicles & Demographics
The total number of vehicles involved in crashes decreased by 5, from 70 in January 2022 to 65 in January 2023. Toyota remained the top vehicle make involved, though its count decreased from 13 to 9. Chevrolet involvement increased from 5 to 7, while Ford involvement decreased from 10 to 7. Among persons involved, the 55-64 age group saw a notable decrease in representation, from 17 persons in January 2022 to 8 in January 2023.
Top Vehicle Makes (65 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Vehicle unit records
5 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (67 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Person-level records linked to crash events
Speed Limit Zones
The distribution of crashes across speed zones shifted year-over-year. Crashes occurring in 35 mph zones, which were the most frequent in January 2022 with 15 incidents, decreased to 12 in January 2023. In contrast, crashes in 30 mph zones increased from 8 to 11, becoming the most frequent speed zone for crashes in the current period. No fatal crashes were recorded in any speed zone during either period.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · 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 2023-01-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2023-01-01 through 2023-01-31 (31 days)
- Geographic scope: FALMOUTH, MA
- Total crash records analyzed: 38
- Total persons involved: 73
- Total vehicles involved: 65
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). "FALMOUTH, MA Crash Intelligence Report: January 2023." Published June 21, 2026. Reporting period: 2023-01-01 to 2023-01-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/falmouth/january-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
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 – 2023-01-31
Generated: June 21, 2026 · All rights reserved