Monthly Traffic Safety Analysis

35 CRASHES IN
ABINGTON, MA
MARCH 2023

All metrics benchmarked againstMarch 2022

Total crashes in ABINGTON increased by 29.6% year-over-year, rising from 27 crashes in March 2022 to 35 crashes in March 2023. The most notable shift was a 500% increase in hit-and-run crashes, which grew from 1 to 6 incidents. Additionally, total injuries rose by 125%, from 4 to 9.

35

29.6%was 27

Total Crash Events

0

-100.0%was 1

Persons Killed

9

125.0%was 4

Persons Injured

6

500.0%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 · 2023-03-01 to 2023-03-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crash activity in ABINGTON increased year-over-year, with total crashes rising by 29.6%, from 27 in March 2022 to 35 in March 2023. This increase was accompanied by a 125% rise in total injuries, from 4 to 9, while fatalities decreased from 1 to 0.

6

Hit-and-Run Crashes — March 2023

500.0% vs prior (1)

Hit-and-run crashes increased by 500%, from 1 in March 2022 to 6 in March 2023. This also led to a substantial increase in the hit-and-run crash rate, which rose from 3.7% to 17.1% of all crashes.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 00.0%

0

Motorists Killed

Prior: 00.0%

1

Cyclists Injured

Prior: 0%

8

Motorists Injured

Prior: 4100.0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-03-01 to 2023-03-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The peak day for crashes remained Friday in both periods, with 10 crashes in March 2023 compared to 5 in March 2022. The peak hour for crashes shifted from 7 AM (5 crashes) in March 2022 to 3 PM (4 crashes) in March 2023.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-03-01 to 2023-03-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-03-01 to 2023-03-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Fatal crashes decreased from 1 in March 2022 to 0 in March 2023, resulting in a fatal crash rate reduction from 3.7% to 0%. Total injuries increased by 125%, from 4 to 9. Serious injuries (severity A) were reported in 2 crashes (5.7% of total crashes) in March 2023, compared to 0 in the prior period, while minor injuries (severity B) increased from 1 crash (3.7%) to 3 crashes (8.6%).

Outcome by Severity (Crash Events)

Serious Injury2serious injury crashes5.7%
Minor Injury3minor injury crashes8.6%
200.0%prior 1
Possible Injury2possible injury crashes5.7%
0.0%prior 2
No Injury27no injury crashes77.1%
17.4%prior 23

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-03-01 to 2023-03-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-03-01 to 2023-03-31 · Most severe injury per crash record

Top Contributing Factors

"No improper driving" became the most frequent contributing factor in March 2023, increasing by 7 crashes from 4 to 11. "Failed to yield right of way" decreased by 1 crash, from 6 to 5, shifting from the most frequent factor in March 2022 to the second most frequent in March 2023. "Operating vehicle in an erratic, reckless, careless, negligent or aggressive manner" increased by 2 crashes, from 2 to 4.

Officer-Reported Primary Contributing Cause

No improper driving11 (31.4%)
Failed to yield right of way5 (14.3%)-16.7%prior 6
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner4 (11.4%)
Followed too closely3 (8.6%)
Distracted2 (5.7%)
Inattention2 (5.7%)
Visibility obstructed2 (5.7%)
Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway1 (2.9%)
Disregarded traffic signs, signals, road markings1 (2.9%)
Wrong side or wrong way1 (2.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-03-01 to 2023-03-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in clear weather conditions increased from 18 to 20, though their share of total crashes decreased from 66.7% to 57.1%. The number of crashes during rainy conditions increased from 1 to 4. The proportion of crashes on wet road surfaces rose from 14.8% to 17.1%, and 1 crash on ice was reported in March 2023, which was not present in March 2022.

Weather

Clear20 (57.1%)
11.1%prior 18
Clear/Other5 (14.3%)
Rain4 (11.4%)
Clear/Cloudy2 (5.7%)
Unknown/Other1 (2.9%)
Cloudy1 (2.9%)
-80.0%prior 5
Cloudy/Other1 (2.9%)
Sleet, hail (freezing rain or drizzle)/Rain1 (2.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-03-01 to 2023-03-31 · Weather condition at time of crash

Lighting

Daylight22 (64.7%)
10.0%prior 20
Dark - lighted roadway6 (17.6%)
20.0%prior 5
Dusk4 (11.8%)
Dawn2 (5.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-03-01 to 2023-03-31 · Lighting condition field

Road Surface

Dry27 (79.4%)
17.4%prior 23
Wet6 (17.6%)
Ice1 (2.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-03-01 to 2023-03-31 · Road surface condition field

Vehicles & Demographics

Top Vehicle Makes (67 vehicles)

1
TOYOTA12 (17.9%)
33.3%prior 9
2
JEEP7 (10.4%)
3
NISSAN7 (10.4%)
4
FORD6 (9%)
20.0%prior 5
5
HONDA4 (6%)
-50.0%prior 8
6
CHEVROLET4 (6%)
7
SUBARU4 (6%)
8
MAZDA2 (3%)
9
VOLKSWAGEN2 (3%)
10
DODGE2 (3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-03-01 to 2023-03-31 · Vehicle unit records

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

Sex Distribution (89 persons with recorded sex)

Male47 (52.8%)
42.4%prior 33
Female42 (47.2%)
100.0%prior 21

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-03-01 to 2023-03-31 · Person-level records linked to crash events

Speed Limit Zones

Crashes in 30 mph zones increased by 4, from 7 to 11, and crashes in 35 mph zones increased by 6, from 4 to 10. Crashes in 45 mph zones increased by 3, from 3 to 6, with the prior period reporting 1 fatal crash in this zone while the current period reported none. Conversely, crashes in 25 mph zones decreased by 4, from 5 to 1, and in 40 mph zones decreased by 4, from 6 to 2.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-03-01 to 2023-03-31 · 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: 2023-03-01 through 2023-03-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2023-03-01 through 2023-03-31 (31 days)
  • Geographic scope: ABINGTON, MA
  • Total crash records analyzed: 35
  • Total persons involved: 100
  • Total vehicles involved: 67

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). "ABINGTON, MA Crash Intelligence Report: March 2023." Published June 21, 2026. Reporting period: 2023-03-01 to 2023-03-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/abington/march-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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Abington, MA Crash Report — March 2023 | ThatCarHitMe.com