ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · FALMOUTH, 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/falmouth/2022-annual-report
Yearly Traffic Safety Analysis
794 CRASHES IN
FALMOUTH, MA
2022
In 2022, Falmouth recorded 794 total crashes, a 1.9% decrease from the 809 crashes in 2021. Despite this slight reduction in overall incidents, the number of people injured rose by 15.1%, increasing from 212 to 244 year-over-year. This shift was accompanied by a 26.9% increase in hit-and-run crashes.
794
▼ -1.9%was 809
Total Crash Events
5
Persons Killed
244
▲ 15.1%was 212
Persons Injured
66
▲ 26.9%was 52
Hit-and-Run Crashes
Note: "Persons Killed" (5) counts individual fatalities across all crash events. "Fatal" in the severity table below (5) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 53 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
Overall crash volume in Falmouth remained relatively stable, with a slight decrease of 1.9% from 809 incidents in 2021 to 794 in 2022. However, this stability masks a shift towards more severe outcomes, as the total number of injuries increased by 15.1% to 244. The number of fatalities held steady at 5 for both years.
66
Hit-and-Run Crashes — 2022
▲ 26.9% vs prior (52)
Hit-and-run incidents showed a notable upward trend. The number of hit-and-run crashes increased by 26.9%, from 52 in 2021 to 66 in 2022. This pushed the hit-and-run rate, or the share of all crashes that are hit-and-runs, from 6.4% to 8.3% year-over-year.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Cyclists Killed
5
Motorists Killed
7
Pedestrians Injured
14
Cyclists Injured
223
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 between the two periods. In 2022, the peak day for crashes was Tuesday with 125 incidents, and the peak hour was 12 p.m. with 85 crashes. This contrasts with 2021, when the peak day was Thursday (140 crashes) and the peak hour was later in the day at 5 p.m. (83 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
While the number of fatal crashes remained unchanged at 5, the overall severity of crashes increased from 2021 to 2022. The proportion of crashes resulting in a serious injury grew from 2.1% (17 incidents) to 3.7% (29 incidents). Correspondingly, the share of crashes with no reported injuries fell from 72.3% in 2021 to 69.4% 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 primary contributing factors were consistent across both years, with 'Inattention' being the most cited factor in 199 crashes in 2022 and 196 in 2021. The count of crashes involving 'Failed to yield right of way' increased by 11.1%, from 90 to 100 incidents. In contrast, crashes where 'Followed too closely' was a factor decreased in count from 74 to 60.
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
Crash conditions remained highly consistent year-over-year, with no significant shifts observed. In both 2022 and 2021, the vast majority of crashes occurred in daylight (73.4% and 72.8%, respectively) and on dry road surfaces (84.0% and 84.5%, respectively). Similarly, crashes in clear weather conditions accounted for a stable majority in both periods.
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 types of vehicles and the demographics of people involved in crashes showed little change between 2021 and 2022. Toyota, Ford, and Honda were the top three vehicle makes involved in crashes in both years, with similar volumes. The 65+ age group consistently represented the largest cohort of individuals involved in crashes, with 337 persons in 2022 compared to 340 in 2021.
Top Vehicle Makes (1,432 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Vehicle unit records
217 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (1,610 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 by speed limit saw most incidents occurring in 35 mph zones in both years, with the count rising from 255 to 273. The locations of fatal crashes shifted; in 2021, fatalities were recorded in 25, 30, 35, and 40 mph zones. In 2022, the 5 fatalities occurred in 30 mph (2), 35 mph (2), and 55 mph (1) zones, with none in the 25 or 40 mph zones.
Fatal crashes by zone: 30 mph: 2 of 180 (1.111%) · 35 mph: 2 of 273 (0.733%) · 55 mph: 1 of 19 (5.263%)
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: FALMOUTH, MA
- Total crash records analyzed: 794
- Total persons involved: 1,827
- Total vehicles involved: 1,432
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: 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/falmouth/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