Monthly Traffic Safety Analysis

103 CRASHES IN
WEST SPRINGFIELD, MA
SEPTEMBER 2023

All metrics benchmarked againstSeptember 2022

Total crashes in West Springfield decreased by 8.85% year-over-year, from 113 crashes in September 2022 to 103 crashes in September 2023. The most notable shift was a significant increase in crashes occurring in wet road conditions, which rose from 12 to 29.

103

-8.8%was 113

Total Crash Events

0

Persons Killed

19

-32.1%was 28

Persons Injured

11

-15.4%was 13

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. 4 crashes with unreported severity are not shown in the severity breakdown.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crash incidents in West Springfield showed a downward trend, with total crashes decreasing by 8.85% from 113 to 103. Total injuries also declined from 28 to 19, while fatalities remained at 0 in both periods.

11

Hit-and-Run Crashes — September 2023

-15.4% vs prior (13)

Hit-and-run crashes decreased from 13 in September 2022 to 11 in September 2023. The hit-and-run rate also saw a slight decrease, moving from 11.5% to 10.7% year-over-year.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

19

Motorists Injured

Prior: 24-20.8%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · 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 in September 2022, which had 22 crashes, to Saturday in September 2023, also with 22 crashes. The peak hour for crashes moved from 6 PM with 11 crashes in the prior period to 4 PM with 10 crashes in the current period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Total injuries decreased from 28 in September 2022 to 19 in September 2023. While the prior period reported 1 serious injury, the current period had no serious injuries, with minor injuries increasing from 9 (8% share) to 12 (11.7% share) and possible injuries decreasing from 9 (8% share) to 2 (1.9% share).

Outcome by Severity (Crash Events)

Minor Injury12minor injury crashes11.7%
33.3%prior 9
Possible Injury2possible injury crashes1.9%
-77.8%prior 9
No Injury85no injury crashes82.5%
-3.4%prior 88

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Most severe injury per crash record

Top Contributing Factors

The count of crashes attributed to "No improper driving" decreased by 4, from 37 to 33, while "Inattention"-related crashes increased by 4, from 16 to 20. Crashes involving "Driving too fast for conditions" saw a notable increase of 5, rising from 3 to 8 year-over-year.

Officer-Reported Primary Contributing Cause

No improper driving33 (32%)-10.8%prior 37
Inattention20 (19.4%)25.0%prior 16
Failed to yield right of way12 (11.7%)-7.7%prior 13
Driving too fast for conditions8 (7.8%)
Followed too closely5 (4.9%)-28.6%prior 7
Failure to keep in proper lane or running off road4 (3.9%)-20.0%prior 5
Disregarded traffic signs, signals, road markings2 (1.9%)
Distracted2 (1.9%)
Emotional1 (1%)
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner1 (1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in clear weather conditions decreased from 92 to 57, while crashes in rain conditions increased from 4 to 17. The number of crashes on wet road surfaces significantly increased from 12 in the prior period to 29 in the current period.

Weather

Clear57 (56.4%)
-38.0%prior 92
Rain17 (16.8%)
Cloudy13 (12.9%)
116.7%prior 6
Cloudy/Rain5 (5.0%)
Clear/Unknown3 (3.0%)
Clear/Cloudy3 (3.0%)
Cloudy/Clear1 (1.0%)
Rain/Cloudy1 (1.0%)
Rain/Other1 (1.0%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Weather condition at time of crash

Lighting

Daylight75 (73.5%)
-5.1%prior 79
Dark - lighted roadway21 (20.6%)
-16.0%prior 25
Dusk3 (2.9%)
Dawn2 (2.0%)
Dark - roadway not lighted1 (1.0%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Lighting condition field

Road Surface

Dry72 (71.3%)
-26.5%prior 98
Wet29 (28.7%)
141.7%prior 12

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Road surface condition field

Vehicles & Demographics

The total number of vehicles involved in crashes decreased from 211 to 196. Toyota remained the top make involved, increasing from 28 to 40 vehicles, while Hyundai involvement decreased from 22 to 16 vehicles. In terms of persons involved, the 26-34 age group saw a decrease from 46 to 36 individuals, and the 0-15 age group decreased from 19 to 7 individuals.

Top Vehicle Makes (196 vehicles)

1
TOYOTA40 (20.4%)
42.9%prior 28
2
HONDA23 (11.7%)
-11.5%prior 26
3
FORD17 (8.7%)
6.3%prior 16
4
HYUNDAI16 (8.2%)
-27.3%prior 22
5
NISSAN12 (6.1%)
-29.4%prior 17
6
CHEVROLET8 (4.1%)
-33.3%prior 12
7
VOLKSWAGEN7 (3.6%)
40.0%prior 5
8
JEEP6 (3.1%)
-53.8%prior 13
9
KIA6 (3.1%)
10
LEXUS6 (3.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Vehicle unit records

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

Sex Distribution (199 persons with recorded sex)

Male112 (56.3%)
-2.6%prior 115
Female87 (43.7%)
-20.2%prior 109

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · Person-level records linked to crash events

Speed Limit Zones

Crashes occurring in 30 mph speed zones increased from 44 to 51, making it the most frequent speed zone for crashes in both periods. Conversely, crashes in 35 mph zones decreased from 12 to 3, and 40 mph zones decreased from 14 to 10. No fatal crashes were reported in any speed zone during either period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-09-01 to 2023-09-30 · 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: 2023-09-01 through 2023-09-30
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2023-09-01 through 2023-09-30 (30 days)
  • Geographic scope: WEST SPRINGFIELD, MA
  • Total crash records analyzed: 103
  • Total persons involved: 234
  • Total vehicles involved: 196

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). "WEST SPRINGFIELD, MA Crash Intelligence Report: September 2023." Published June 21, 2026. Reporting period: 2023-09-01 to 2023-09-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/west-springfield/september-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

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West Springfield, MA Crash Report — September 2023 | ThatCarHitMe.com