ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · WEST SPRINGFIELD, 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.
Yearly Traffic Safety Analysis
1,280 CRASHES IN
WEST SPRINGFIELD, MA
2024
Total crashes in West Springfield rose from 1,004 to 1,280, a 27.5% year-over-year increase. This surge was accompanied by a doubling of fatalities from 1 to 2 and a 24.2% rise in total injuries. A notable aspect of this trend was the 80% increase in crashes resulting in serious injuries, which grew from 10 to 18 incidents.
1,280
▲ 27.5%was 1,004
Total Crash Events
2
▲ 100.0%was 1
Persons Killed
364
▲ 24.2%was 293
Persons Injured
158
▲ 10.5%was 143
Hit-and-Run Crashes
Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 25 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
Traffic collisions in West Springfield showed a significant upward trend year-over-year. Total reported crashes increased by 27.5%, rising from 1,004 to 1,280. This trend extended to crash outcomes, with total injuries increasing by 24.2% from 293 to 364, and total fatalities doubling from one to two.
158
Hit-and-Run Crashes — 2024
▲ 10.5% vs prior (143)
The absolute number of hit-and-run incidents increased by 10.5%, from 143 in the prior year to 158 in the current year. However, due to the larger overall increase in total crashes, the hit-and-run rate as a percentage of all crashes trended downward. The rate fell from 14.2% in the prior period to 12.3% in the current period.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Cyclists Killed
2
Motorists Killed
0
Other Killed
12
Pedestrians Injured
9
Cyclists Injured
342
Motorists Injured
1
Other 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 primary temporal patterns for crashes remained consistent between the two periods, with Friday serving as the peak day and 4 p.m. as the peak hour in both years. However, the volume of collisions during these peak times increased substantially. Crashes on Fridays grew from 170 to 218, and collisions during the 4 p.m. hour increased from 94 to 127.
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
Crash severity worsened compared to the previous year. The number of fatal crashes doubled from one to two, and the corresponding fatal crash rate increased from 0.1% to 0.16%. Crashes resulting in a serious injury saw a substantial 80% increase, rising from 10 to 18 incidents. The overall proportion of crashes involving any type of injury grew from 19.1% to 20.8% of all collisions.
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
The leading contributing factors remained consistent, with "No improper driving," "Inattention," and "Failed to yield right of way" as the top three in both periods. While the count of crashes attributed to "Inattention" decreased from 136 to 124 (an 8.8% drop), other factors saw notable increases in count. Incidents involving "Followed too closely" rose from 52 to 74 (a 42.3% increase), and those involving "Failure to keep in proper lane" increased from 27 to 48 (a 77.8% increase).
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
In both years, the majority of crashes occurred in favorable conditions: during daylight, on dry roads, and in clear weather. The proportion of crashes under these ideal conditions was slightly higher in the current year. For example, crashes on dry roads accounted for 76.9% of the total in the prior period and rose to 80.8% in the current period, even as total crashes increased by 27.5%.
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 top three vehicle makes involved in crashes—Toyota, Honda, and Ford—remained the same across both periods. An analysis of persons involved shows a disproportionate increase in collisions involving younger adults. Crash involvement for individuals aged 21-25 and 26-34 grew by 39.4% and 38.7% respectively, outpacing the 27.5% overall increase in crashes.
Top Vehicle Makes (2,390 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Vehicle unit records
287 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (2,641 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 majority of crashes in both periods occurred in 30 mph zones, which accounted for 45.6% of crashes in the current year and 48.8% in the prior year. A notable shift occurred in the location of fatal crashes, which moved from a 65 mph zone in the prior year to lower-speed 25 mph and 30 mph zones in the current year. The share of crashes in 65 mph zones decreased from 8.3% to 4.6% of all incidents where speed limits were recorded.
Fatal crashes by zone: 25 mph: 1 of 89 (1.124%) · 30 mph: 1 of 554 (0.181%)
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: WEST SPRINGFIELD, MA
- Total crash records analyzed: 1,280
- Total persons involved: 2,949
- Total vehicles involved: 2,390
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: 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/west-springfield/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