ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · READING, MA · 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/2024-annual-report
Yearly Traffic Safety Analysis
470 CRASHES IN
READING, MA
2024
In 2024, Reading recorded 470 total crashes, an 8.6% decrease from the 514 crashes reported in 2023. This year-over-year comparison shows a reduction in total injuries from 112 to 94 and a drop in fatalities from one to zero. The most notable shift was the elimination of fatal crashes in the current period.
470
▼ -8.6%was 514
Total Crash Events
0
▼ -100.0%was 1
Persons Killed
94
▼ -16.1%was 112
Persons Injured
32
▲ 28.0%was 25
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. 5 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall traffic crashes in Reading trended downward year-over-year, with total incidents falling by 8.6% from 514 in 2023 to 470 in 2024. This downward trend extended to personal injuries, which decreased by 16.1% from 112 to 94, and fatalities, which dropped from one to zero.
32
Hit-and-Run Crashes — 2024
▲ 28.0% vs prior (25)
Hit-and-run incidents increased in both count and as a proportion of total crashes. The number of hit-and-run crashes rose from 25 in 2023 to 32 in 2024, a 28% increase in count. Consequently, the hit-and-run rate climbed from 4.9% of all crashes in the prior year to 6.8% in the current year.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Cyclists Killed
0
Motorists Killed
5
Pedestrians Injured
2
Cyclists Injured
87
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal patterns of crashes remained broadly consistent year-over-year. Friday was the peak day for crashes in both 2024 (100 crashes) and 2023 (81 crashes). The peak hour for crashes shifted slightly, moving from 4 p.m. in 2023 (48 crashes) to 5 p.m. in 2024 (50 crashes), remaining within the evening commute window.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
Crash severity decreased in 2024 compared to the prior year. The city recorded zero fatal crashes, down from one in 2023, and serious injury crashes fell from five to just one. While the count of minor injury crashes remained stable at 38, their proportion of total crashes increased slightly from 7.4% to 8.1%. Crashes resulting in no injury made up a larger share of the total, rising from 81.7% in 2023 to 82.8% in 2024.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Most severe injury per crash record
Top Contributing Factors
"Followed too closely" remained the top contributing factor in both periods, with a nearly identical count of 110 crashes in 2024 versus 111 in 2023. A notable shift occurred in other leading factors; crashes attributed to "Inattention" decreased by 47.0% in count (from 83 to 44), while crashes involving "Failed to yield right of way" increased by 47.7% in count (from 44 to 65). This change caused "Failed to yield right of way" to rise to the third-ranked contributing factor in 2024, up from fourth in the prior year.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
The distribution of crashes across different environmental conditions remained largely unchanged year-over-year. In both 2024 and 2023, the vast majority of crashes occurred in daylight (73.0% and 74.9% of total crashes, respectively) and on dry roads (82.8% and 81.9% of total crashes, respectively). The proportion of crashes happening on non-dry road surfaces like wet, snow, or ice was also stable, accounting for 16.8% of crashes in 2024 compared to 17.7% in 2023.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Road surface condition field
Vehicles & Demographics
The top three vehicle makes involved in crashes remained consistent, with Toyota, Honda, and Ford leading in both years, although the total number of vehicles from each make decreased in 2024. When analyzing the age of persons involved, there was a shift in representation for younger drivers. The 16-20 age group constituted 12.3% of all persons involved in 2024, up from a 10.1% share in 2023, while the 26-34 age group's share decreased from 17.2% to 15.7%.
Top Vehicle Makes (929 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Vehicle unit records
66 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (1,075 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Person-level records linked to crash events
Speed Limit Zones
There was a notable shift in the distribution of crashes by speed zone between the two years. Crashes in 55 mph zones decreased from 157 in 2023 to 105 in 2024. Conversely, incidents in 30 mph zones increased from 131 to 154. The single fatal crash recorded in 2023 occurred in a 35 mph zone; no fatal crashes were recorded in any speed zone in 2024.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · 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: 2024-01-01 through 2024-12-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2024-01-01 through 2024-12-31 (366 days)
- Geographic scope: READING, MA
- Total crash records analyzed: 470
- Total persons involved: 1,156
- Total vehicles involved: 929
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: 2024." Published June 21, 2026. Reporting period: 2024-01-01 to 2024-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/reading/2024-annual-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: 2024-01-01 – 2024-12-31
Generated: June 21, 2026 · All rights reserved