Monthly Traffic Safety Analysis

352 CRASHES IN
SPRINGFIELD, MA
JANUARY 2023

All metrics benchmarked againstJanuary 2022

In January 2023, Springfield experienced 352 crashes, a 9.74% decrease compared to the 390 crashes recorded in January 2022. The most notable shift was the increase in total fatalities, rising from 0 in the prior period to 1 in the current period.

352

-9.7%was 390

Total Crash Events

1

Persons Killed

171

7.5%was 159

Persons Injured

40

-21.6%was 51

Hit-and-Run Crashes

Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 16 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 2023-01-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, the total number of crashes in Springfield decreased by 9.74% year-over-year, falling from 390 crashes in January 2022 to 352 crashes in January 2023. Despite this reduction in total crashes, total injuries increased by 7.55%, from 159 to 171.

40

Hit-and-Run Crashes — January 2023

-21.6% vs prior (51)

The number of hit-and-run crashes decreased by 11, from 51 in the prior period to 40 in the current period. This resulted in a reduction of the hit-and-run rate from 13.1% to 11.4% of total crashes.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

0

Cyclists Killed

Prior: 00.0%

1

Motorists Killed

Prior: 0%

8

Pedestrians Injured

Prior: 3166.7%

2

Cyclists Injured

Prior: 0%

161

Motorists Injured

Prior: 1563.2%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · 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 Wednesday with 66 crashes in January 2022 to Tuesday with 64 crashes in January 2023. The peak hour also changed, moving from 8 AM with 38 crashes in the prior period to 5 PM with 35 crashes in the current period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Total fatalities increased from 0 in January 2022 to 1 in January 2023, resulting in a fatal crash rate of 0.28% in the current period. Serious injuries (code A) decreased from 8 (2.1% of crashes) to 2 (0.6% of crashes), while minor injuries (code B) increased from 52 (13.3% of crashes) to 68 (19.3% of crashes).

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.3%
Serious Injury2serious injury crashes0.6%
-75.0%prior 8
Minor Injury68minor injury crashes19.3%
30.8%prior 52
Possible Injury48possible injury crashes13.6%
2.1%prior 47
No Injury217no injury crashes61.6%
-1.4%prior 220

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Most severe injury per crash record

Top Contributing Factors

Comparing contributing factors, 'Driving too fast for conditions' saw a significant decrease of 25 crashes, from 44 in the prior period to 19 in the current period. Conversely, 'Failed to yield right of way' increased by 16 crashes, from 50 to 66. 'Distracted' driving crashes also rose by 3, from 5 to 8, representing a 60% increase in count.

Officer-Reported Primary Contributing Cause

Inattention74 (21%)-1.3%prior 75
Failed to yield right of way66 (18.8%)32.0%prior 50
No improper driving42 (11.9%)16.7%prior 36
Failure to keep in proper lane or running off road23 (6.5%)-30.3%prior 33
Disregarded traffic signs, signals, road markings22 (6.3%)10.0%prior 20
Driving too fast for conditions19 (5.4%)-56.8%prior 44
Followed too closely17 (4.8%)-22.7%prior 22
Other improper action9 (2.6%)28.6%prior 7
Distracted8 (2.3%)60.0%prior 5
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner8 (2.3%)-11.1%prior 9

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in clear weather conditions decreased from 262 to 153 year-over-year, while crashes during rain increased significantly from 13 to 60. Correspondingly, crashes on dry road surfaces decreased from 234 to 184, whereas crashes on wet surfaces rose from 53 to 141.

Weather

Clear153 (44.1%)
-41.6%prior 262
Rain60 (17.3%)
361.5%prior 13
Cloudy57 (16.4%)
83.9%prior 31
Cloudy/Rain31 (8.9%)
520.0%prior 5
Snow14 (4.0%)
-36.4%prior 22
Clear/Cloudy5 (1.4%)
-16.7%prior 6
Rain/Snow5 (1.4%)
Cloudy/Snow3 (0.9%)
Rain/Sleet, hail (freezing rain or drizzle)3 (0.9%)
Snow/Sleet, hail (freezing rain or drizzle)3 (0.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Weather condition at time of crash

Lighting

Daylight192 (54.9%)
-9.4%prior 212
Dark - lighted roadway130 (37.1%)
-12.2%prior 148
Dusk13 (3.7%)
18.2%prior 11
Dawn11 (3.1%)
57.1%prior 7
Dark - roadway not lighted4 (1.1%)
-33.3%prior 6

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Lighting condition field

Road Surface

Dry184 (52.6%)
-21.4%prior 234
Wet141 (40.3%)
166.0%prior 53
Snow15 (4.3%)
-72.2%prior 54
Ice5 (1.4%)
-89.1%prior 46
Slush3 (0.9%)
Other1 (0.3%)
Water (standing, moving)1 (0.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Road surface condition field

Vehicles & Demographics

The total number of vehicles involved in crashes decreased from 732 to 685 year-over-year. Honda vehicles involved in crashes decreased by 39, from 125 to 86, while Toyota vehicles increased by 17, from 89 to 106. The 65+ age group saw the largest increase in persons involved in crashes, rising by 29 from 49 to 78.

Top Vehicle Makes (685 vehicles)

1
TOYOTA106 (15.5%)
19.1%prior 89
2
HONDA86 (12.6%)
-31.2%prior 125
3
NISSAN64 (9.3%)
-7.2%prior 69
4
HYUNDAI54 (7.9%)
3.8%prior 52
5
FORD51 (7.4%)
-7.3%prior 55
6
CHEVROLET51 (7.4%)
10.9%prior 46
7
JEEP28 (4.1%)
0.0%prior 28
8
SUBARU23 (3.4%)
15.0%prior 20
9
BMW16 (2.3%)
60.0%prior 10
10
DODGE13 (1.9%)
-23.5%prior 17

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Vehicle unit records

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

Sex Distribution (794 persons with recorded sex)

Male416 (52.4%)
-4.4%prior 435
Female378 (47.6%)
8.3%prior 349

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · Person-level records linked to crash events

Speed Limit Zones

One fatal crash occurred in a 30 mph speed zone in January 2023, compared to zero fatal crashes in any speed zone in January 2022. The number of crashes in 25 mph zones decreased by 24, from 126 to 102, and crashes in 30 mph zones decreased by 12, from 152 to 140.

Fatal crashes by zone: 30 mph: 1 of 140 (0.714%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-01-31 · 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 2023-01-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2023-01-01 through 2023-01-31 (31 days)
  • Geographic scope: SPRINGFIELD, MA
  • Total crash records analyzed: 352
  • Total persons involved: 899
  • Total vehicles involved: 685

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: January 2023." Published June 21, 2026. Reporting period: 2023-01-01 to 2023-01-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/springfield/january-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

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Springfield, MA Crash Report — January 2023 | ThatCarHitMe.com