ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · WEST SPRINGFIELD, MA · DECEMBER 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.
Monthly Traffic Safety Analysis
93 CRASHES IN
WEST SPRINGFIELD, MA
DECEMBER 2023
In December 2023, West Springfield experienced 93 total crashes, which remained stable compared to the 93 crashes recorded in December 2022. A significant positive shift was observed in fatalities, which decreased by 100% from 1 fatality in the prior year to 0 fatalities in the current period. Total injuries also saw a slight decrease, moving from 27 to 26 year-over-year.
93
Total Crash Events
0
▼ -100.0%was 1
Persons Killed
26
▼ -3.7%was 27
Persons Injured
15
▲ 25.0%was 12
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 · 2023-12-01 to 2023-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
The overall trend for total crashes in West Springfield remained stable year-over-year, with 93 crashes reported in both December 2023 and December 2022. While total injuries saw a minor decrease from 27 to 26, the most notable positive trend was the 100% reduction in total fatalities, from 1 in December 2022 to 0 in December 2023.
15
Hit-and-Run Crashes — December 2023
▲ 25.0% vs prior (12)
Hit-and-run crashes increased by 25% year-over-year, rising from 12 in December 2022 to 15 in December 2023. Concurrently, the hit-and-run rate increased from 12.9% to 16.1%, indicating an upward trend in the proportion of crashes involving a hit-and-run incident.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Motorists Killed
2
Pedestrians Injured
24
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 to 2023-12-31 · 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 Friday in December 2022, which had 19 crashes, to Saturday in December 2023, with 17 crashes. The peak hour remained 5 PM for both periods, however, crashes at 5 PM increased from 10 in December 2022 to 13 in December 2023. Crashes on Sundays increased from 7 to 16, while crashes on Mondays decreased from 14 to 8.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 to 2023-12-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 to 2023-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
Fatal crashes decreased from 1 in December 2022 to 0 in December 2023, representing a 100% reduction. Serious injuries (Severity A) decreased by 60% from 5 to 2, and possible injuries (Severity C) decreased by 66.7% from 6 to 2. Conversely, minor injuries (Severity B) increased by 44.4% from 9 to 13, and crashes with no injuries increased by 4.2% from 71 to 74.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 to 2023-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 to 2023-12-31 · Most severe injury per crash record
Top Contributing Factors
The contributing factor 'No improper driving' increased in count from 32 to 36 crashes, a 12.5% rise. 'Inattention' decreased significantly in count from 17 to 9 crashes, a 47.1% reduction. 'Driving too fast for conditions' saw a 66.7% increase in count, from 3 to 5 crashes, while 'Followed too closely' decreased by 60% in count, from 5 to 2 crashes. 'Operating vehicle in erratic, reckless, careless, negligent or aggressive manner' emerged with 5 crashes in the current period, whereas it was not among the top factors in the prior period.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 to 2023-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes occurring in 'Clear' weather conditions decreased from 61 to 57, while those in 'Cloudy' conditions increased from 6 to 16. Crashes during 'Daylight' decreased from 50 to 40, whereas crashes in 'Dark - lighted roadway' conditions increased from 35 to 41. Regarding road surface, crashes on 'Wet' roads increased from 22 to 27, and crashes on 'Dry' roads slightly decreased from 68 to 66. Conditions involving snow or ice, which accounted for 3 crashes in the prior period, were not reported in the current period.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 to 2023-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 to 2023-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 to 2023-12-31 · Road surface condition field
Vehicles & Demographics
The total number of vehicles involved remained constant at 171 for both periods. Honda increased its representation from 20 to 27 vehicles, becoming the most common make in the current period, while Toyota slightly decreased from 20 to 19. Significant shifts were observed in age group representation, with the 16-20 age group increasing from 16 to 26 persons and the 26-34 age group increasing from 26 to 41 persons. Conversely, the 45-54 age group decreased from 35 to 19 persons, and the 65+ age group decreased from 23 to 17 persons.
Top Vehicle Makes (171 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 to 2023-12-31 · Vehicle unit records
25 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (178 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 to 2023-12-31 · Person-level records linked to crash events
Speed Limit Zones
Crashes in the 30 mph speed zone slightly decreased from 40 to 38, while those in the 40 mph zone increased from 15 to 20. Crashes in the 65 mph zone also saw a minor increase from 6 to 7. Notably, there was 1 fatal crash in the 35 mph zone in December 2022, but no fatalities were recorded across any speed zone in December 2023.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 to 2023-12-31 · 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: 2023-12-01 through 2023-12-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2023-12-01 through 2023-12-31 (31 days)
- Geographic scope: WEST SPRINGFIELD, MA
- Total crash records analyzed: 93
- Total persons involved: 207
- Total vehicles involved: 171
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: December 2023." Published June 21, 2026. Reporting period: 2023-12-01 to 2023-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/west-springfield/december-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: 2023-12-01 – 2023-12-31
Generated: June 21, 2026 · All rights reserved