Monthly Traffic Safety Analysis

48 CRASHES IN
NORTHAMPTON, MA
JANUARY 2024

All metrics benchmarked againstJanuary 2023

NORTHAMPTON experienced a decrease in total crashes in January 2024 compared to January 2023, with crashes falling from 53 to 48, representing a 9.4% reduction. The most notable shift was a significant decrease in total injuries, which fell by 42.9% from 14 injuries in January 2023 to 8 injuries in January 2024.

48

-9.4%was 53

Total Crash Events

0

Persons Killed

8

-42.9%was 14

Persons Injured

0

-100.0%was 1

Hit-and-Run Crashes

Note: "Persons Killed" (0) counts individual fatalities across all crash events. "Fatal" in the severity table below (0) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 1 crash with unreported severity is not shown in the severity breakdown.

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

Trend Summary

Overall, crashes in NORTHAMPTON decreased by 9.4% year-over-year, from 53 in January 2023 to 48 in January 2024. This decline was accompanied by a more substantial 42.9% reduction in total injuries, which decreased from 14 to 8 during the same period. Fatalities remained at zero for both months.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

0

Motorists Killed

Prior: 00.0%

1

Pedestrians Injured

Prior: 10.0%

7

Motorists Injured

Prior: 13-46.2%

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

When Crashes Happen

The temporal patterns for crashes shifted year-over-year; in January 2024, Monday became the peak day with 14 crashes, while Friday was the peak day in January 2023, also with 14 crashes. The peak hour for crashes moved from 2 PM (9 crashes) in January 2023 to 4 PM (7 crashes) in January 2024, indicating a later afternoon concentration of incidents.

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

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

Crash Severity Breakdown

There were no fatal crashes in either January 2023 or January 2024. Total injuries decreased from 14 to 8, a 42.9% reduction. Minor injury crashes decreased from 8 (15.1% of crashes) to 5 (10.4% of crashes), while possible injury crashes decreased from 3 (5.7% of crashes) to 2 (4.2% of crashes).

Outcome by Severity (Crash Events)

Minor Injury5minor injury crashes10.4%
-37.5%prior 8
Possible Injury2possible injury crashes4.2%
-33.3%prior 3
No Injury40no injury crashes83.3%
-2.4%prior 41

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

Severity Distribution (Crash Events)

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

Top Contributing Factors

Among contributing factors, 'Inattention' increased by 1 crash, from 13 in January 2023 to 14 in January 2024. 'No improper driving' saw a significant increase of 4 crashes, rising from 7 to 11. Conversely, 'Followed too closely' decreased by 1 crash, from 5 to 4, and 'Distracted' as a factor was reported in 4 crashes in January 2023 but not in January 2024.

Officer-Reported Primary Contributing Cause

Inattention14 (29.2%)7.7%prior 13
No improper driving11 (22.9%)57.1%prior 7
Failed to yield right of way6 (12.5%)0.0%prior 6
Followed too closely4 (8.3%)-20.0%prior 5
Driving too fast for conditions3 (6.3%)
Failure to keep in proper lane or running off road3 (6.3%)
Made an improper turn2 (4.2%)
Glare1 (2.1%)
Exceeded authorized speed limit1 (2.1%)
Other improper action1 (2.1%)

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

Road & Environmental Conditions

Crashes occurring in 'Clear' weather conditions decreased from 28 to 24 year-over-year, while 'Cloudy' weather crashes increased from 8 to 11. Crashes in 'Rain' decreased from 7 to 2, and crashes in 'Snow' conditions increased from 1 to 5. Regarding lighting, 'Daylight' crashes decreased from 38 to 35, while crashes in 'Dark - lighted roadway' increased from 8 to 11.

Weather

Clear24 (50.0%)
-14.3%prior 28
Cloudy11 (22.9%)
37.5%prior 8
Snow5 (10.4%)
Rain2 (4.2%)
-71.4%prior 7
Cloudy/Snow1 (2.1%)
Clear/Unknown1 (2.1%)
Rain/Sleet, hail (freezing rain or drizzle)1 (2.1%)
Rain/Snow1 (2.1%)
Clear/Snow1 (2.1%)
Snow/Sleet, hail (freezing rain or drizzle)1 (2.1%)

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

Lighting

Daylight35 (72.9%)
-7.9%prior 38
Dark - lighted roadway11 (22.9%)
37.5%prior 8
Dark - roadway not lighted1 (2.1%)
Dusk1 (2.1%)

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

Road Surface

Dry32 (66.7%)
-3.0%prior 33
Snow7 (14.6%)
Wet5 (10.4%)
-61.5%prior 13
Ice2 (4.2%)
Slush2 (4.2%)

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

Vehicles & Demographics

The total number of vehicles involved in crashes increased by 9.8%, from 102 in January 2023 to 112 in January 2024. Toyota vehicles involved in crashes increased by 9, from 20 to 29, while Honda vehicles decreased by 5, from 15 to 10. The 45-54 age group saw a substantial increase in persons involved in crashes, rising from 7 to 27.

Top Vehicle Makes (112 vehicles)

1
TOYOTA29 (25.9%)
45.0%prior 20
2
HYUNDAI11 (9.8%)
83.3%prior 6
3
HONDA10 (8.9%)
-33.3%prior 15
4
SUBARU8 (7.1%)
60.0%prior 5
5
NISSAN8 (7.1%)
6
CHEVROLET8 (7.1%)
-20.0%prior 10
7
FORD7 (6.3%)
40.0%prior 5
8
JEEP7 (6.3%)
9
MERCEDES-BENZ3 (2.7%)
10
AUDI2 (1.8%)

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

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

Sex Distribution (130 persons with recorded sex)

Male69 (53.1%)
30.2%prior 53
Female59 (45.4%)
13.5%prior 52
X / Unspecified2 (1.5%)

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

Speed Limit Zones

Crashes in the 25 mph speed limit zone decreased by 3, from 18 to 15, and in the 30 mph zone by 2, from 15 to 13. Conversely, crashes in the 45 mph speed limit zone increased by 2, from 2 to 4. There were no fatal crashes reported in any speed limit zone for either period.

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

Data Coverage

  • Reporting period: 2024-01-01 through 2024-01-31 (31 days)
  • Geographic scope: NORTHAMPTON, MA
  • Total crash records analyzed: 48
  • Total persons involved: 142
  • Total vehicles involved: 112

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). "NORTHAMPTON, MA Crash Intelligence Report: January 2024." Published June 21, 2026. Reporting period: 2024-01-01 to 2024-01-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/northampton/january-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

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Northampton, MA Crash Report — January 2024 | ThatCarHitMe.com