ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · READING, MA · MARCH 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/reading/march-2022-report
Monthly Traffic Safety Analysis
33 CRASHES IN
READING, MA
MARCH 2022
In March 2022, READING recorded 33 total crashes, an increase of 17.9% compared to the 28 crashes reported in March 2021. The total number of injuries remained stable at 4 in both periods, and no fatalities were reported in either period. The most notable shift was the 17.9% increase in total crashes year-over-year.
33
▲ 17.9%was 28
Total Crash Events
0
Persons Killed
4
Persons Injured
2
▼ -33.3%was 3
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 · 2022-03-01 to 2022-03-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall, crash incidents in READING increased year-over-year, with total crashes rising from 28 in March 2021 to 33 in March 2022. This represents a 17.9% increase in total crashes during the observed period. The number of injuries and fatalities remained unchanged.
2
Hit-and-Run Crashes — March 2022
▼ -33.3% vs prior (3)
The number of hit-and-run crashes decreased from 3 in March 2021 to 2 in March 2022. This resulted in a decrease in the hit-and-run rate from 10.7% to 6.1% of total crashes, indicating a downward trend in hit-and-run incidents.
Vulnerable Road User Casualties
0
Motorists Killed
4
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · 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 Saturday (5 crashes) in March 2021 to Monday (8 crashes) in March 2022. Similarly, the peak hour for crashes moved from 12 PM (4 crashes) in March 2021 to 3 PM (4 crashes) in March 2022. Crashes during the 6 AM to 8 AM period also saw an increase, with 6 crashes in March 2021 compared to 8 crashes in March 2022.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The number of total injuries remained constant at 4 in both March 2021 and March 2022, with no fatal crashes recorded in either period. Minor injuries increased from 1 crash (3.6% of total) in the prior period to 2 crashes (6.1% of total) in the current period, while possible injuries decreased from 3 crashes (10.7% of total) to 2 crashes (6.1% of total). The proportion of crashes resulting in no injury remained largely stable, at 85.7% in the prior period and 87.9% in the current period.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · Most severe injury per crash record
Top Contributing Factors
The leading contributing factor, "No improper driving," saw a significant increase from 4 crashes in March 2021 to 13 crashes in March 2022. Conversely, "Inattention" decreased from 7 crashes to 4 crashes year-over-year. The factor "Followed too closely" remained stable with 5 crashes in both periods, while "Failed to yield right of way" increased from 2 crashes to 3 crashes.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Daylight conditions accounted for an increased number of crashes, rising from 23 in March 2021 to 30 in March 2022, while crashes in dark-lighted roadway conditions decreased from 4 to 1. The prevalence of adverse road surface conditions such as ice and snow, which were not present in March 2021, accounted for 5 crashes in March 2022. Dry road crashes decreased from 26 to 24, while wet road crashes increased from 2 to 4.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · Road surface condition field
Vehicles & Demographics
The total number of vehicles involved in crashes increased from 56 in March 2021 to 72 in March 2022. There was a notable increase in crashes involving drivers aged 21-25, rising from 7 to 14, and those aged 55-64, increasing from 6 to 11. The number of male drivers involved in crashes increased from 35 to 49, while female drivers decreased from 34 to 30.
Top Vehicle Makes (72 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · Vehicle unit records
2 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 · 2022-03-01 to 2022-03-31 · Person-level records linked to crash events
Speed Limit Zones
Crashes in 30 mph zones decreased from 12 in March 2021 to 7 in March 2022. Conversely, crashes in 35 mph zones increased from 1 to 5, and crashes in 55 mph zones increased from 8 to 12. Crashes in 65 mph zones also increased, from 2 to 4, indicating a shift towards higher speed zones.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-03-01 to 2022-03-31 · 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: 2022-03-01 through 2022-03-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2022-03-01 through 2022-03-31 (31 days)
- Geographic scope: READING, MA
- Total crash records analyzed: 33
- Total persons involved: 84
- Total vehicles involved: 72
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). "READING, MA Crash Intelligence Report: March 2022." Published June 21, 2026. Reporting period: 2022-03-01 to 2022-03-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/reading/march-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: 2022-03-01 – 2022-03-31
Generated: June 21, 2026 · All rights reserved