ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · FALL RIVER, MA · 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/fall-river/2024-annual-report
Yearly Traffic Safety Analysis
3,080 CRASHES IN
FALL RIVER, MA
2024
In Fall River, total vehicle crashes increased by 2.8%, from 2,997 in 2023 to 3,080 in 2024. While total injuries decreased, the number of fatalities rose from 3 to 4. The most notable year-over-year shift was a 68% increase in crashes involving a driver suspected of being under the influence of alcohol, which rose from 25 to 42 incidents.
3,080
▲ 2.8%was 2,997
Total Crash Events
4
▲ 33.3%was 3
Persons Killed
1,071
▼ -6.5%was 1,145
Persons Injured
428
▲ 25.1%was 342
Hit-and-Run Crashes
Note: "Persons Killed" (4) counts individual fatalities across all crash events. "Fatal" in the severity table below (4) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 177 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Crash data indicates a slight upward trend in the total number of collisions, which rose by 2.8% from 2,997 to 3,080 year-over-year. Despite this increase in crashes, the number of people injured fell by 6.5%, from 1,145 to 1,071. However, the number of fatalities increased from 3 in 2023 to 4 in 2024.
428
Hit-and-Run Crashes — 2024
▲ 25.1% vs prior (342)
Hit-and-run crashes showed a significant upward trend. The number of such incidents increased by 25.1%, from 342 in 2023 to 428 in 2024. Consequently, the hit-and-run rate, which represents the proportion of all crashes that are hit-and-runs, rose from 11.4% to 13.9% year-over-year.
Vulnerable Road User Casualties
2
Pedestrians Killed
0
Cyclists Killed
1
Motorists Killed
1
Other Killed
68
Pedestrians Injured
15
Cyclists Injured
983
Motorists Injured
5
Other Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · 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. The peak day for collisions moved from Monday (472 crashes) in 2023 to Friday (501 crashes) in 2024. Similarly, the peak hour for crashes occurred later in the day, shifting from 3 PM in the prior year (287 crashes) to 4 PM in the current year (266 crashes).
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The fatal crash rate saw a minor increase, rising from 0.10% of all crashes in 2023 to 0.13% in 2024. While the count of serious injury crashes increased from 45 to 53, the overall proportion of crashes resulting in any level of injury (serious, minor, or possible) declined from 27.1% to 24.5%. This was primarily driven by a drop in the share of crashes classified as resulting in minor or possible injuries.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Most severe injury per crash record
Top Contributing Factors
The leading contributing factors remained consistent, with "No improper driving," "Inattention," and "Failed to yield right of way" as the top three in both years. The count of crashes where no improper driving was noted increased from 803 to 996. A notable change was the increase in crashes attributed to "Disregarded traffic signs, signals, road markings," with the count rising from 97 in 2023 to 135 in 2024.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
The distribution of crashes across environmental conditions remained largely consistent year-over-year. Crashes in daylight accounted for approximately 69% of all incidents in both periods, while collisions on dry roads made up about 84% of the total in both years. There was a small decrease in the proportion of crashes occurring on wet road surfaces, which fell from 15.0% in 2023 to 13.1% in 2024.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Road surface condition field
Vehicles & Demographics
The top three vehicle makes involved in crashes—Toyota, Honda, and Ford—were the same in both periods, with their rankings unchanged. The number of Toyotas involved increased from 882 to 952, while counts for Honda and Ford were nearly static. The age distribution of individuals involved in crashes also remained stable, with no significant shifts observed across any age group.
Top Vehicle Makes (6,000 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Vehicle unit records
1,364 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (5,804 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Person-level records linked to crash events
Speed Limit Zones
There was a significant shift in where crashes occurred, with collisions in 25 mph zones increasing from 958 to 1,219, while those in 30 mph zones fell from 1,316 to 1,053. The location of fatal crashes also changed; in 2024, three of the four fatal crashes happened in 25 or 30 mph zones. In contrast, all three fatal crashes in 2023 occurred in higher speed zones of 55 mph or 65 mph.
Fatal crashes by zone: 25 mph: 2 of 1,219 (0.164%) · 30 mph: 1 of 1,053 (0.095%)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · 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: 2024-01-01 through 2024-12-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2024-01-01 through 2024-12-31 (366 days)
- Geographic scope: FALL RIVER, MA
- Total crash records analyzed: 3,080
- Total persons involved: 7,441
- Total vehicles involved: 6,000
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). "FALL RIVER, MA Crash Intelligence Report: 2024." Published June 21, 2026. Reporting period: 2024-01-01 to 2024-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/fall-river/2024-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: 2024-01-01 – 2024-12-31
Generated: June 21, 2026 · All rights reserved