ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · ASHLAND, MA · 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/ashland/2022-annual-report
Yearly Traffic Safety Analysis
214 CRASHES IN
ASHLAND, MA
2022
In 2022, Ashland recorded 214 total vehicle crashes, a marginal increase from the 213 crashes in 2021. While total injuries decreased from 52 to 45, the most notable year-over-year shift was the occurrence of one fatal crash in 2022, whereas none were reported in the prior year. The leading contributing factor also changed, with crashes attributed to 'Inattention' increasing significantly.
214
▲ 0.5%was 213
Total Crash Events
1
Persons Killed
45
▼ -13.5%was 52
Persons Injured
6
▼ -33.3%was 9
Hit-and-Run Crashes
Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) 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 · 2022-01-01 to 2022-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall crash trends in Ashland remained relatively stable year-over-year, with total incidents increasing by just one crash from 213 to 214. However, the outcomes of these crashes shifted; total injuries saw a 13.5% decrease from 52 to 45, while fatalities rose from zero to one in the same period.
6
Hit-and-Run Crashes — 2022
▼ -33.3% vs prior (9)
Hit-and-run crashes trended downward between the two periods. The total count of hit-and-run incidents fell from 9 in 2021 to 6 in 2022. This resulted in a decrease in the hit-and-run rate, which dropped from 4.2% of all crashes in the prior year to 2.8% in the current year.
Vulnerable Road User Casualties
0
Pedestrians Killed
1
Motorists Killed
7
Pedestrians Injured
38
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal patterns of crashes showed some changes between the two periods. The peak day for crashes shifted from Friday (36 crashes) in 2021 to Tuesday and Wednesday (34 crashes each) in 2022. The 3 p.m. hour remained the single most frequent time for crashes in both years, though the number of incidents during this peak hour declined from 26 to 18.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
While 2021 had no fatal crashes, 2022 saw one fatal crash, representing 0.5% of all incidents. The proportion of crashes resulting in serious injuries decreased from 3.3% (7 crashes) in 2021 to 0.9% (2 crashes) in 2022. Conversely, the share of crashes with minor injuries increased from 7.0% to 8.9% of the total.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Most severe injury per crash record
Top Contributing Factors
A significant shift occurred in the primary contributing factors for crashes. 'Inattention' became the leading factor in 2022, with its count rising from 17 to 50 crashes, a 194% increase. In contrast, crashes where 'No improper driving' was cited fell from being the top factor in 2021 (63 crashes) to 25 crashes in 2022, a 60% decrease in count. The count of crashes involving 'Failed to yield right of way' also grew from 21 to 29.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
The vast majority of crashes in both years occurred during daylight on clear days. However, the role of road surface conditions shifted, with the count of crashes on wet roads more than doubling from 14 in 2021 to 32 in 2022. Consequently, the share of crashes on wet surfaces increased from 6.6% to 15.0% of all incidents.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Road surface condition field
Vehicles & Demographics
The top vehicle makes involved in crashes were largely consistent, with Toyota, Ford, and Chevrolet/Honda leading in both years. Analysis of involved persons shows a decrease in the 16-20 age group (from 53 to 43 individuals) and an increase in the 35-44 age group (from 60 to 72 individuals). The 21-25 age group also saw an increase in involvement from 37 to 47 persons.
Top Vehicle Makes (377 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Vehicle unit records
17 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (404 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · Person-level records linked to crash events
Speed Limit Zones
Crash locations shifted slightly between speed zones year-over-year. The number of crashes in 35 mph zones increased from 90 to 101, and incidents in 25 mph zones rose from 60 to 72. The single fatal crash recorded in 2022 occurred within a 25 mph zone. Crashes in 30 mph zones decreased from 34 in 2021 to 25 in 2022.
Fatal crashes by zone: 25 mph: 1 of 72 (1.389%)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-12-31 · 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: 2022-01-01 through 2022-12-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2022-01-01 through 2022-12-31 (365 days)
- Geographic scope: ASHLAND, MA
- Total crash records analyzed: 214
- Total persons involved: 426
- Total vehicles involved: 377
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). "ASHLAND, MA Crash Intelligence Report: 2022." Published June 21, 2026. Reporting period: 2022-01-01 to 2022-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/ashland/2022-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: 2022-01-01 – 2022-12-31
Generated: June 21, 2026 · All rights reserved