Monthly Traffic Safety Analysis

17 CRASHES IN
NORTH READING, MA
SEPTEMBER 2024

All metrics benchmarked againstSeptember 2023

In September 2024, NORTH READING experienced 17 crashes, a decrease of 26.1% compared to the 23 crashes reported in September 2023. The most notable year-over-year shift was the 80% reduction in total injuries, from 5 in the prior period to 1 in the current period.

17

-26.1%was 23

Total Crash Events

0

Persons Killed

1

-80.0%was 5

Persons Injured

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 · 2024-09-01 to 2024-09-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Total crashes in NORTH READING decreased by 26.1% year-over-year, from 23 crashes in September 2023 to 17 crashes in September 2024. This indicates a downward trend in overall crash incidents for the month.

1

Hit-and-Run Crashes — September 2024

5.9% hit-and-run rate this period vs 0.0% prior. Prior period: 0.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

1

Motorists Injured

Prior: 5-80.0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · 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 Thursday with 5 crashes in September 2023 to Monday with 5 crashes in September 2024. The peak crash hour also changed, moving from 8 AM with 5 crashes in the prior year to 5 PM with 3 crashes in the current year.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Both periods recorded no fatal crashes. Total injuries decreased significantly from 5 in September 2023 to 1 in September 2024, an 80% reduction. The proportion of crashes resulting in 'Possible Injury' decreased from 8.7% (2 crashes) in the prior year to 5.9% (1 crash) in the current year, while 'Minor Injury' crashes were absent in the current period compared to 8.7% (2 crashes) previously.

Outcome by Severity (Crash Events)

Possible Injury1possible injury crashes5.9%
-50.0%prior 2
No Injury16no injury crashes94.1%
-5.9%prior 17

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · Most severe injury per crash record

Top Contributing Factors

The number of crashes attributed to 'Failed to yield right of way' increased from 3 in September 2023 to 7 in September 2024. 'No improper driving' remained constant at 6 crashes in both periods. 'Followed too closely' decreased from 2 crashes in the prior year to 1 crash in the current year.

Officer-Reported Primary Contributing Cause

Failed to yield right of way7 (41.2%)
No improper driving6 (35.3%)0.0%prior 6
Distracted1 (5.9%)
Failure to keep in proper lane or running off road1 (5.9%)
Followed too closely1 (5.9%)
Other improper action1 (5.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in 'Clear' weather conditions decreased from 16 in September 2023 to 12 in September 2024. The number of crashes on 'Wet' road surfaces decreased from 5 in the prior year to 1 in the current year, while 'Dry' road surface crashes decreased from 18 to 16.

Weather

Clear12 (70.6%)
-25.0%prior 16
Cloudy2 (11.8%)
Clear/Other1 (5.9%)
Clear/Unknown1 (5.9%)
Rain1 (5.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · Weather condition at time of crash

Road Surface

Dry16 (94.1%)
-11.1%prior 18
Wet1 (5.9%)
-80.0%prior 5

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · Road surface condition field

Vehicles & Demographics

Top Vehicle Makes (32 vehicles)

1
HONDA4 (12.5%)
-33.3%prior 6
2
CHEVROLET3 (9.4%)
3
MERCEDES-BENZ3 (9.4%)
4
FORD3 (9.4%)
5
SUBARU3 (9.4%)
6
JEEP2 (6.3%)
7
LEXUS2 (6.3%)
8
TOYOTA2 (6.3%)
-66.7%prior 6
9
INFI1 (3.1%)
10
CRMT1 (3.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · Vehicle unit records

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

Sex Distribution (39 persons with recorded sex)

Male24 (61.5%)
26.3%prior 19
Female15 (38.5%)
-28.6%prior 21

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · Person-level records linked to crash events

Speed Limit Zones

Crashes occurring in 30 mph speed zones decreased from 11 in September 2023 to 4 in September 2024. Conversely, crashes in 35 mph zones increased from 1 in the prior year to 6 in the current year. There were no fatal crashes reported in any speed zone during either period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-09-01 to 2024-09-30 · 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: 2024-09-01 through 2024-09-30
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2024-09-01 through 2024-09-30 (30 days)
  • Geographic scope: NORTH READING, MA
  • Total crash records analyzed: 17
  • Total persons involved: 41
  • Total vehicles involved: 32

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: September 2024." Published June 21, 2026. Reporting period: 2024-09-01 to 2024-09-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/north-reading/september-2024-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 — September 2024 | ThatCarHitMe.com