ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · NEW BEDFORD, 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/new-bedford/january-2023-report
Monthly Traffic Safety Analysis
305 CRASHES IN
NEW BEDFORD, MA
JANUARY 2023
In January 2023, New Bedford experienced 305 total crashes, a decrease from the 318 crashes recorded in January 2022, representing a 4.1% reduction. The most notable year-over-year shift was the absence of traffic fatalities in January 2023, compared to one fatality in the prior year. However, total injuries increased significantly during the same period.
305
▼ -4.1%was 318
Total Crash Events
0
▼ -100.0%was 1
Persons Killed
92
▲ 37.3%was 67
Persons Injured
24
▼ -4.0%was 25
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. 33 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, total crashes in New Bedford decreased by 4.1%, from 318 in January 2022 to 305 in January 2023. While fatal crashes dropped from one to zero, total injuries rose by 37.3%, from 67 in January 2022 to 92 in January 2023. This indicates a decrease in crash frequency but an increase in injury severity among reported incidents.
24
Hit-and-Run Crashes — January 2023
▼ -4.0% vs prior (25)
Hit-and-run crashes decreased slightly from 25 in January 2022 to 24 in January 2023. Despite this small decrease in count, the hit-and-run rate remained stable at 7.9% of total crashes for both periods.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Cyclists Killed
0
Motorists Killed
7
Pedestrians Injured
1
Cyclists Injured
84
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 temporal patterns of crashes shifted year-over-year, with the peak day moving from Monday and Wednesday in January 2022 (59 crashes each) to Friday in January 2023 (48 crashes). The peak crash hour also changed, occurring at 4 PM with 29 crashes in January 2022, but shifting to 3 PM with 35 crashes in January 2023. Crashes on Mondays and Wednesdays saw decreases of 18 and 14 respectively, while Friday crashes increased by 9.
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
Fatal crashes decreased from one in January 2022 to zero in January 2023. The number of serious injury crashes remained constant at 4 for both periods. However, minor injury crashes increased by 38.7%, from 31 in January 2022 to 43 in January 2023, and possible injury crashes rose by 13.6%, from 22 to 25. This resulted in an overall increase in total injuries despite fewer total crashes.
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
Among contributing factors, 'Failed to yield right of way' crashes increased by 11 (61.1%), rising from 18 in January 2022 to 29 in January 2023. 'Inattention' also saw an increase of 8 crashes (38.1%), from 21 to 29. Conversely, crashes attributed to 'Driving too fast for conditions' decreased significantly by 13 (81.3%), from 16 to 3. 'No improper driving' crashes decreased by 9 (10.7%), from 84 to 75.
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
Adverse weather conditions played a different role year-over-year; crashes occurring in rain increased by 21 (100%), from 21 in January 2022 to 42 in January 2023. Conversely, crashes during snow conditions decreased by 15 (65.2%), from 23 to 8. Road surface conditions showed a similar trend, with wet road crashes increasing by 58 (111.5%) and snow/ice-related crashes decreasing by 31 (86.1%) and 16 (94.1%) respectively.
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 slightly from 612 in January 2022 to 593 in January 2023. Toyota and Ford vehicles involved in crashes increased by 12 and 10 respectively, while Honda and Chevrolet vehicles involved decreased by 12 and 20. The age group 35-44 saw the largest increase in persons involved, rising by 27 from 89 to 116, while the 26-34 age group experienced the largest decrease, falling by 17 from 122 to 105.
Top Vehicle Makes (593 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Vehicle unit records
145 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (556 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 the 30 mph speed zone decreased by 20, from 202 in January 2022 to 182 in January 2023, with the single fatality from the prior year occurring in this zone. Conversely, crashes in the 25 mph speed zone increased by 12, from 30 to 42. Crashes in the 65 mph zone decreased by 7, from 15 to 8.
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: NEW BEDFORD, MA
- Total crash records analyzed: 305
- Total persons involved: 732
- Total vehicles involved: 593
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: 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/new-bedford/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