ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · FALMOUTH, 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/falmouth/2023-annual-report
Yearly Traffic Safety Analysis
657 CRASHES IN
FALMOUTH, MA
2023
In 2023, Falmouth recorded 657 total traffic crashes, a 17.3% decrease from the 794 crashes reported in 2022. This downward trend was also reflected in crash outcomes, with total fatalities dropping from 5 to 2 and total injuries decreasing from 244 to 153 year-over-year. The most notable shift was the 37.3% reduction in the number of people injured in collisions.
657
▼ -17.3%was 794
Total Crash Events
2
▼ -60.0%was 5
Persons Killed
153
▼ -37.3%was 244
Persons Injured
52
▼ -21.2%was 66
Hit-and-Run Crashes
Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 41 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
Traffic crashes in Falmouth showed a significant downward trend from 2022 to 2023. Total collisions fell by 17.3%, from 794 to 657. This decrease was accompanied by a 60% reduction in fatalities (from 5 to 2) and a 37.3% reduction in injuries (from 244 to 153).
52
Hit-and-Run Crashes — 2023
▼ -21.2% vs prior (66)
Hit-and-run incidents decreased from 2022 to 2023. The total number of hit-and-run crashes fell from 66 to 52. The rate of hit-and-runs as a percentage of all crashes also saw a slight decline, dropping from 8.3% in 2022 to 7.9% in 2023, indicating a small downward trend.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Cyclists Killed
2
Motorists Killed
11
Pedestrians Injured
12
Cyclists Injured
130
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 peak day for crashes was Friday with 113 incidents, whereas in 2022, the peak was Tuesday with 125 incidents. The busiest hour also changed, moving from 12 p.m. in 2022 (85 crashes) to 1 p.m. in 2023 (62 crashes). Summer remained the season with the highest crash volume in both years, with the peak month shifting from August in 2022 (121 crashes) to July in 2023 (85 crashes).
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
Crash severity decreased from 2022 to 2023. The number of fatal crashes fell from 5 to 2, with the fatal crash rate dropping from 0.6% to 0.3% of all crashes. The proportion of crashes resulting in any injury also declined, from 23.3% in 2022 (185 injury crashes) to 18.7% in 2023 (123 injury crashes). Specifically, serious injury crashes more than halved, decreasing from 29 in the prior year to 14 in the current year.
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 for crashes remained consistent across both years, with 'Inattention' being the most cited factor in both 2023 (189 crashes) and 2022 (199 crashes). The count of crashes attributed to inattention decreased by 5.0%. Similarly, 'Failed to yield right of way' remained a top factor, with its count decreasing by 4.0% from 100 to 96. Crashes involving 'Followed too closely' saw a more significant 25.0% reduction in count, from 60 incidents in 2022 to 45 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
The distribution of crashes by environmental conditions remained largely stable year-over-year. In 2023, 76.4% of crashes occurred in clear weather, compared to 73.7% in 2022. Crashes on dry roads accounted for 83.6% of the total in 2023, nearly identical to the 84.0% in 2022. Similarly, incidents in daylight conditions made up 73.7% of crashes in 2023, a negligible change from 73.4% 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 three vehicle makes involved in crashes were the same in both periods: Toyota, Ford, and Honda. However, the number of vehicles from each of these makes involved in crashes decreased from 2022 to 2023, with Toyota dropping from 269 to 213 and Ford from 166 to 129. The 65+ age group was the largest demographic involved in crashes in both years, with its count slightly increasing from 337 individuals in 2022 to 345 in 2023, even as the total number of people involved in crashes decreased.
Top Vehicle Makes (1,180 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Vehicle unit records
163 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (1,350 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 were most frequent in 35 mph and 30 mph zones in both years, though the counts in these zones decreased from 2022 to 2023. Crashes in 35 mph zones fell from 273 to 214, and those in 30 mph zones dropped from 180 to 158. The two fatal crashes in 2023 occurred in 25 mph and 30 mph zones. This contrasts with 2022, when the five fatal crashes were recorded in higher speed zones: two in 30 mph zones, two in 35 mph zones, and one in a 55 mph zone.
Fatal crashes by zone: 25 mph: 1 of 51 (1.961%) · 30 mph: 1 of 158 (0.633%)
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: FALMOUTH, MA
- Total crash records analyzed: 657
- Total persons involved: 1,514
- Total vehicles involved: 1,180
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: 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/falmouth/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