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YEAR-OVER-YEAR CRASH REPORT · FALL RIVER, 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/fall-river/january-2023-report
Monthly Traffic Safety Analysis
229 CRASHES IN
FALL RIVER, MA
JANUARY 2023
In January 2023, Fall River, MA experienced 229 total crashes, a decrease of 13.26% compared to the 264 crashes reported in January 2022. Total injuries also saw a slight reduction from 72 to 68. The most significant year-over-year shift was an 80% decrease in DUI-related crashes, falling from 5 in January 2022 to 1 in January 2023.
229
▼ -13.3%was 264
Total Crash Events
0
Persons Killed
68
▼ -5.6%was 72
Persons Injured
17
▲ 70.0%was 10
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. 7 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, crashes in Fall River, MA showed a downward trend year-over-year, with total crashes decreasing by 13.26% from 264 in January 2022 to 229 in January 2023. This represents a reduction of 35 crashes. Total injuries also decreased by 5.56%, from 72 to 68.
17
Hit-and-Run Crashes — January 2023
▲ 70.0% vs prior (10)
Hit-and-run crashes increased by 70% year-over-year, rising from 10 crashes in January 2022 to 17 crashes in January 2023. The hit-and-run rate also increased from 3.8% of total crashes in the prior period to 7.4% in the current period.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Cyclists Killed
0
Motorists Killed
5
Pedestrians Injured
2
Cyclists Injured
61
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 peak day for crashes shifted from Wednesday (44 crashes) in January 2022 to Thursday (42 crashes) in January 2023. The peak hour also changed, moving from 2 PM (27 crashes) in the prior period to 3 PM (21 crashes) in the current period. Crashes on Sundays decreased by 14, from 42 to 28, while crashes on Thursdays increased by 3, from 39 to 42.
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
There were no fatal crashes or fatalities reported in either January 2023 or January 2022. Total injuries decreased by 5.56%, from 72 to 68. Serious injuries decreased by 66.67%, from 3 to 1, while minor injuries saw a slight decrease from 39 to 38.
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 number of crashes with 'No improper driving' as a factor decreased by 19, from 73 in the prior period to 54 in the current period. Conversely, crashes attributed to 'Followed too closely' increased by 7, from 21 to 28, representing a 33.33% rise. 'Other improper action' also increased by 8 crashes, from 16 to 24, a 50% increase.
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
Crashes occurring in 'Clear' weather conditions decreased by 69, from 168 in the prior period to 99 in the current period, while crashes in 'Rain' conditions increased by 19, from 10 to 29. The number of crashes on 'Dry' road surfaces decreased by 39, from 183 to 144. Conversely, crashes on 'Wet' road surfaces increased by 49, from 31 to 80, a 158.06% rise.
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 8.1%, from 494 in January 2022 to 454 in January 2023. The age group 26-34 saw the largest decrease in persons involved in crashes, dropping by 43 from 140 to 97. In terms of vehicle makes, NISSAN saw a 35.9% decrease in involvement, dropping from 39 to 25, while TOYOTA involvement increased by 5, from 70 to 75.
Top Vehicle Makes (454 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Vehicle unit records
83 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (469 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
Crashes in 30 mph speed zones decreased by 59, from 171 in January 2022 to 112 in January 2023, a 34.5% reduction. Conversely, crashes in 25 mph zones increased significantly by 42, from 33 to 75, a 127.27% rise. 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: FALL RIVER, MA
- Total crash records analyzed: 229
- Total persons involved: 581
- Total vehicles involved: 454
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: 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/fall-river/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