ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · FALMOUTH, MA · SEPTEMBER 2024
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/september-2024-report
Monthly Traffic Safety Analysis
58 CRASHES IN
FALMOUTH, MA
SEPTEMBER 2024
Falmouth experienced a 6.5% decrease in total crashes, from 62 in September 2023 to 58 in September 2024. Despite this reduction in crash events, total injuries increased by 38.9%, rising from 18 injured persons to 25. This indicates that while fewer crashes occurred, those that did resulted in a higher proportion of injuries.
58
▼ -6.5%was 62
Total Crash Events
0
Persons Killed
25
▲ 38.9%was 18
Persons Injured
2
▼ -50.0%was 4
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. 1 crash with unreported severity is not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall, crash trends in Falmouth show a slight decline in the total number of incidents year-over-year, decreasing by 6.5% from 62 to 58 crashes. Fatalities remained stable at zero for both periods. However, the total number of injured persons increased significantly by 38.9%, rising from 18 to 25.
2
Hit-and-Run Crashes — September 2024
▼ -50.0% vs prior (4)
Hit-and-run crashes decreased by 50% year-over-year, falling from 4 incidents in September 2023 to 2 in September 2024. The hit-and-run rate also saw a decline, dropping from 6.5% to 3.4% of all crashes. This indicates a positive trend with fewer hit-and-run incidents reported.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Cyclists Killed
0
Motorists Killed
1
Pedestrians Injured
2
Cyclists Injured
22
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal distribution of crashes shifted year-over-year, with the peak day moving from Saturday in September 2023 (14 crashes) to Friday in September 2024 (14 crashes). Similarly, the peak crash hour shifted from 1 PM (8 crashes) in the prior period to 3 PM (11 crashes) in the current period. Notably, crashes on Fridays doubled from 7 to 14, while Saturday crashes decreased by 71.4% from 14 to 4.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
Fatal crash rates remained at zero for both September 2023 and September 2024. While serious injury crashes decreased from 3 (4.8% of crashes) to 1 (1.7% of crashes), minor injury crashes saw an increase from 12 (19.4% of crashes) to 17 (29.3% of crashes). Overall, the total number of injured persons rose by 38.9%, from 18 to 25.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · Most severe injury per crash record
Top Contributing Factors
Contributing factors showed significant shifts, with 'Inattention' crashes decreasing by 8 incidents, from 18 to 10, and dropping from the top factor to a tie for second. Conversely, 'No improper driving' crashes increased by 4, from 13 to 17, becoming the most frequent factor. 'Followed too closely' crashes rose by 4 incidents (66.7%), from 6 to 10, and 'Failed to yield right of way' crashes saw a substantial increase of 6 incidents (300%), from 2 to 8.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Adverse weather conditions were less prevalent in September 2024 compared to the prior year. Crashes occurring in rainy conditions decreased from 7 to 2, and those on wet road surfaces dropped from 13 to 7. Correspondingly, crashes during daylight hours increased from 43 to 48, suggesting a shift towards crashes occurring in clearer and drier conditions.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · Road surface condition field
Vehicles & Demographics
The total number of vehicles involved in crashes decreased slightly from 115 to 108 year-over-year. While Toyota remained the most common make, its involvement decreased from 21 to 17 vehicles. Notably, Subaru involvement doubled from 4 to 8 vehicles, while Honda and Jeep involvement decreased by 4 vehicles each, from 10 to 6 and 7 to 3 respectively.
Top Vehicle Makes (108 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · Vehicle unit records
10 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (122 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · Person-level records linked to crash events
Speed Limit Zones
Crashes in 25 mph speed zones decreased by 50%, from 8 to 4 incidents, and those in 15 mph zones decreased by 60%, from 5 to 2. Conversely, crashes in 10 mph zones doubled, increasing from 2 to 4 incidents. Crashes in 30 mph zones saw a slight increase from 14 to 15, while 35 mph zones remained stable at 17 crashes.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · 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: 2024-09-01 through 2024-09-30
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2024-09-01 through 2024-09-30 (30 days)
- Geographic scope: FALMOUTH, MA
- Total crash records analyzed: 58
- Total persons involved: 132
- Total vehicles involved: 108
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: September 2024." Published June 21, 2026. Reporting period: 2024-09-01 to 2024-09-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/falmouth/september-2024-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: 2024-09-01 – 2024-09-30
Generated: June 21, 2026 · All rights reserved