Monthly Traffic Safety Analysis

35 CRASHES IN
READING, MA
FEBRUARY 2024

All metrics benchmarked againstFebruary 2023

In February 2024, READING experienced 35 crashes, an increase from 29 crashes in February 2023. Despite this 20.7% rise in total crashes, the number of injuries significantly decreased by 66.7%, from 9 to 3.

35

20.7%was 29

Total Crash Events

0

Persons Killed

3

-66.7%was 9

Persons Injured

4

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 · 2024-02-01 to 2024-02-29 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crashes in READING increased by 20.7% year-over-year, rising from 29 crashes in February 2023 to 35 crashes in February 2024. Conversely, total injuries saw a substantial decrease of 66.7%, falling from 9 to 3.

4

Hit-and-Run Crashes — February 2024

11.4% hit-and-run rate this period vs 0.0% prior. Prior period: 0.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

0

Motorists Killed

Prior: 00.0%

2

Pedestrians Injured

Prior: 0%

1

Motorists Injured

Prior: 9-88.9%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The peak day for crashes remained Friday in both periods, with 10 crashes in February 2024 and 7 in February 2023. The peak crash hour shifted from 6 PM in February 2023 (5 crashes) to 5 PM in February 2024 (6 crashes).

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Fatalities remained at zero in both February 2023 and February 2024. Total injuries decreased significantly by 66.7%, from 9 in February 2023 to 3 in February 2024, with the share of no-injury crashes increasing from 69% to 91.4%.

Outcome by Severity (Crash Events)

Minor Injury1minor injury crashes2.9%
-75.0%prior 4
Possible Injury2possible injury crashes5.7%
-60.0%prior 5
No Injury32no injury crashes91.4%
60.0%prior 20

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · Most severe injury per crash record

Top Contributing Factors

The factor 'Followed too closely' saw a 75% increase in count, rising from 4 crashes in February 2023 to 7 in February 2024, becoming the top contributing factor. Conversely, 'No improper driving' decreased by 33.3% in count, from 6 to 4 crashes, and 'Inattention' decreased by 60% in count, from 5 to 2 crashes.

Officer-Reported Primary Contributing Cause

Followed too closely7 (20%)
Failed to yield right of way5 (14.3%)0.0%prior 5
No improper driving4 (11.4%)-33.3%prior 6
Other improper action4 (11.4%)
Made an improper turn3 (8.6%)
Disregarded traffic signs, signals, road markings2 (5.7%)
Failure to keep in proper lane or running off road2 (5.7%)
Inattention2 (5.7%)-60.0%prior 5
Driving too fast for conditions1 (2.9%)
Distracted1 (2.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in clear weather conditions (Clear/Clear or Clear) increased from 19 in February 2023 to 29 in February 2024. Crashes on dry road surfaces also increased from 24 to 32 year-over-year, while snow-related crashes decreased from 3 to 1.

Weather

Clear/Clear18 (51.4%)
50.0%prior 12
Clear11 (31.4%)
57.1%prior 7
Cloudy/Cloudy3 (8.6%)
Rain/Rain2 (5.7%)
Clear/Other1 (2.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · Weather condition at time of crash

Lighting

Daylight23 (65.7%)
9.5%prior 21
Dark - lighted roadway9 (25.7%)
28.6%prior 7
Dusk2 (5.7%)
Dark - roadway not lighted1 (2.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · Lighting condition field

Road Surface

Dry32 (91.4%)
33.3%prior 24
Wet2 (5.7%)
Snow1 (2.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · Road surface condition field

Vehicles & Demographics

The total number of persons involved in crashes increased from 70 to 92, and vehicles from 56 to 72. Notably, the 16-20 age group saw a 160% increase in involved persons, from 5 to 13, and Honda became the top vehicle make involved, with its count increasing from 7 to 13.

Top Vehicle Makes (72 vehicles)

1
HONDA13 (18.1%)
85.7%prior 7
2
TOYOTA8 (11.1%)
-20.0%prior 10
3
FORD6 (8.3%)
-33.3%prior 9
4
CHEVROLET5 (6.9%)
5
SUBARU4 (5.6%)
6
JEEP3 (4.2%)
7
NISSAN3 (4.2%)
-50.0%prior 6
8
MERCEDES-BENZ3 (4.2%)
9
DODGE3 (4.2%)
10
AUDI2 (2.8%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · Vehicle unit records

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

Sex Distribution (86 persons with recorded sex)

Male49 (57.0%)
28.9%prior 38
Female36 (41.9%)
33.3%prior 27
X / Unspecified1 (1.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · Person-level records linked to crash events

Speed Limit Zones

Crashes in 30 mph zones increased by 50% in count, from 8 to 12, and those in 35 mph zones saw a 600% increase, from 1 to 7. Conversely, crashes in 55 mph zones decreased by 44.4% in count, from 9 to 5. Fatal crash rates remained at zero in all speed zones for both periods.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-02-01 to 2024-02-29 · 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: 2024-02-01 through 2024-02-29
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2024-02-01 through 2024-02-29 (29 days)
  • Geographic scope: READING, MA
  • Total crash records analyzed: 35
  • Total persons involved: 92
  • 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: February 2024." Published June 21, 2026. Reporting period: 2024-02-01 to 2024-02-29. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/reading/february-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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Reading, MA Crash Report — February 2024 | ThatCarHitMe.com