Yearly Traffic Safety Analysis

2,551 CRASHES IN
SPRINGFIELD, MA
2024

All metrics benchmarked against2023

In Springfield, total traffic crashes decreased by 39.5% from 4,217 incidents in 2023 to 2,551 in 2024. This downward trend was accompanied by a 35% reduction in fatalities and a 47.4% drop in injuries. Despite the overall improvement in safety metrics, the most notable year-over-year shift was a significant increase in the hit-and-run rate, which rose from 10.6% to 19.4% of all crashes.

2,551

▼ -39.5%was 4,217

Total Crash Events

13

▼ -35.0%was 20

Persons Killed

1,170

▼ -47.4%was 2,224

Persons Injured

494

▲ 10.0%was 449

Hit-and-Run Crashes

Note: "Persons Killed" (13) counts individual fatalities across all crash events. "Fatal" in the severity table below (13) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 168 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 safety data for Springfield shows a significant downward trend year-over-year. Total crashes fell by 1,666 incidents, from 4,217 in 2023 to 2,551 in 2024, a 39.5% decrease. Similarly, the number of persons injured dropped from 2,224 to 1,170, and fatalities decreased from 20 to 13.

494

Hit-and-Run Crashes — 2024

▲ 10.0% vs prior (449)

While total crashes declined, hit-and-run incidents showed an opposing trend. The absolute number of hit-and-run crashes increased by 10%, from 449 in 2023 to 494 in 2024. As a result, the hit-and-run rate, representing the proportion of all crashes that were hit-and-runs, rose sharply from 10.6% to 19.4% year-over-year.

Vulnerable Road User Casualties

4

Pedestrians Killed

Prior: 7↓-42.9%

0

Cyclists Killed

Prior: 0→0.0%

8

Motorists Killed

Prior: 13↓-38.5%

1

Other Killed

Prior: 0→%

49

Pedestrians Injured

Prior: 71↓-31.0%

32

Cyclists Injured

Prior: 37↓-13.5%

1,082

Motorists Injured

Prior: 2,116↓-48.9%

7

Other Injured

Prior: 0→%

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 temporal patterns of crashes remained consistent between the two periods, despite a large reduction in overall volume. In both 2023 and 2024, Friday was the peak day for crashes (694 and 433, respectively) and the 4 PM hour was the peak time (340 and 218, respectively). The distribution of crashes throughout the week and day did not shift significantly, indicating that the underlying timing of traffic risk was unchanged.

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

While the absolute number of fatal crashes fell from 19 to 13, the fatal crash rate per 100 crashes saw a slight increase from 0.45% in 2023 to 0.51% in 2024. The proportion of crashes resulting in serious injuries also grew, rising from 1.2% to 1.8% of all incidents. Conversely, the share of crashes involving minor injuries decreased from 21% to 19% year-over-year.

Outcome by Severity (Crash Events)

Fatal13fatal crashes0.5%
↓-31.6%prior 19
Serious Injury47serious injury crashes1.8%
↓-9.6%prior 52
Minor Injury484minor injury crashes19%
↓-45.3%prior 885
Possible Injury270possible injury crashes10.6%
↓-46.2%prior 502
No Injury1,569no injury crashes61.5%
↓-39.0%prior 2,573

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 'Inattention' and 'Failed to yield right of way' being the top two in both years. The count of crashes attributed to 'Inattention' decreased by 45.2%, from 988 to 541 incidents. Crashes involving 'Failed to yield right of way' saw a 49.5% reduction in count, falling from 727 to 367. Despite these large decreases in count, their share of total crashes remained high.

Officer-Reported Primary Contributing Cause

Inattention541 (21.2%)↓-45.2%prior 988
No improper driving440 (17.2%)↓-22.1%prior 565
Failed to yield right of way367 (14.4%)↓-49.5%prior 727
Followed too closely166 (6.5%)↓-27.8%prior 230
Failure to keep in proper lane or running off road158 (6.2%)↓-41.0%prior 268
Driving too fast for conditions120 (4.7%)↓-17.2%prior 145
Disregarded traffic signs, signals, road markings106 (4.2%)↓-48.3%prior 205
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner67 (2.6%)↓-27.2%prior 92
Other improper action63 (2.5%)↓-52.6%prior 133
Exceeded authorized speed limit57 (2.2%)↓-30.5%prior 82

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

A comparison of crash conditions reveals broad consistency between the two years. The majority of crashes in both 2024 and 2023 occurred during daylight hours (62.4% and 64.8% of crashes, respectively). Similarly, most incidents took place on dry road surfaces in both periods (76.7% in 2024 vs. 77.9% in 2023). There were no significant shifts in the proportions of crashes occurring under adverse lighting, weather, or road surface conditions.

Weather

Clear1,763 (69.5%)
↓-41.5%prior 3,014
Cloudy209 (8.2%)
↓-41.5%prior 357
Rain206 (8.1%)
↓-48.9%prior 403
Clear/Clear73 (2.9%)
Snow70 (2.8%)
↑84.2%prior 38
Cloudy/Rain64 (2.5%)
↓-61.0%prior 164
Clear/Cloudy32 (1.3%)
↓-27.3%prior 44
Clear/Other17 (0.7%)
↓-34.6%prior 26
Rain/Cloudy15 (0.6%)
↓-55.9%prior 34
Clear/Unknown12 (0.5%)
↓-42.9%prior 21

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Weather condition at time of crash

Lighting

Daylight1,591 (62.9%)
↓-41.8%prior 2,733
Dark - lighted roadway770 (30.5%)
↓-36.1%prior 1,205
Dusk80 (3.2%)
↓-36.0%prior 125
Dawn40 (1.6%)
↓-50.6%prior 81
Dark - roadway not lighted39 (1.5%)
↓-7.1%prior 42
Dark - unknown roadway lighting8 (0.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Lighting condition field

Road Surface

Dry1,956 (77.2%)
↓-40.5%prior 3,289
Wet440 (17.4%)
↓-47.6%prior 840
Snow91 (3.6%)
↑89.6%prior 48
Ice35 (1.4%)
↑118.8%prior 16
Slush9 (0.4%)
↓-10.0%prior 10
Sand, mud, dirt, oil, gravel1 (0.0%)
Water (standing, moving)1 (0.0%)

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 were identical in both periods: Honda, Toyota, and Nissan, though the total count for each make declined in line with the overall trend. The demographic profile of persons involved in crashes also remained stable. The 26-34 age group was the largest cohort in both 2024 and 2023, representing 17.2% and 17.5% of all individuals, respectively.

Top Vehicle Makes (4,815 vehicles)

1
HONDA771 (16%)
↓-36.3%prior 1,211
2
TOYOTA654 (13.6%)
↓-39.4%prior 1,080
3
NISSAN390 (8.1%)
↓-46.9%prior 734
4
FORD348 (7.2%)
↓-49.0%prior 683
5
HYUNDAI335 (7%)
↓-42.4%prior 582
6
CHEVROLET280 (5.8%)
↓-45.6%prior 515
7
ACURA139 (2.9%)
↓-35.9%prior 217
8
JEEP126 (2.6%)
↓-60.0%prior 315
9
KIA126 (2.6%)
↓-30.8%prior 182
10
SUBARU111 (2.3%)
↓-46.6%prior 208

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Vehicle unit records

1,023 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (5,359 persons with recorded sex)

Male2,909 (54.3%)
↓-42.8%prior 5,086
Female2,450 (45.7%)
↓-46.6%prior 4,585

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

Crashes in both periods were most prevalent in zones with posted speed limits of 25, 30, and 35 mph. A notable shift occurred as the 25 mph zone became the most common crash location in 2024 with 769 incidents, overtaking the 30 mph zone, which was highest in 2023 with 1,473 incidents. The fatality rate within the 35 mph zone increased from 0.622% in 2023 to 0.765% in 2024.

Fatal crashes by zone: 20 mph: 1 of 59 (1.695%) · 25 mph: 1 of 769 (0.13%) · 30 mph: 4 of 737 (0.543%) · 35 mph: 4 of 523 (0.765%) · 50 mph: 1 of 39 (2.564%) · 55 mph: 1 of 185 (0.541%)

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: SPRINGFIELD, MA
  • Total crash records analyzed: 2,551
  • Total persons involved: 6,439
  • Total vehicles involved: 4,815

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: 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/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

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Springfield, MA Crash Report — 2024 | ThatCarHitMe.com