Monthly Traffic Safety Analysis

80 CRASHES IN
WEST SPRINGFIELD, MA
JANUARY 2022

All metrics benchmarked againstJanuary 2021

In January 2022, West Springfield experienced 80 crashes, an increase from 68 crashes in January 2021. This represents a 17.65% rise in total crashes year-over-year. The most notable shift was a 200% increase in hit-and-run crashes, which rose from 4 in January 2021 to 12 in January 2022.

80

17.6%was 68

Total Crash Events

0

Persons Killed

14

7.7%was 13

Persons Injured

12

200.0%was 4

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 · 2022-01-01 to 2022-01-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend indicates an increase in crash activity, with total crashes rising from 68 in January 2021 to 80 in January 2022. This constitutes a 17.65% increase in crashes year-over-year. Total injuries also saw a slight increase, from 13 to 14, representing a 7.69% rise.

12

Hit-and-Run Crashes — January 2022

200.0% vs prior (4)

Hit-and-run crashes increased significantly from 4 in January 2021 to 12 in January 2022, marking a 200% increase. Consequently, the hit-and-run rate more than doubled, rising from 5.9% of all crashes in the prior period to 15% in the current period.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

14

Motorists Injured

Prior: 137.7%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-01-31 · 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 Tuesday (16 crashes) in January 2021 to Wednesday (18 crashes) in January 2022. The peak hour also changed, moving from 2 p.m. (8 crashes) in the prior period to 8 a.m. (11 crashes) in the current period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-01-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-01-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Fatalities remained at 0 in both January 2021 and January 2022. While total injuries increased from 13 to 14, the proportion of injury crashes relative to total crashes decreased from 19.1% (13 of 68 crashes) in the prior year to 15% (12 of 80 crashes) in the current year.

Outcome by Severity (Crash Events)

Minor Injury4minor injury crashes5%
-20.0%prior 5
Possible Injury8possible injury crashes10%
0.0%prior 8
No Injury63no injury crashes78.8%
23.5%prior 51

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-01-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-01-31 · Most severe injury per crash record

Top Contributing Factors

The number of crashes attributed to 'No improper driving' increased from 18 to 30, while 'Inattention' decreased from 11 to 9 crashes. Crashes where 'Failed to yield right of way' was a factor increased from 4 to 8, and 'Driving too fast for conditions' decreased from 6 to 2 crashes.

Officer-Reported Primary Contributing Cause

No improper driving30 (37.5%)66.7%prior 18
Inattention9 (11.3%)-18.2%prior 11
Failed to yield right of way8 (10%)
Followed too closely4 (5%)
Failure to keep in proper lane or running off road4 (5%)
Other improper action3 (3.8%)
Glare2 (2.5%)
Visibility obstructed2 (2.5%)
Driving too fast for conditions2 (2.5%)-66.7%prior 6
Disregarded traffic signs, signals, road markings1 (1.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-01-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in 'Clear' weather conditions increased from 38 in January 2021 to 51 in January 2022. There was a notable increase in crashes on 'Ice' road surfaces, rising from 3 in the prior period to 11 in the current period. Crashes occurring during 'Daylight' hours increased from 41 to 54 year-over-year.

Weather

Clear51 (64.6%)
34.2%prior 38
Cloudy8 (10.1%)
-11.1%prior 9
Snow5 (6.3%)
-28.6%prior 7
Sleet, hail (freezing rain or drizzle)2 (2.5%)
Rain2 (2.5%)
Cloudy/Sleet, hail (freezing rain or drizzle)2 (2.5%)
Clear/Other2 (2.5%)
Snow/Blowing sand, snow1 (1.3%)
Clear/Fog, smog, smoke1 (1.3%)
Cloudy/Snow1 (1.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-01-31 · Weather condition at time of crash

Lighting

Daylight54 (67.5%)
31.7%prior 41
Dark - lighted roadway21 (26.3%)
40.0%prior 15
Dawn2 (2.5%)
Dark - roadway not lighted1 (1.3%)
Dusk1 (1.3%)
-80.0%prior 5
Other1 (1.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-01-31 · Lighting condition field

Road Surface

Dry51 (64.6%)
8.5%prior 47
Ice11 (13.9%)
Wet8 (10.1%)
14.3%prior 7
Snow6 (7.6%)
-25.0%prior 8
Sand, mud, dirt, oil, gravel2 (2.5%)
Slush1 (1.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-01-31 · Road surface condition field

Vehicles & Demographics

The total number of persons involved in crashes increased from 143 to 196. The 26-34 age group saw a rise in involvement from 20 to 34 persons, and the 16-20 age group increased from 20 to 27 persons. Toyota remained the top vehicle make involved, increasing from 17 to 23 vehicles, while Honda involvement increased from 12 to 19 vehicles.

Top Vehicle Makes (160 vehicles)

1
TOYOTA23 (14.4%)
35.3%prior 17
2
HONDA19 (11.9%)
58.3%prior 12
3
NISSAN18 (11.3%)
157.1%prior 7
4
FORD18 (11.3%)
20.0%prior 15
5
CHEVROLET10 (6.3%)
11.1%prior 9
6
SUBARU9 (5.6%)
7
HYUNDAI8 (5%)
-46.7%prior 15
8
LEXUS5 (3.1%)
9
RAM4 (2.5%)
10
DODGE4 (2.5%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-01-31 · Vehicle unit records

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

Sex Distribution (171 persons with recorded sex)

Male111 (64.9%)
40.5%prior 79
Female60 (35.1%)
20.0%prior 50

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-01-31 · Person-level records linked to crash events

Speed Limit Zones

Crashes occurring in 30 mph speed zones saw a slight decrease from 33 to 32. Conversely, crashes in 40 mph speed zones increased from 8 to 12, and those in 65 mph speed zones increased from 7 to 9. No fatal crashes were recorded in any speed zone during either period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2022-01-01 to 2022-01-31 · 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: 2022-01-01 through 2022-01-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2022-01-01 through 2022-01-31 (31 days)
  • Geographic scope: WEST SPRINGFIELD, MA
  • Total crash records analyzed: 80
  • Total persons involved: 196
  • Total vehicles involved: 160

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: January 2022." Published June 21, 2026. Reporting period: 2022-01-01 to 2022-01-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/west-springfield/january-2022-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 — January 2022 | ThatCarHitMe.com