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YEAR-OVER-YEAR CRASH REPORT · AGAWAM, MA · JULY 2023
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/agawam/july-2023-report
Monthly Traffic Safety Analysis
70 CRASHES IN
AGAWAM, MA
JULY 2023
In July 2023, Agawam experienced 70 total crashes, marking a 6.1% increase from the 66 crashes reported in July 2022. The most significant year-over-year shift was a 122.2% increase in total injuries, rising from 9 to 20. This indicates a notable escalation in the severity of crash outcomes despite a smaller increase in overall crash frequency.
70
▲ 6.1%was 66
Total Crash Events
0
Persons Killed
20
▲ 122.2%was 9
Persons Injured
10
▲ 100.0%was 5
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. 2 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-07-01 to 2023-07-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall crash data for Agawam shows an upward trend year-over-year, with total crashes increasing from 66 in July 2022 to 70 in July 2023. This represents a 6.1% rise in crash incidents. The number of injured persons also significantly increased by 122.2%, from 9 to 20.
10
Hit-and-Run Crashes — July 2023
▲ 100.0% vs prior (5)
Hit-and-run crashes increased significantly year-over-year, doubling from 5 incidents in July 2022 to 10 in July 2023. Consequently, the hit-and-run rate also rose from 7.6% of all crashes in the prior period to 14.3% in the current period, indicating an upward trend.
Vulnerable Road User Casualties
0
Motorists Killed
20
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-07-01 to 2023-07-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal patterns of crashes shifted considerably year-over-year. In July 2022, the peak day for crashes was Sunday with 15 incidents, and the peak hour was 9 p.m. with 8 crashes. By July 2023, the peak day had shifted to Thursday with 15 crashes, and the peak hour moved to 4 p.m. with 13 crashes, indicating a change in when high-frequency crash periods occur.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-07-01 to 2023-07-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-07-01 to 2023-07-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
Fatal crashes remained at zero for both July 2022 and July 2023, indicating no change in this critical metric. However, total injuries saw a substantial increase of 122.2%, rising from 9 injured persons in July 2022 to 20 in July 2023. This includes the appearance of 1 serious injury in July 2023, where none were reported in the prior year.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-07-01 to 2023-07-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-07-01 to 2023-07-31 · Most severe injury per crash record
Top Contributing Factors
Contributing factors saw shifts in both count and ranking between the two periods. 'Followed too closely' crashes increased from 8 to 14, representing a 75% increase in count and moving from third to second highest factor. Conversely, 'Inattention' crashes decreased from 18 to 15 (a 16.7% decrease in count), and 'No improper driving' crashes decreased from 16 to 13 (an 18.8% decrease in count).
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-07-01 to 2023-07-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
There were notable shifts in crash conditions year-over-year. Crashes occurring in daylight conditions increased from 48 to 62, while those in dark-lighted roadway conditions decreased from 11 to 2. Crashes on wet or standing water road surfaces increased from 1 in July 2022 to 9 in July 2023, even as dry surface crashes slightly decreased from 65 to 61.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-07-01 to 2023-07-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-07-01 to 2023-07-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-07-01 to 2023-07-31 · Road surface condition field
Vehicles & Demographics
The total number of vehicles involved in crashes increased from 132 in July 2022 to 137 in July 2023. Honda became the most frequently involved make, increasing from 13 vehicles to 24, surpassing Ford which decreased from 20 to 14 vehicles. In terms of persons involved, the 35-44 age group saw an increase from 22 to 29 individuals, and the 55-64 age group increased from 15 to 23 individuals.
Top Vehicle Makes (137 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-07-01 to 2023-07-31 · Vehicle unit records
18 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (152 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-07-01 to 2023-07-31 · Person-level records linked to crash events
Speed Limit Zones
The distribution of crashes across speed zones saw some changes, though the peak number of crashes remained at the 35 mph zone with 21 incidents in both periods. Crashes in the 25 mph zone increased from 13 to 18, and in the 30 mph zone from 2 to 9. Conversely, crashes in the 40 mph zone decreased from 11 to 8, and in the 55 mph zone from 7 to 4.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-07-01 to 2023-07-31 · 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: 2023-07-01 through 2023-07-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2023-07-01 through 2023-07-31 (31 days)
- Geographic scope: AGAWAM, MA
- Total crash records analyzed: 70
- Total persons involved: 180
- Total vehicles involved: 137
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). "AGAWAM, MA Crash Intelligence Report: July 2023." Published June 21, 2026. Reporting period: 2023-07-01 to 2023-07-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/agawam/july-2023-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: 2023-07-01 – 2023-07-31
Generated: June 21, 2026 · All rights reserved