ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · NEW BEDFORD, MA · NOVEMBER 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/new-bedford/november-2024-report
Monthly Traffic Safety Analysis
314 CRASHES IN
NEW BEDFORD, MA
NOVEMBER 2024
In November 2024, New Bedford recorded 314 crashes, a 3.3% increase from the 304 crashes reported in November 2023. Notably, there were no fatalities in November 2024, a decrease from one fatality in the prior year. Total injuries decreased by 13.6% from 81 to 70 year-over-year.
314
▲ 3.3%was 304
Total Crash Events
0
▼ -100.0%was 1
Persons Killed
70
▼ -13.6%was 81
Persons Injured
43
▼ -12.2%was 49
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. 22 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-11-01 to 2024-11-30 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall, total crashes in New Bedford saw a slight increase of 3.3%, rising from 304 in November 2023 to 314 in November 2024. This increase in crash volume was accompanied by a positive trend in safety outcomes, with total fatalities decreasing from one to zero and total injuries falling by 13.6% from 81 to 70.
43
Hit-and-Run Crashes — November 2024
▼ -12.2% vs prior (49)
Hit-and-run crashes decreased from 49 in November 2023 to 43 in November 2024. The hit-and-run rate also saw a decline, moving from 16.1% in the prior period to 13.7% in the current period, indicating a downward trend in these incidents.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Cyclists Killed
0
Motorists Killed
12
Pedestrians Injured
5
Cyclists Injured
53
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-11-01 to 2024-11-30 · 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 Thursday in November 2023 (57 crashes) to Friday in November 2024 (57 crashes). Similarly, the peak hour for crashes moved from 3 p.m. (29 crashes) in the prior period to 5 p.m. (34 crashes) in the current period. Crashes on Friday increased significantly by 18, from 39 to 57, while Monday crashes decreased by 13, from 54 to 41.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-11-01 to 2024-11-30 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-11-01 to 2024-11-30 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
Fatal crashes decreased from one in November 2023 to zero in November 2024. Serious injuries, however, saw a slight increase from 2 to 3. Minor injuries decreased from 39 to 32, and possible injuries also decreased from 19 to 14, contributing to an overall reduction in total injuries.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-11-01 to 2024-11-30 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-11-01 to 2024-11-30 · Most severe injury per crash record
Top Contributing Factors
The leading contributing factor, 'No improper driving', increased by 43 crashes, rising from 92 in November 2023 to 135 in November 2024, and its share increased from 30.3% to 43%. Conversely, 'Inattention' decreased by 11 crashes, from 32 to 21, representing a 34.4% reduction in count. 'Failed to yield right of way' also saw a decrease of 6 crashes, from 25 to 19.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-11-01 to 2024-11-30 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes occurring in 'Clear' weather conditions decreased by 10, from 236 in November 2023 to 226 in November 2024. Conversely, crashes during 'Rain' increased by 9, from 10 to 19. Crashes on 'Wet' road surfaces increased by 12, from 28 to 40, while crashes in 'Dark - roadway not lighted' conditions decreased by 10, from 15 to 5.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-11-01 to 2024-11-30 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-11-01 to 2024-11-30 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-11-01 to 2024-11-30 · Road surface condition field
Vehicles & Demographics
The total number of vehicles involved decreased slightly from 613 to 609. Toyota remained the top make involved, increasing from 88 to 97 vehicles, while Honda saw a notable increase of 15 vehicles, from 60 to 75. In terms of age distribution, the 65+ age group experienced a significant increase of 27 persons involved in crashes, rising from 48 to 75, and the 26-34 age group increased by 26 persons, from 113 to 139.
Top Vehicle Makes (609 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-11-01 to 2024-11-30 · Vehicle unit records
141 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (598 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-11-01 to 2024-11-30 · Person-level records linked to crash events
Speed Limit Zones
The highest number of crashes in both periods occurred in the 30 mph speed zone, with a slight increase from 190 to 194 crashes. Crashes in the 35 mph zone increased significantly from 9 to 24. There was a notable decrease in crashes within the 65 mph speed zone, dropping from 17 crashes with one fatality in November 2023 to 7 crashes with no fatalities in November 2024.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-11-01 to 2024-11-30 · 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: 2024-11-01 through 2024-11-30
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2024-11-01 through 2024-11-30 (30 days)
- Geographic scope: NEW BEDFORD, MA
- Total crash records analyzed: 314
- Total persons involved: 756
- Total vehicles involved: 609
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). "NEW BEDFORD, MA Crash Intelligence Report: November 2024." Published June 21, 2026. Reporting period: 2024-11-01 to 2024-11-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/new-bedford/november-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: 2024-11-01 – 2024-11-30
Generated: June 21, 2026 · All rights reserved