Monthly Traffic Safety Analysis

46 CRASHES IN
AGAWAM, MA
JULY 2024

All metrics benchmarked againstJuly 2023

Total crashes in Agawam decreased by 34.3% from 70 in July 2023 to 46 in July 2024. Concurrently, total injuries saw a significant reduction of 55%, falling from 20 to 9 over the same period. The most notable shift was the decrease in 'Followed too closely' as a contributing factor, which dropped from 14 crashes in the prior period to 4 in the current period.

46

-34.3%was 70

Total Crash Events

0

Persons Killed

9

-55.0%was 20

Persons Injured

5

-50.0%was 10

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 · 2024-07-01 to 2024-07-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend indicates a decrease in crash activity in Agawam, with total crashes falling by 24 incidents (34.3%) year-over-year. Total injuries also saw a substantial decline, decreasing by 11 individuals (55%) from 20 to 9. Fatalities remained at zero in both July 2023 and July 2024.

5

Hit-and-Run Crashes — July 2024

-50.0% vs prior (10)

Hit-and-run crashes decreased by 5 incidents, falling from 10 in July 2023 to 5 in July 2024. Correspondingly, the hit-and-run rate declined by 3.4 percentage points, from 14.3% in the prior period to 10.9% in the current period, indicating a downward trend.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

9

Motorists Injured

Prior: 20-55.0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-07-01 to 2024-07-31 · 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 Thursday with 15 crashes in July 2023 to Tuesday and Saturday, each with 11 crashes, in July 2024. The peak hour for crashes remained consistently at 4 PM in both periods, although the number of crashes at this hour decreased from 13 in the prior year to 12 in the current year.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-07-01 to 2024-07-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-07-01 to 2024-07-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

There were no fatal crashes in either period, maintaining a fatal crash rate of 0. Serious injuries decreased from 1 in July 2023 to 0 in July 2024. Minor injuries decreased from 6 to 5, and possible injuries saw a notable reduction from 7 to 1, leading to an increase in the proportion of 'No Injury' crashes from 77.1% to 82.6%.

Outcome by Severity (Crash Events)

Minor Injury5minor injury crashes10.9%
-16.7%prior 6
Possible Injury1possible injury crashes2.2%
-85.7%prior 7
No Injury38no injury crashes82.6%
-29.6%prior 54

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-07-01 to 2024-07-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-07-01 to 2024-07-31 · Most severe injury per crash record

Top Contributing Factors

The contributing factor 'Followed too closely' saw the largest decrease, dropping from 14 crashes in the prior period to 4 in the current period. 'Inattention' increased slightly from 15 to 18 crashes, becoming the top contributing factor in July 2024. 'No improper driving' decreased from 13 crashes to 8, and 'Failed to yield right of way' decreased from 8 crashes to 2.

Officer-Reported Primary Contributing Cause

Inattention18 (39.1%)20.0%prior 15
No improper driving8 (17.4%)-38.5%prior 13
Followed too closely4 (8.7%)-71.4%prior 14
Failed to yield right of way2 (4.3%)-75.0%prior 8
Made an improper turn2 (4.3%)
Fatigued/asleep2 (4.3%)
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner1 (2.2%)
Physical impairment1 (2.2%)
Visibility obstructed1 (2.2%)
Wrong side or wrong way1 (2.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-07-01 to 2024-07-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The number of crashes occurring in clear weather conditions decreased from 48 to 33, while those in daylight conditions decreased from 62 to 38. Crashes on dry road surfaces also decreased from 61 to 41. Despite these numerical decreases, the proportion of crashes in clear weather increased slightly from 68.6% to 71.7%, and on dry roads from 87.1% to 89.1%, indicating a higher concentration of crashes in these conditions relative to the overall decrease in crashes.

Weather

Clear33 (71.7%)
-31.3%prior 48
Cloudy4 (8.7%)
-42.9%prior 7
Rain3 (6.5%)
Clear/Other2 (4.3%)
Cloudy/Other2 (4.3%)
Cloudy/Rain2 (4.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-07-01 to 2024-07-31 · Weather condition at time of crash

Lighting

Daylight38 (82.6%)
-38.7%prior 62
Dark - lighted roadway7 (15.2%)
Dusk1 (2.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-07-01 to 2024-07-31 · Lighting condition field

Road Surface

Dry41 (89.1%)
-32.8%prior 61
Wet5 (10.9%)
-28.6%prior 7

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-07-01 to 2024-07-31 · Road surface condition field

Vehicles & Demographics

The total number of vehicles involved in crashes decreased from 137 in July 2023 to 82 in July 2024. All age groups except 16-20 saw a decrease in persons involved in crashes, with the 55-64 age group experiencing the largest numerical drop from 23 to 8 persons. Honda remained the top vehicle make involved in crashes, though its count decreased from 24 to 15, while Toyota saw a significant drop from 18 to 7 vehicles.

Top Vehicle Makes (82 vehicles)

1
HONDA15 (18.3%)
-37.5%prior 24
2
FORD10 (12.2%)
-28.6%prior 14
3
CHEVROLET9 (11%)
28.6%prior 7
4
TOYOTA7 (8.5%)
-61.1%prior 18
5
VOLKSWAGEN4 (4.9%)
6
JEEP4 (4.9%)
7
HYUNDAI3 (3.7%)
-72.7%prior 11
8
DODGE3 (3.7%)
9
MAZDA3 (3.7%)
10
AUDI3 (3.7%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-07-01 to 2024-07-31 · Vehicle unit records

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

Sex Distribution (92 persons with recorded sex)

Male54 (58.7%)
-34.9%prior 83
Female38 (41.3%)
-44.9%prior 69

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-07-01 to 2024-07-31 · Person-level records linked to crash events

Speed Limit Zones

The highest number of crashes continued to occur in the 35 mph speed zone, decreasing from 21 crashes in July 2023 to 14 crashes in July 2024. Crashes in 25 mph zones decreased by 9 incidents (from 18 to 9), and 30 mph zones decreased by 5 incidents (from 9 to 4). There were no fatal crashes recorded in any speed zone during either period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-07-01 to 2024-07-31 · 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: 2024-07-01 through 2024-07-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2024-07-01 through 2024-07-31 (31 days)
  • Geographic scope: AGAWAM, MA
  • Total crash records analyzed: 46
  • Total persons involved: 106
  • Total vehicles involved: 82

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 2024." Published June 21, 2026. Reporting period: 2024-07-01 to 2024-07-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/agawam/july-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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Agawam, MA Crash Report — July 2024 | ThatCarHitMe.com