ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · NEWTON, MA · OCTOBER 2022
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/newton/october-2022-report
Monthly Traffic Safety Analysis
151 CRASHES IN
NEWTON, MA
OCTOBER 2022
Total crashes in October 2022 were 151, a decrease of 1.9% from 154 crashes in October 2021. Fatalities remained at zero for both periods. The most notable shift was a 200% increase in DUI-related crashes, rising from 1 in October 2021 to 3 in October 2022.
151
▼ -1.9%was 154
Total Crash Events
0
Persons Killed
45
▼ -8.2%was 49
Persons Injured
30
▲ 7.1%was 28
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. 8 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall, total crashes in October 2022 saw a slight decrease of 1.9% compared to October 2021, moving from 154 to 151 crashes. This indicates a relatively stable trend in total crash volume year-over-year.
30
Hit-and-Run Crashes — October 2022
▲ 7.1% vs prior (28)
Hit-and-run crashes increased from 28 in October 2021 to 30 in October 2022. Concurrently, the hit-and-run rate rose from 18.2% to 19.9% of total crashes. This indicates an upward trend in both the count and proportion of hit-and-run incidents.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Cyclists Killed
0
Motorists Killed
0
Other Killed
3
Pedestrians Injured
2
Cyclists Injured
39
Motorists Injured
1
Other Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · 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 Friday (33 crashes) in October 2021 to Thursday (26 crashes) in October 2022. The peak hour also shifted, from 6 PM (18 crashes) in the prior period to 5 PM (17 crashes) in the current period. Notably, crashes on Fridays decreased by 10, while crashes during the 5 PM hour increased by 7.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
There were no fatalities reported in either October 2021 or October 2022. Total injuries decreased by 8.2%, from 49 in the prior period to 45 in the current period. The share of serious injury crashes increased from 1.9% to 2.6%, while minor injury crashes decreased from 19.5% to 15.2% of total crashes.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Most severe injury per crash record
Top Contributing Factors
No improper driving remained the top contributing factor, increasing in count from 27 to 39. Failed to yield right of way saw a substantial increase of 10 crashes, rising from 7 to 17. Conversely, Inattention decreased by 5 crashes, moving from 24 to 19, and Driving too fast for conditions decreased by 4 crashes, from 6 to 2.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes occurring in Dry road surface conditions decreased from 121 to 110, while those on Wet surfaces increased from 30 to 39. For lighting conditions, Dark - lighted roadway crashes increased from 32 to 35, whereas Dark - roadway not lighted incidents decreased from 6 to 1. Clear weather conditions saw a slight increase in associated crashes from 98 to 100.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Road surface condition field
Vehicles & Demographics
The age groups 0-15, 16-20, 21-25, 45-54, and 55-64 all saw an increase in persons involved in crashes. Specifically, persons aged 35-44 saw the largest decrease, from 64 to 49. Among vehicle makes, Toyota, Honda, and Ford remained the top three, with Ford showing the largest count increase from 21 to 33.
Top Vehicle Makes (289 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Vehicle unit records
36 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (329 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · Person-level records linked to crash events
Speed Limit Zones
Crashes in 25 mph zones slightly decreased from 61 to 58, while those in 30 mph zones saw a minor increase from 32 to 33. There was a notable decrease in crashes within 55 mph zones, falling from 34 to 26. Conversely, crashes in 45 mph zones increased from 1 to 6.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-10-01 to 2022-10-31 · 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: 2022-10-01 through 2022-10-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2022-10-01 through 2022-10-31 (31 days)
- Geographic scope: NEWTON, MA
- Total crash records analyzed: 151
- Total persons involved: 364
- Total vehicles involved: 289
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). "NEWTON, MA Crash Intelligence Report: October 2022." Published June 21, 2026. Reporting period: 2022-10-01 to 2022-10-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/newton/october-2022-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: 2022-10-01 – 2022-10-31
Generated: June 21, 2026 · All rights reserved