ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · WEST NEWBURY, 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/west-newbury/2024-annual-report
Yearly Traffic Safety Analysis
49 CRASHES IN
WEST NEWBURY, MA
2024
In West Newbury, total traffic crashes increased from 45 in 2023 to 49 in 2024, an 8.9% rise. While fatalities remained at zero in both periods, the number of people injured more than doubled, increasing from 3 to 7. The most notable shift was the emergence of a serious injury crash in 2024, a severity level not recorded in the prior year's data.
49
▲ 8.9%was 45
Total Crash Events
0
Persons Killed
7
▲ 133.3%was 3
Persons Injured
4
▲ 100.0%was 2
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.
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
The overall trend in traffic incidents shows a slight increase year-over-year. Total crashes rose from 45 in 2023 to 49 in 2024, representing an 8.9% increase. This increase was accompanied by a more significant rise in the number of injuries, which grew by 133% from 3 to 7.
4
Hit-and-Run Crashes — 2024
▲ 100.0% vs prior (2)
Hit-and-run incidents increased compared to the previous year. The number of hit-and-run crashes doubled from 2 in 2023 to 4 in 2024. Consequently, the hit-and-run rate, representing the percentage of total crashes where a driver left the scene, rose from 4.4% to 8.2%.
Vulnerable Road User Casualties
0
Cyclists Killed
0
Motorists Killed
1
Cyclists Injured
6
Motorists 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 timing of crashes shifted between the two periods. In 2023, the peak day for crashes was Monday with 9 incidents, and the peak hour was 3 p.m. with 6 incidents. In 2024, the peak day shifted to Friday with 12 crashes, and the peak time moved to the morning commute, with both 7 a.m. and 8 a.m. recording 6 crashes each.
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
Crash severity worsened year-over-year, although no fatal crashes occurred in either period. In 2023, all 3 injury-related incidents were classified as 'Minor Injury.' In 2024, the 7 injuries were distributed across 'Serious Injury' (1), 'Minor Injury' (5), and 'Possible Injury' (1). The proportion of crashes resulting in any injury increased from 6.7% in 2023 to 14.3% in 2024.
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 factor in both years was 'No improper driving,' with counts increasing from 20 in 2023 to 24 in 2024. The count for 'Inattention' also rose slightly from 6 to 7 incidents. Conversely, crashes attributed to 'Distracted' driving decreased from 3 in 2023 to 2 in 2024. 'Followed too closely' incidents doubled from 1 to 2.
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
Crashes in clear weather and on dry roads were predominant in both periods and saw an increase in count year-over-year. Incidents in 'Clear' weather rose from 29 to 39, while those on 'Dry' road surfaces increased from 35 to 38. Crashes during daylight hours also increased from 26 to 33. The number of crashes on wet roads decreased from 8 in 2023 to 3 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 makes of vehicles most frequently involved in crashes shifted between periods. In 2023, Subaru and Honda led with 11 vehicles each. In 2024, Toyota and Ford became the most common, with 10 vehicles each, while Subaru's involvement decreased to 5. The number of people aged 45-54 involved in crashes increased from 11 to 15, and the 55-64 age group saw an increase from 8 to 13 people.
Top Vehicle Makes (69 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Vehicle unit records
3 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (79 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
Year-over-year, crashes shifted into higher speed zones. While the 35 mph zone remained the most common location with 17 crashes in both years, incidents in 40 mph zones more than doubled from 6 in 2023 to 14 in 2024. Concurrently, crashes in 25 mph zones decreased from 7 to 2. No fatal crashes were recorded in any speed zone in either period.
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: WEST NEWBURY, MA
- Total crash records analyzed: 49
- Total persons involved: 86
- Total vehicles involved: 69
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). "WEST NEWBURY, 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/west-newbury/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