ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · NEWTON, MA · SEPTEMBER 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/september-2022-report
Monthly Traffic Safety Analysis
150 CRASHES IN
NEWTON, MA
SEPTEMBER 2022
Total crashes in NEWTON decreased by 8.54% year-over-year, from 164 crashes in September 2021 to 150 crashes in September 2022. This period also saw a notable 22.2% reduction in total injuries, decreasing from 54 to 42. Fatalities remained at zero in both periods.
150
▼ -8.5%was 164
Total Crash Events
0
Persons Killed
42
▼ -22.2%was 54
Persons Injured
20
▼ -28.6%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 · 2022-09-01 to 2022-09-30 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall, crash data for NEWTON shows a decreasing trend year-over-year, with total crashes falling by 14 incidents from 164 to 150. Total injuries also decreased, dropping by 12 from 54 to 42. Fatalities remained stable at zero in both periods.
20
Hit-and-Run Crashes — September 2022
▼ -28.6% vs prior (28)
The number of hit-and-run crashes decreased from 28 in September 2021 to 20 in September 2022. Consequently, the hit-and-run crash rate also trended downwards, decreasing from 17.1% to 13.3% of all crashes.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Cyclists Killed
0
Motorists Killed
1
Pedestrians Injured
4
Cyclists Injured
37
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-09-01 to 2022-09-30 · 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 between the two periods. The peak day for crashes moved from Wednesday with 39 incidents in September 2021 to Friday with 31 incidents in September 2022. The peak crash hour also shifted from 3 PM with 22 incidents in the prior year to 4 PM with 17 incidents in the current year.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-09-01 to 2022-09-30 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-09-01 to 2022-09-30 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
There were no fatal crashes or fatalities reported in either period. The number of crashes resulting in serious injuries increased slightly from 4 to 5, while minor injury crashes decreased significantly from 26 to 16. Overall, crashes involving any injury (serious, minor, or possible) decreased from 39 to 32.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-09-01 to 2022-09-30 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-09-01 to 2022-09-30 · Most severe injury per crash record
Top Contributing Factors
The leading contributing factor, 'Inattention,' increased by 11 crashes (68.75%) from 16 to 27, moving from the third to the first rank. Conversely, 'No improper driving' decreased by 11 crashes (29.7%) from 37 to 26, shifting from the first to the second rank. 'Followed too closely' also saw a substantial decrease of 12 crashes (42.9%), dropping from 28 to 16 incidents.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-09-01 to 2022-09-30 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes occurring in 'Clear' weather conditions remained the most frequent, with counts slightly decreasing from 101 to 100. Crashes during 'Rain' conditions decreased from 20 to 12, while those on 'Wet' road surfaces significantly dropped from 37 to 20. Crashes in 'Daylight' decreased from 130 to 117, whereas those in 'Dark - lighted roadway' increased from 19 to 22.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-09-01 to 2022-09-30 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-09-01 to 2022-09-30 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-09-01 to 2022-09-30 · Road surface condition field
Vehicles & Demographics
The top three vehicle makes involved in crashes remained Toyota, Honda, and Ford in both periods, though their individual counts decreased. For persons involved, the 0-15 and 16-20 age groups saw increases in their counts, rising from 17 to 26 and 25 to 33 respectively. Conversely, the 21-25 and 26-34 age groups experienced decreases in their counts, falling from 49 to 41 and 88 to 64 respectively.
Top Vehicle Makes (281 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-09-01 to 2022-09-30 · Vehicle unit records
37 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (334 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-09-01 to 2022-09-30 · Person-level records linked to crash events
Speed Limit Zones
Crashes within 25 mph speed zones decreased from 76 to 60 incidents year-over-year. In contrast, crashes in 30 mph zones increased from 23 to 33. There were no fatal crashes recorded in any speed limit zone for either period.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-09-01 to 2022-09-30 · 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: 2022-09-01 through 2022-09-30
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2022-09-01 through 2022-09-30 (30 days)
- Geographic scope: NEWTON, MA
- Total crash records analyzed: 150
- Total persons involved: 368
- Total vehicles involved: 281
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: September 2022." Published June 21, 2026. Reporting period: 2022-09-01 to 2022-09-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/newton/september-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: 2022-09-01 – 2022-09-30
Generated: June 21, 2026 · All rights reserved