Monthly Traffic Safety Analysis

21 CRASHES IN
NORTH READING, MA
DECEMBER 2022

All metrics benchmarked againstDecember 2021

In December 2022, NORTH READING experienced 21 total crashes, marking a 31.25% increase compared to the 16 crashes recorded in December 2021. Injuries also rose from 3 to 4, representing a 33.33% increase. The most notable shift was the increase in crashes attributed to 'Failed to yield right of way,' which more than doubled.

21

31.3%was 16

Total Crash Events

0

Persons Killed

4

33.3%was 3

Persons Injured

0

-100.0%was 1

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 · 2022-12-01 to 2022-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crash incidents in NORTH READING increased year-over-year, with total crashes rising from 16 in December 2021 to 21 in December 2022, an increase of 31.25%. Similarly, total injuries increased by 33.33%, from 3 in the prior period to 4 in the current period.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

4

Motorists Injured

Prior: 333.3%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-12-01 to 2022-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The peak day for crashes shifted from Friday in December 2021 (4 crashes) to Sunday in December 2022 (5 crashes), with Sunday seeing a significant increase from 0 crashes in the prior period. The peak hour remained consistent, with 4 crashes occurring at 5 PM in December 2022 and 4 crashes at 4 PM in December 2021. Crashes on Wednesday decreased from 4 in the prior period to 1 in the current period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-12-01 to 2022-12-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-12-01 to 2022-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The distribution of crash severity showed a slight shift, with minor injury crashes increasing from 2 (12.5% of total crashes) in December 2021 to 3 (14.3% of total crashes) in December 2022. Possible injury crashes, which accounted for 1 incident (6.3%) in the prior period, were not reported in the current period. Both periods recorded no fatal crashes.

Outcome by Severity (Crash Events)

Minor Injury3minor injury crashes14.3%
50.0%prior 2
No Injury18no injury crashes85.7%
38.5%prior 13

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-12-01 to 2022-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-12-01 to 2022-12-31 · Most severe injury per crash record

Top Contributing Factors

Among common contributing factors, 'Failed to yield right of way' saw a significant increase, rising from 3 crashes in December 2021 to 7 crashes in December 2022. 'No improper driving' also increased from 2 crashes to 6 crashes year-over-year. Factors such as 'Operating vehicle in erratic, reckless, careless, negligent or aggressive manner,' 'Over-correcting/over-steering,' and 'Exceeded authorized speed limit' were each cited in 1 crash in December 2022 but were not present in the top factors for December 2021. Conversely, 'Other improper action,' 'Inattention,' and 'Visibility obstructed' were contributing factors in December 2021 but not in December 2022.

Officer-Reported Primary Contributing Cause

Failed to yield right of way7 (33.3%)
No improper driving6 (28.6%)
Failure to keep in proper lane or running off road1 (4.8%)
Followed too closely1 (4.8%)
Made an improper turn1 (4.8%)
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner1 (4.8%)
Over-correcting/over-steering1 (4.8%)
Emotional1 (4.8%)
Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway1 (4.8%)
Exceeded authorized speed limit1 (4.8%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-12-01 to 2022-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in 'Rain' conditions doubled, increasing from 2 in December 2021 to 4 in December 2022. The number of crashes on 'Wet' road surfaces increased from 2 to 5 year-over-year. December 2022 also saw crashes reported in 'Snow/Sleet, hail (freezing rain or drizzle)' (1 crash) and 'Snow' (1 crash) conditions, which were not present in the prior period. Crashes in 'Daylight' increased from 7 to 9, and crashes in 'Dark - lighted roadway' increased from 8 to 9.

Weather

Clear9 (42.9%)
-10.0%prior 10
Rain4 (19.0%)
Clear/Unknown2 (9.5%)
Cloudy2 (9.5%)
Snow/Sleet, hail (freezing rain or drizzle)1 (4.8%)
Clear/Cloudy1 (4.8%)
Cloudy/Rain1 (4.8%)
Snow1 (4.8%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-12-01 to 2022-12-31 · Weather condition at time of crash

Lighting

Dark - lighted roadway9 (42.9%)
12.5%prior 8
Daylight9 (42.9%)
28.6%prior 7
Dark - roadway not lighted2 (9.5%)
Dawn1 (4.8%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-12-01 to 2022-12-31 · Lighting condition field

Road Surface

Dry13 (61.9%)
-7.1%prior 14
Wet5 (23.8%)
Ice2 (9.5%)
Snow1 (4.8%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-12-01 to 2022-12-31 · Road surface condition field

Vehicles & Demographics

Top Vehicle Makes (35 vehicles)

1
JEEP5 (14.3%)
2
HONDA4 (11.4%)
-20.0%prior 5
3
CHEVROLET4 (11.4%)
4
HYUNDAI3 (8.6%)
5
BMW3 (8.6%)
6
VOLKSWAGEN2 (5.7%)
7
FORD2 (5.7%)
8
TOYOTA2 (5.7%)
-60.0%prior 5
9
CHRYSLER1 (2.9%)
10
LEXUS1 (2.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-12-01 to 2022-12-31 · Vehicle unit records

1 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (34 persons with recorded sex)

Male20 (58.8%)
42.9%prior 14
Female14 (41.2%)
-33.3%prior 21

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-12-01 to 2022-12-31 · Person-level records linked to crash events

Speed Limit Zones

Crashes within 30 mph speed zones increased significantly, rising from 4 in December 2021 to 10 in December 2022. Crashes in 35 mph zones also increased from 1 to 4. Conversely, crashes in 40 mph zones decreased from 8 to 5 year-over-year. Neither period recorded any fatal crashes across any speed limit zone.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-12-01 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: 2022-12-01 through 2022-12-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2022-12-01 through 2022-12-31 (31 days)
  • Geographic scope: NORTH READING, MA
  • Total crash records analyzed: 21
  • Total persons involved: 36
  • Total vehicles involved: 35

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). "NORTH READING, MA Crash Intelligence Report: December 2022." Published June 21, 2026. Reporting period: 2022-12-01 to 2022-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/north-reading/december-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

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North Reading, MA Crash Report — December 2022 | ThatCarHitMe.com