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YEAR-OVER-YEAR CRASH REPORT · AGAWAM, 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/agawam/2024-annual-report
Yearly Traffic Safety Analysis
685 CRASHES IN
AGAWAM, MA
2024
In Agawam, total crashes remained stable year-over-year, with 685 incidents recorded in 2024 compared to 688 in 2023, a decrease of less than one percent. While overall totals were consistent, the number of crashes where speeding was a factor more than doubled, increasing from 15 in the prior period to 35 in the current period. The total number of injuries decreased by 8.8% from 194 to 177, while fatalities held steady at one for each year.
685
▼ -0.4%was 688
Total Crash Events
1
Persons Killed
177
▼ -8.8%was 194
Persons Injured
75
▼ -7.4%was 81
Hit-and-Run Crashes
Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 26 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 crash volume in Agawam saw a negligible decrease, from 688 crashes in 2023 to 685 in 2024. The number of injuries resulting from these crashes declined by 8.8%, from 194 to 177. The number of fatalities remained unchanged, with one fatality recorded in each period.
75
Hit-and-Run Crashes — 2024
▼ -7.4% vs prior (81)
Hit-and-run incidents showed a downward trend in both count and rate. The number of hit-and-run crashes decreased from 81 in 2023 to 75 in 2024. Correspondingly, the hit-and-run rate, representing the percentage of total crashes that were hit-and-runs, fell from 11.8% to 10.9%.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Cyclists Killed
1
Motorists Killed
5
Pedestrians Injured
7
Cyclists Injured
165
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 showed some shifts between the two periods. The peak day for crashes moved from Tuesday (112 crashes) in 2023 to Monday (117 crashes) in 2024. The peak hour for collisions also shifted earlier, moving from the 5 PM hour in the prior period (73 crashes) to the 4 PM hour in the current period (84 crashes).
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
The severity of crashes saw a slight increase in the proportion of injury-related incidents, even as total crashes remained stable. The number of fatal crashes was unchanged at one for both 2023 and 2024. However, the share of crashes resulting in serious injuries rose from 0.6% (4 crashes) to 0.9% (6 crashes), and minor injury crashes increased from 9.3% (64 crashes) of all crashes to 11.4% (78 crashes).
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
Inattention remained the leading contributing factor for crashes in both periods, with its count increasing by 28.1% from 160 incidents in 2023 to 205 in 2024. Crashes attributed to 'Followed too closely' decreased by 31.3% in count, from 83 to 57, causing it to drop from the third to the fourth-ranked factor. The count of crashes with 'No improper driving' also decreased, from 154 to 125.
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
Environmental conditions remained broadly consistent across both years. Crashes on dry road surfaces accounted for the vast majority in both periods, comprising 81.9% of incidents in 2024 compared to 83.1% in 2023. Similarly, the proportion of crashes occurring during daylight hours was stable, at 72.6% in the current period versus 74.0% in the prior period. The distribution of crashes by weather condition also showed no significant year-over-year changes.
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 ranking of the most frequently involved vehicle makes shifted, with Honda taking the top spot in 2024 with 162 vehicles, up from third place (140 vehicles) in 2023. Ford, the previous top make with 170 vehicles, dropped to third place with 137 vehicles involved in crashes. An analysis of persons involved shows an increase in the 16-20 age group, which grew from 180 individuals in the prior year to 204 in the current year.
Top Vehicle Makes (1,267 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Vehicle unit records
167 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (1,429 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
The distribution of crashes across speed zones showed some changes year-over-year. The number of crashes in 35 mph zones decreased from 205 to 171, while crashes in 40 mph zones increased from 104 to 115. In both 2023 and 2024, the single fatal crash recorded during the year occurred in a 40 mph zone.
Fatal crashes by zone: 40 mph: 1 of 115 (0.87%)
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: AGAWAM, MA
- Total crash records analyzed: 685
- Total persons involved: 1,627
- Total vehicles involved: 1,267
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: 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/agawam/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