Monthly Traffic Safety Analysis

40 CRASHES IN
READING, MA
MAY 2024

All metrics benchmarked againstMay 2023

Total crashes in May 2024 increased by 8.11%, with 40 crashes compared to 37 crashes in May 2023. A notable positive shift was the absence of fatalities in May 2024, down from one fatality in May 2023, despite a 100% increase in total injuries from 3 to 6.

40

▲ 8.1%was 37

Total Crash Events

0

▼ -100.0%was 1

Persons Killed

6

▲ 100.0%was 3

Persons Injured

2

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 · 2024-05-01 to 2024-05-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crashes in Reading, MA showed a slight upward trend, increasing by 8.11% from 37 crashes in May 2023 to 40 crashes in May 2024. While total injuries doubled from 3 to 6, a significant improvement was observed in fatalities, which decreased from 1 to 0 during the same period.

2

Hit-and-Run Crashes — May 2024

▼ 0.0% vs prior (2)

The number of hit-and-run crashes remained consistent at 2 in both May 2023 and May 2024. However, the hit-and-run rate slightly decreased from 5.4% in May 2023 to 5% in May 2024.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 0→0.0%

6

Motorists Injured

Prior: 3↑100.0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · 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 Tuesday, which had 9 crashes in May 2023, to Wednesday, with 8 crashes in May 2024. The peak hour remained 8a in both periods, although the number of crashes at this hour decreased from 6 in May 2023 to 5 in May 2024.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Fatal crashes decreased from 1 in May 2023 to 0 in May 2024. Total injuries increased by 100%, from 3 persons injured in May 2023 to 6 persons injured in May 2024. The proportion of crashes resulting in no injury slightly decreased from 91.9% in May 2023 to 85% in May 2024.

Outcome by Severity (Crash Events)

Minor Injury1minor injury crashes2.5%
→0.0%prior 1
Possible Injury5possible injury crashes12.5%
↑400.0%prior 1
No Injury34no injury crashes85%
→0.0%prior 34

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Most severe injury per crash record

Top Contributing Factors

The count of crashes attributed to "No improper driving" increased by 42.9% from 7 in May 2023 to 10 in May 2024, becoming the most frequent contributing factor. Conversely, crashes due to "Followed too closely" decreased by 22.2% from 9 to 7, and "Inattention" decreased by 42.9% from 7 to 4. Factors like "Distracted" and "Exceeded authorized speed limit" appeared in May 2024 with 2 and 1 crashes respectively, while not being among the top factors in May 2023.

Officer-Reported Primary Contributing Cause

No improper driving10 (25%)↑42.9%prior 7
Followed too closely7 (17.5%)↓-22.2%prior 9
Inattention4 (10%)↓-42.9%prior 7
Failed to yield right of way3 (7.5%)
Failure to keep in proper lane or running off road3 (7.5%)
Distracted2 (5%)
Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway2 (5%)
Driving too fast for conditions1 (2.5%)
Made an improper turn1 (2.5%)
Exceeded authorized speed limit1 (2.5%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in 'Daylight' conditions decreased from 35 in May 2023 to 32 in May 2024, while those in 'Dark - lighted roadway' conditions increased from 1 to 4. The number of crashes on 'Wet' road surfaces increased from 4 to 6. Crashes during 'Rain/Rain' conditions appeared in May 2024 with 2 occurrences, compared to none in May 2023.

Weather

Clear/Clear19 (47.5%)
→0.0%prior 19
Clear9 (22.5%)
↑12.5%prior 8
Cloudy/Cloudy3 (7.5%)
Clear/Cloudy3 (7.5%)
Cloudy2 (5.0%)
Rain/Rain2 (5.0%)
Cloudy/Rain1 (2.5%)
Rain1 (2.5%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Weather condition at time of crash

Lighting

Daylight32 (80.0%)
↓-8.6%prior 35
Dark - lighted roadway4 (10.0%)
Dark - roadway not lighted2 (5.0%)
Dawn1 (2.5%)
Dusk1 (2.5%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Lighting condition field

Road Surface

Dry34 (85.0%)
↑3.0%prior 33
Wet6 (15.0%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Road surface condition field

Vehicles & Demographics

The total number of vehicles involved in crashes increased from 76 in May 2023 to 80 in May 2024. Among vehicle makes, NISSAN, TOYOTA, CHEVROLET, and HYUNDAI saw increases in crash involvement, while JEEP and VOLKSWAGEN saw decreases. The 45-54 age group experienced a notable increase in crash involvement from 10 persons in May 2023 to 18 persons in May 2024, while the 35-44 age group decreased from 22 to 12.

Top Vehicle Makes (80 vehicles)

1
HONDA10 (12.5%)
→0.0%prior 10
2
NISSAN9 (11.3%)
↑28.6%prior 7
3
TOYOTA8 (10%)
↑14.3%prior 7
4
CHEVROLET8 (10%)
↑60.0%prior 5
5
FORD5 (6.3%)
→0.0%prior 5
6
HYUNDAI5 (6.3%)
7
JEEP4 (5%)
↓-33.3%prior 6
8
SUBARU4 (5%)
9
KIA3 (3.8%)
10
RAM3 (3.8%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Vehicle unit records

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

Sex Distribution (78 persons with recorded sex)

Female44 (56.4%)
↓-10.2%prior 49
Male34 (43.6%)
↓-26.1%prior 46

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Person-level records linked to crash events

Speed Limit Zones

Crashes in the 30 mph speed zone increased from 11 in May 2023 to 15 in May 2024, while those in the 55 mph zone decreased from 8 to 7. Fatal crashes in the 35 mph speed zone, which accounted for 1 fatality in May 2023, were absent in May 2024. The 65 mph zone saw an increase in crashes from 1 to 3.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · 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: 2024-05-01 through 2024-05-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2024-05-01 through 2024-05-31 (31 days)
  • Geographic scope: READING, MA
  • Total crash records analyzed: 40
  • Total persons involved: 86
  • Total vehicles involved: 80

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: May 2024." Published June 21, 2026. Reporting period: 2024-05-01 to 2024-05-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/reading/may-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 — May 2024 | ThatCarHitMe.com