ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · SPRINGFIELD, MA · 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.
GET: https://thatcarhitme.com/api/crash-data/reports/data/massachusetts/springfield/2023-annual-report
Yearly Traffic Safety Analysis
4,217 CRASHES IN
SPRINGFIELD, MA
2023
In 2023, Springfield recorded 4,217 total crashes, a 2.9% decrease from the 4,342 crashes in 2022. While total crashes and injuries declined, the most notable year-over-year shift was a 66.7% increase in fatalities, which rose from 12 to 20.
4,217
▼ -2.9%was 4,342
Total Crash Events
20
▲ 66.7%was 12
Persons Killed
2,224
▼ -6.1%was 2,368
Persons Injured
449
▼ -8.6%was 491
Hit-and-Run Crashes
Note: "Persons Killed" (20) counts individual fatalities across all crash events. "Fatal" in the severity table below (19) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 186 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Overall crash trends in Springfield show a slight decline year-over-year. Total crashes decreased by 2.9% from 4,342 in 2022 to 4,217 in 2023, and the number of people injured fell by 6.1% from 2,368 to 2,224. However, this downward trend in crashes did not extend to fatalities, which saw a significant increase.
449
Hit-and-Run Crashes — 2023
▼ -8.6% vs prior (491)
Hit-and-run incidents showed a downward trend from 2022 to 2023. The total count of hit-and-run crashes decreased by 8.6%, from 491 to 449. The hit-and-run rate, as a percentage of all crashes, also declined from 11.3% in the prior year to 10.6% in the current year.
Vulnerable Road User Casualties
7
Pedestrians Killed
0
Cyclists Killed
13
Motorists Killed
71
Pedestrians Injured
37
Cyclists Injured
2,116
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The daily pattern of crashes shifted between the two periods, with the peak day for collisions moving from Wednesday (688 crashes) in 2022 to Friday (694 crashes) in 2023. The peak hour for crashes remained consistent at 4 p.m. in both years. However, the number of crashes during this peak hour decreased from 407 in 2022 to 340 in 2023.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
While total crashes decreased, the severity of outcomes worsened in 2023. The number of fatal crashes increased from 12 to 19, and the fatal crash rate rose from 0.28% to 0.45%. Conversely, the count of serious injury crashes fell from 93 to 52. The overall proportion of crashes involving any injury decreased slightly from 35.1% in 2022 to 34.1% in 2023.
Severity is per crash event (most severe injury). 19 fatal crash events resulted in 20 persons killed.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Most severe injury per crash record
Top Contributing Factors
The top contributing factors remained consistent, with "Inattention" and "Failed to yield right of way" leading in both years. The count of crashes attributed to inattention decreased by 12.3% from 1,126 in 2022 to 988 in 2023. In contrast, crashes due to "Failed to yield right of way" saw a 9.5% increase in count, rising from 664 to 727.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
The distribution of crashes across different environmental conditions saw a minor shift. The proportion of crashes occurring on wet road surfaces increased from 16.4% of total crashes in 2022 to 19.9% in 2023. Similarly, crashes during rain grew from a 6.5% share to a 9.6% share. Crashes in daylight and on dry roads remained the most common scenario in both years but constituted a slightly smaller percentage of the total in 2023.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Road surface condition field
Vehicles & Demographics
The top five vehicle makes involved in crashes—Honda, Toyota, Nissan, Ford, and Hyundai—were identical in both 2022 and 2023, with their rankings remaining stable. An analysis of persons involved shows the age distribution was largely consistent year-over-year. However, the share of persons aged 65 and older increased from 6.4% of all individuals involved in 2022 to 7.4% in 2023.
Top Vehicle Makes (8,058 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Vehicle unit records
1,179 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (9,671 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Person-level records linked to crash events
Speed Limit Zones
The majority of crashes in both periods occurred in speed zones between 25 and 35 mph. A significant year-over-year change was observed in fatal crashes within the 30 mph speed zone, where fatalities increased from 2 in 2022 to 11 in 2023. Conversely, fatalities in 25 mph zones decreased from 4 to 1 over the same period.
Fatal crashes by zone: 25 mph: 1 of 1,193 (0.084%) · 30 mph: 11 of 1,473 (0.747%) · 35 mph: 6 of 964 (0.622%) · 55 mph: 1 of 241 (0.415%)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 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: 2023-01-01 through 2023-12-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2023-01-01 through 2023-12-31 (365 days)
- Geographic scope: SPRINGFIELD, MA
- Total crash records analyzed: 4,217
- Total persons involved: 10,947
- Total vehicles involved: 8,058
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). "SPRINGFIELD, MA Crash Intelligence Report: 2023." Published June 21, 2026. Reporting period: 2023-01-01 to 2023-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/springfield/2023-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: 2023-01-01 – 2023-12-31
Generated: June 21, 2026 · All rights reserved