Monthly Traffic Safety Analysis

13 CRASHES IN
LEE, MA
JULY 2023

All metrics benchmarked againstJuly 2022

In July 2023, LEE experienced 13 crashes, a 7.1% decrease compared to the 14 crashes recorded in July 2022. Total injuries significantly declined by 50%, from 6 in the prior year to 3 in the current period. A notable shift was observed in speeding-related crashes, which increased from 0 in July 2022 to 2 in July 2023.

13

-7.1%was 14

Total Crash Events

0

Persons Killed

3

-50.0%was 6

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 · 2023-07-01 to 2023-07-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crash activity in LEE showed a declining trend year-over-year, with total crashes decreasing by 7.1% from 14 to 13. This reduction was accompanied by a substantial 50% decrease in total injuries, falling from 6 in July 2022 to 3 in July 2023. Fatalities remained at 0 in both periods.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

3

Motorists Injured

Prior: 6-50.0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-07-01 to 2023-07-31 · 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 Saturday in July 2022, with 4 crashes, to Friday in July 2023, also with 4 crashes. Crashes occurring on Friday saw a 300% increase, rising from 1 in the prior period to 4 in the current period, while crashes on Sunday decreased by 66.7% from 3 to 1. The peak crash hour also changed, moving from 9 PM with 2 crashes in July 2022 to 1 PM with 3 crashes in July 2023, representing an infinite increase from 0 crashes at 1 PM in the prior year.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-07-01 to 2023-07-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-07-01 to 2023-07-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The distribution of crash severity changed year-over-year, with minor injuries decreasing by 75% from 4 in July 2022 to 1 in July 2023. Possible injuries, which were not reported in July 2022, accounted for 1 crash in July 2023. The proportion of 'No Injury' crashes increased from 64.3% in July 2022 to 76.9% in July 2023.

Outcome by Severity (Crash Events)

Minor Injury1minor injury crashes7.7%
-75.0%prior 4
Possible Injury1possible injury crashes7.7%
No Injury10no injury crashes76.9%
11.1%prior 9

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-07-01 to 2023-07-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-07-01 to 2023-07-31 · Most severe injury per crash record

Top Contributing Factors

Among contributing factors, 'Inattention' increased by 50%, rising from 2 crashes in July 2022 to 3 crashes in July 2023. Conversely, 'No improper driving' decreased by 33.3%, from 3 crashes to 2 crashes. Factors such as 'Over-correcting/over-steering' and 'Driving too fast for conditions' emerged in July 2023 with 2 crashes each, having been absent in July 2022, while 'Failed to yield right of way' was absent in July 2023 after being associated with 3 crashes in July 2022.

Officer-Reported Primary Contributing Cause

Inattention3 (23.1%)
Over-correcting/over-steering2 (15.4%)
No improper driving2 (15.4%)
Driving too fast for conditions2 (15.4%)
Failure to keep in proper lane or running off road1 (7.7%)
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner1 (7.7%)
Physical impairment1 (7.7%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-07-01 to 2023-07-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in 'Clear' weather conditions decreased by 8.3% from 12 in July 2022 to 11 in July 2023. The number of crashes on 'Wet' road surfaces doubled, increasing from 2 in the prior period to 4 in the current period, while 'Dry' road surface crashes decreased by 25%, from 12 to 9. Crashes during 'Daylight' hours increased by 25%, from 8 to 10, whereas crashes in 'Dark - roadway not lighted' conditions decreased by 75%, from 4 to 1.

Weather

Clear11 (84.6%)
-8.3%prior 12
Cloudy/Rain1 (7.7%)
Rain1 (7.7%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-07-01 to 2023-07-31 · Weather condition at time of crash

Lighting

Daylight10 (76.9%)
25.0%prior 8
Dark - lighted roadway1 (7.7%)
Dark - roadway not lighted1 (7.7%)
Dusk1 (7.7%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-07-01 to 2023-07-31 · Lighting condition field

Road Surface

Dry9 (69.2%)
-25.0%prior 12
Wet4 (30.8%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-07-01 to 2023-07-31 · Road surface condition field

Vehicles & Demographics

Top Vehicle Makes (19 vehicles)

1
HONDA4 (21.1%)
2
TOYOTA4 (21.1%)
3
CHEVROLET3 (15.8%)
4
KIA1 (5.3%)
5
MASE1 (5.3%)
6
MAZDA1 (5.3%)
7
RAM1 (5.3%)
8
BMW1 (5.3%)
9
VOLVO1 (5.3%)
10
HYUNDAI1 (5.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-07-01 to 2023-07-31 · Vehicle unit records

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

Sex Distribution (24 persons with recorded sex)

Male16 (66.7%)
33.3%prior 12
Female8 (33.3%)
14.3%prior 7

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-07-01 to 2023-07-31 · Person-level records linked to crash events

Speed Limit Zones

Crashes occurring in 65 mph speed zones saw a 200% increase, rising from 2 in July 2022 to 6 in July 2023. Conversely, crashes in 25 mph zones decreased by 28.6%, from 7 to 5. Crashes in 35 mph zones also decreased by 50%, from 2 to 1, indicating a shift in crash distribution towards higher speed limit areas.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-07-01 to 2023-07-31 · 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: 2023-07-01 through 2023-07-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2023-07-01 through 2023-07-31 (31 days)
  • Geographic scope: LEE, MA
  • Total crash records analyzed: 13
  • Total persons involved: 28
  • Total vehicles involved: 19

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). "LEE, MA Crash Intelligence Report: July 2023." Published June 21, 2026. Reporting period: 2023-07-01 to 2023-07-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/lee/july-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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Lee, MA Crash Report — July 2023 | ThatCarHitMe.com