Monthly Traffic Safety Analysis

7 CRASHES IN
ERVING, MA
JANUARY 2023

All metrics benchmarked againstJanuary 2022

Total crashes in ERVING, MA saw a significant increase of 250% year-over-year, rising from 2 crashes in January 2022 to 7 crashes in January 2023. This marks the most notable shift in crash data between the two periods. While total crashes increased, fatalities remained at 0 in both periods, and total injuries decreased from 1 to 0.

7

250.0%was 2

Total Crash Events

0

Persons Killed

0

-100.0%was 1

Persons Injured

2

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. 7 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

Crash incidents in ERVING, MA are trending upwards, with a substantial increase of 250% in January 2023 compared to January 2022. The total number of crashes rose from 2 to 7 year-over-year. Despite this rise in incidents, no fatalities were recorded in either period.

2

Hit-and-Run Crashes — January 2023

28.6% hit-and-run rate this period vs 0.0% prior. Prior period: 0.

When Crashes Happen

The temporal patterns of crashes shifted significantly year-over-year. In January 2023, the peak day for crashes was Wednesday with 3 incidents, and the peak hour was 9a with 2 crashes, indicating a spread across weekdays and morning hours. This contrasts with January 2022, where both crashes occurred on Saturday, with a peak at 2p, suggesting a concentration on weekend afternoons.

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)

Top Contributing Factors

There were no common contributing factors listed in the primary data between January 2022 and January 2023 for comparative analysis. In January 2023, 'No improper driving' was noted in 2 crashes, while 'Failure to keep in proper lane or running off road' and 'Followed too closely' each contributed to 1 crash. For January 2022, 'Distracted' and 'Over-correcting/over-steering' were each cited in 1 crash.

Officer-Reported Primary Contributing Cause

No improper driving2 (28.6%)
Failure to keep in proper lane or running off road1 (14.3%)
Followed too closely1 (14.3%)

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 daylight conditions increased from 1 in January 2022 to 3 in January 2023, while crashes under dark - lighted roadway conditions remained at 1 for both periods. Crashes on wet road surfaces also saw an increase, rising from 1 in January 2022 to 3 in January 2023. The specific weather conditions reported for crashes were entirely different between the two periods, preventing direct comparison.

Weather

Clear/Unknown2 (33.3%)
Cloudy2 (33.3%)
Clear1 (16.7%)
Cloudy/Unknown1 (16.7%)

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

Lighting

Daylight3 (50.0%)
Dark - lighted roadway1 (16.7%)
Dark - roadway not lighted1 (16.7%)
Dawn1 (16.7%)

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

Road Surface

Dry4 (57.1%)
Wet3 (42.9%)

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

Vehicles & Demographics

Top Vehicle Makes (10 vehicles)

1
FORD3 (30%)
2
SUBARU2 (20%)
3
TOYOTA2 (20%)
4
HONDA1 (10%)
5
MAZDA1 (10%)

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

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

Sex Distribution (10 persons with recorded sex)

Male7 (70.0%)
Female3 (30.0%)

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

No common speed limits were involved in crashes across both periods, making direct year-over-year comparison of specific zones challenging. In January 2023, crashes were distributed across speed limits ranging from 25 mph to 50 mph, with 2 crashes each at 25 mph and 40 mph. In contrast, January 2022 reported crashes at 35 mph and 55 mph. No fatalities were recorded in any speed zone during either period.

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: ERVING, MA
  • Total crash records analyzed: 7
  • Total persons involved: 12
  • Total vehicles involved: 10

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). "ERVING, 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/erving/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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Erving, MA Crash Report — January 2023 | ThatCarHitMe.com