ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · MILTON, MA · SEPTEMBER 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/milton/september-2023-report
Monthly Traffic Safety Analysis
61 CRASHES IN
MILTON, MA
SEPTEMBER 2023
Total crashes in Milton increased by 19.6%, from 51 in September 2022 to 61 in September 2023. Concurrently, the total number of injuries rose by 37.5%, from 16 to 22. A notable shift was the 100% increase in crashes attributed to speeding, rising from 2 to 4.
61
▲ 19.6%was 51
Total Crash Events
0
Persons Killed
22
▲ 37.5%was 16
Persons Injured
2
▼ -66.7%was 6
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 · 2023-09-01 to 2023-09-30 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall, crash data for September 2023 indicates an upward trend compared to the previous year, with total crashes rising by 19.6% from 51 to 61. This increase in crash volume was accompanied by a 37.5% rise in total injuries, from 16 to 22. No fatalities were reported in either period.
2
Hit-and-Run Crashes — September 2023
▼ -66.7% vs prior (6)
Hit-and-run crashes saw a significant decrease year-over-year, falling by 66.7% from 6 crashes in September 2022 to 2 crashes in September 2023. Consequently, the hit-and-run rate also declined substantially, from 11.8% to 3.3% of all crashes.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Cyclists Killed
0
Motorists Killed
1
Pedestrians Injured
1
Cyclists Injured
20
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · 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 year-over-year. In September 2023, both Wednesday and Saturday recorded the highest number of crashes with 13 each, whereas Thursday was the peak day in September 2022 with 12 crashes. The peak hour for crashes also shifted from 5 PM with 6 crashes in the prior period to 7 PM with 5 crashes in the current period.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
No fatal crashes were reported in either September 2023 or September 2022. The total number of injured persons increased by 37.5%, from 16 in the prior period to 22 in the current period. While minor injury crashes remained proportionally stable (19.7% vs 19.6%), serious injury crashes, which were not reported in September 2022, accounted for 3.3% of crashes in September 2023.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Most severe injury per crash record
Top Contributing Factors
Contributing factors showed several shifts year-over-year. Crashes attributed to 'Failed to yield right of way' increased by 3 crashes, a 150% rise in count, while 'Distracted' and 'Driving too fast for conditions' both doubled in count, increasing by 2 crashes and 1 crash respectively. 'Followed too closely' crashes increased by 2 (40% in count), and 'No improper driving' crashes saw a slight increase of 1 crash (5.3% in count), though its share of total crashes decreased from 37.3% to 32.8%.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes occurring in rainy conditions significantly increased from 2 in September 2022 to 15 in September 2023. Similarly, crashes on wet road surfaces more than doubled, rising from 7 to 16. Crashes during daylight hours increased from 25 to 30, and crashes during dusk saw an increase from 0 to 6.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Road surface condition field
Vehicles & Demographics
The total number of vehicles involved increased from 103 to 109. Among top vehicle makes, HONDA crashes increased from 16 to 20, and TOYOTA crashes increased from 16 to 19, while JEEP crashes decreased from 7 to 5. Significant demographic shifts were observed in persons involved, with those aged 35-44 increasing from 17 to 30, and those aged 65+ tripling from 4 to 12, whereas involvement of persons aged 45-54 decreased from 22 to 10.
Top Vehicle Makes (109 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Vehicle unit records
11 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (130 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Person-level records linked to crash events
Speed Limit Zones
Crashes in 20 mph zones doubled from 1 to 2, and those in 25 mph zones tripled from 1 to 3. Crashes in 35 mph zones also increased from 6 to 8, while 30 mph zones saw a slight decrease from 10 to 9 crashes. No fatal crashes were reported in any speed zone for either period.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · 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: 2023-09-01 through 2023-09-30
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2023-09-01 through 2023-09-30 (30 days)
- Geographic scope: MILTON, MA
- Total crash records analyzed: 61
- Total persons involved: 144
- Total vehicles involved: 109
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). "MILTON, MA Crash Intelligence Report: September 2023." Published June 21, 2026. Reporting period: 2023-09-01 to 2023-09-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/milton/september-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: 2023-09-01 – 2023-09-30
Generated: June 21, 2026 · All rights reserved