ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · READING, MA · AUGUST 2024
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/august-2024-report
Monthly Traffic Safety Analysis
46 CRASHES IN
READING, MA
AUGUST 2024
In August 2024, Reading experienced 46 crashes, a slight decrease from the 48 crashes recorded in August 2023, representing a 4.17% reduction. Despite fewer total crashes, the number of injured persons dramatically increased from 4 to 15, marking a 275% rise year-over-year. This indicates a significant shift towards more injurious outcomes in crashes.
46
▼ -4.2%was 48
Total Crash Events
0
Persons Killed
15
▲ 275.0%was 4
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 · 2024-08-01 to 2024-08-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
The overall trend shows a slight decrease in total crashes, with 46 crashes in August 2024 compared to 48 in August 2023. This represents a 4.17% reduction in the total number of crash events. However, the number of persons injured in these crashes rose substantially.
2
Hit-and-Run Crashes — August 2024
▼ 0.0% vs prior (2)
The number of hit-and-run crashes remained consistent at 2 in both August 2023 and August 2024. The hit-and-run rate saw a marginal increase from 4.2% in the prior period to 4.3% in the current period. This indicates a stable trend in the occurrence of hit-and-run incidents.
Vulnerable Road User Casualties
0
Cyclists Killed
0
Motorists Killed
1
Cyclists Injured
14
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-08-01 to 2024-08-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The peak day for crashes remained Friday in both periods, though the count decreased from 13 crashes in August 2023 to 11 in August 2024. The peak hour for crashes also remained 12 PM, increasing from 7 crashes in the prior period to 9 crashes in the current period. There was a notable decrease in crashes on Tuesdays, falling from 8 to 3, while crashes on Sundays increased from 6 to 7.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-08-01 to 2024-08-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-08-01 to 2024-08-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
There were no fatalities in either August 2023 or August 2024. However, the number of persons injured surged from 4 in August 2023 to 15 in August 2024, a 275% increase. Crashes resulting in minor injuries (B) increased from 1 to 5, and crashes with possible injuries (C) rose from 3 to 5, indicating a higher proportion of injury-involved crashes in the current period.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-08-01 to 2024-08-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-08-01 to 2024-08-31 · Most severe injury per crash record
Top Contributing Factors
The top contributing factor, 'Followed too closely,' saw an increase from 12 crashes in August 2023 to 15 crashes in August 2024, a 25% change in count. 'Inattention' crashes significantly decreased from 9 to 2, a 77.8% reduction in count, causing its ranking to drop. Conversely, 'Failed to yield right of way' crashes increased from 3 to 5, a 66.7% change in count, moving up in ranking.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-08-01 to 2024-08-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes occurring in dry road conditions increased slightly from 42 in August 2023 to 44 in August 2024, while wet road crashes decreased from 6 to 1. Crashes during daylight hours saw a decrease from 43 to 39, whereas crashes in 'Dark - lighted roadway' conditions increased from 3 to 5. The overall proportion of crashes in clear weather conditions remained dominant in both periods.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-08-01 to 2024-08-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-08-01 to 2024-08-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-08-01 to 2024-08-31 · Road surface condition field
Vehicles & Demographics
The total number of vehicles involved remained stable, with 93 in August 2024 compared to 94 in August 2023. Toyota remained the top vehicle make involved, with its count increasing from 15 to 18. There was a notable increase in persons aged 0-15 involved in crashes, rising from 4 to 14, and those aged 35-44, increasing from 12 to 18.
Top Vehicle Makes (93 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-08-01 to 2024-08-31 · Vehicle unit records
5 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (123 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-08-01 to 2024-08-31 · Person-level records linked to crash events
Speed Limit Zones
Crashes in 55 mph speed zones saw a significant decrease from 19 in August 2023 to 10 in August 2024, a 47.4% reduction in count. Conversely, crashes in 25 mph zones increased from 1 to 6, and 40 mph zones increased from 4 to 8. No fatal crashes were recorded in any speed zone during either period.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-08-01 to 2024-08-31 · 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: 2024-08-01 through 2024-08-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2024-08-01 through 2024-08-31 (31 days)
- Geographic scope: READING, MA
- Total crash records analyzed: 46
- Total persons involved: 128
- Total vehicles involved: 93
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: August 2024." Published June 21, 2026. Reporting period: 2024-08-01 to 2024-08-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/reading/august-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
ThatCarHitMe.com · An Injuria.ai Company
ThatCarHitMe.com
An Injuria.ai Company
Crash Data Intelligence
Data: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly
Period: 2024-08-01 – 2024-08-31
Generated: June 21, 2026 · All rights reserved