Monthly Traffic Safety Analysis

176 CRASHES IN
SPRINGFIELD, MA
MAY 2024

All metrics benchmarked againstMay 2023

Total crashes in Springfield, MA decreased significantly from 367 in May 2023 to 176 in May 2024, representing a 52.04% reduction. Despite this overall decrease, fatalities increased from 0 in May 2023 to 2 in May 2024, marking a critical shift in crash outcomes. This increase in fatalities is the most notable year-over-year change.

176

▼ -52.0%was 367

Total Crash Events

2

Persons Killed

80

▼ -59.8%was 199

Persons Injured

40

▲ 11.1%was 36

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. 11 crashes with unreported severity are not shown in the severity breakdown.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crash incidents in Springfield, MA saw a substantial decline year-over-year, with total crashes falling by 52.04% from 367 to 176. Similarly, total injuries decreased by 59.79%, from 199 to 80. However, total fatalities increased from 0 to 2 during the same period, indicating a negative trend in crash severity.

40

Hit-and-Run Crashes — May 2024

▲ 11.1% vs prior (36)

Hit-and-run crashes increased from 36 in May 2023 to 40 in May 2024. The hit-and-run rate more than doubled, rising from 9.8% of total crashes in May 2023 to 22.7% in May 2024. This indicates an upward trend in the proportion of crashes classified as hit-and-run.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 0→%

3

Pedestrians Injured

Prior: 6↓-50.0%

1

Cyclists Injured

Prior: 5↓-80.0%

76

Motorists Injured

Prior: 188↓-59.6%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal distribution of crashes shifted year-over-year, with the peak day changing from Monday (68 crashes) in May 2023 to Thursday (45 crashes) in May 2024. The peak hour also changed, moving from 4 PM (39 crashes) in May 2023 to 8 AM (16 crashes) in May 2024. These changes suggest a different daily and hourly pattern for crash occurrences compared to the prior year.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Fatal crashes increased from 0 in May 2023 to 2 in May 2024, with the fatal crash rate rising from 0% to 1.14%. While serious injuries remained at 1 in both periods, their proportion of total crashes increased from 0.3% to 0.6%. Minor and possible injury crashes both saw substantial decreases in count, falling from 83 to 33 and 46 to 20 respectively.

Outcome by Severity (Crash Events)

Fatal2fatal crashes1.1%
Serious Injury1serious injury crashes0.6%
→0.0%prior 1
Minor Injury33minor injury crashes18.8%
↓-60.2%prior 83
Possible Injury20possible injury crashes11.4%
↓-56.5%prior 46
No Injury109no injury crashes61.9%
↓-50.0%prior 218

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Most severe injury per crash record

Top Contributing Factors

The top contributing factor, 'Inattention', decreased in count from 97 in May 2023 to 43 in May 2024, a 55.67% reduction. 'Failed to yield right of way' also saw a significant decrease, from 68 crashes to 21 crashes, a 69.12% drop. The ranking of 'No improper driving' remained third, decreasing slightly from 35 to 33 crashes, a 5.71% change.

Officer-Reported Primary Contributing Cause

Inattention43 (24.4%)↓-55.7%prior 97
No improper driving33 (18.8%)↓-5.7%prior 35
Failed to yield right of way21 (11.9%)↓-69.1%prior 68
Followed too closely17 (9.7%)↑30.8%prior 13
Failure to keep in proper lane or running off road8 (4.5%)↓-65.2%prior 23
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner8 (4.5%)↓-20.0%prior 10
Exceeded authorized speed limit7 (4%)↓-12.5%prior 8
Disregarded traffic signs, signals, road markings4 (2.3%)↓-76.5%prior 17
Driving too fast for conditions4 (2.3%)↓-60.0%prior 10
Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway3 (1.7%)↓-62.5%prior 8

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in clear weather conditions decreased from 310 in May 2023 to 124 in May 2024. Crashes in daylight conditions also decreased, from 284 to 126. Similarly, crashes on dry road surfaces fell from 333 to 144, while crashes on wet surfaces decreased marginally from 33 to 31.

Weather

Clear124 (70.5%)
↓-60.0%prior 310
Cloudy19 (10.8%)
↓-9.5%prior 21
Rain13 (7.4%)
↓-27.8%prior 18
Cloudy/Rain8 (4.5%)
Clear/Cloudy5 (2.8%)
↓-37.5%prior 8
Rain/Cloudy3 (1.7%)
Clear/Other2 (1.1%)
Clear/Unknown1 (0.6%)
Unknown/Clear1 (0.6%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Weather condition at time of crash

Lighting

Daylight126 (72.4%)
↓-55.6%prior 284
Dark - lighted roadway41 (23.6%)
↓-38.8%prior 67
Dusk4 (2.3%)
↓-60.0%prior 10
Dawn2 (1.1%)
↓-60.0%prior 5
Dark - unknown roadway lighting1 (0.6%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Lighting condition field

Road Surface

Dry144 (82.3%)
↓-56.8%prior 333
Wet31 (17.7%)
↓-6.1%prior 33

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Road surface condition field

Vehicles & Demographics

The total number of vehicles involved in crashes decreased from 699 in May 2023 to 341 in May 2024. Honda remained the top vehicle make involved, though its count decreased from 114 to 51, and Toyota remained second, decreasing from 90 to 44. In terms of age distribution, all age groups saw a reduction in person counts, with the 26-34 age group experiencing the largest drop from 172 to 78 persons.

Top Vehicle Makes (341 vehicles)

1
HONDA51 (15%)
↓-55.3%prior 114
2
TOYOTA44 (12.9%)
↓-51.1%prior 90
3
HYUNDAI30 (8.8%)
↓-37.5%prior 48
4
FORD26 (7.6%)
↓-57.4%prior 61
5
NISSAN26 (7.6%)
↓-61.8%prior 68
6
CHEVROLET17 (5%)
↓-67.3%prior 52
7
ACURA10 (2.9%)
↓-50.0%prior 20
8
LEXUS8 (2.3%)
→0.0%prior 8
9
INFI8 (2.3%)
↓-42.9%prior 14
10
KIA7 (2.1%)
↓-50.0%prior 14

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Vehicle unit records

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

Sex Distribution (389 persons with recorded sex)

Male213 (54.8%)
↓-50.7%prior 432
Female176 (45.2%)
↓-53.8%prior 381

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Person-level records linked to crash events

Speed Limit Zones

Crashes in 30 mph speed zones decreased from 132 in May 2023 to 60 in May 2024, with the fatal crash count in this zone increasing from 0 to 1. Crashes in 35 mph speed zones also decreased, from 83 to 35, and the fatal crash count in this zone increased from 0 to 1. These changes indicate a shift in the presence of fatal crashes within these speed limit categories.

Fatal crashes by zone: 30 mph: 1 of 60 (1.667%) · 35 mph: 1 of 35 (2.857%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · 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: 2024-05-01 through 2024-05-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2024-05-01 through 2024-05-31 (31 days)
  • Geographic scope: SPRINGFIELD, MA
  • Total crash records analyzed: 176
  • Total persons involved: 468
  • Total vehicles involved: 341

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: May 2024." Published June 21, 2026. Reporting period: 2024-05-01 to 2024-05-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/springfield/may-2024-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

Springfield, MA Crash Report — May 2024 | ThatCarHitMe.com