Monthly Traffic Safety Analysis

17 CRASHES IN
LEICESTER, MA
MAY 2023

All metrics benchmarked againstMay 2022

In May 2023, Leicester experienced 17 crashes, a 10.5% decrease from the 19 crashes recorded in May 2022. Total injuries increased by 12.5%, rising from 8 to 9. A notable shift is the occurrence of 1 DUI crash in May 2023, compared to 0 in May 2022.

17

-10.5%was 19

Total Crash Events

0

Persons Killed

9

12.5%was 8

Persons Injured

0

Fatal Crash Events

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 · 2023-05-01 to 2023-05-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend shows a decrease in total crashes, from 19 in May 2022 to 17 in May 2023, representing a 10.5% reduction. Conversely, the number of total injuries increased by 12.5%, from 8 in May 2022 to 9 in May 2023.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

0

Motorists Killed

Prior: 00.0%

2

Pedestrians Injured

Prior: 0%

7

Motorists Injured

Prior: 8-12.5%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · 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 with 5 crashes in May 2022 to Monday with 5 crashes in May 2023. The peak hour also changed, with 4 crashes occurring at 3 PM in May 2022, while May 2023 saw 2 crashes at 5 PM as its peak hour. Crashes on Saturday decreased from 5 to 2 year-over-year.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

There were no fatalities reported in either May 2022 or May 2023. Serious injuries (Severity A) increased from 1 crash (5.3% of total crashes) in May 2022 to 2 crashes (11.8%) in May 2023. Minor injuries (Severity B) decreased from 3 crashes (15.8%) to 2 crashes (11.8%) year-over-year.

Outcome by Severity (Crash Events)

Serious Injury2serious injury crashes11.8%
100.0%prior 1
Minor Injury2minor injury crashes11.8%
-33.3%prior 3
Possible Injury2possible injury crashes11.8%
0.0%prior 2
No Injury10no injury crashes58.8%
-23.1%prior 13

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Most severe injury per crash record

Top Contributing Factors

The number of crashes attributed to 'No improper driving' decreased from 6 in May 2022 to 3 in May 2023. Crashes involving 'Inattention' remained consistent with 5 incidents in both periods. 'Failure to keep in proper lane or running off road' crashes increased from 1 in May 2022 to 2 in May 2023.

Officer-Reported Primary Contributing Cause

Inattention5 (29.4%)0.0%prior 5
No improper driving3 (17.6%)-50.0%prior 6
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner3 (17.6%)
Failure to keep in proper lane or running off road2 (11.8%)
Fatigued/asleep1 (5.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

In terms of lighting conditions, crashes occurring in 'Daylight' decreased from 16 in May 2022 to 12 in May 2023. Conversely, crashes in 'Dark - lighted roadway' increased from 2 to 3 during the same period. Data for weather and road surface conditions were not available for comparison in May 2023.

Lighting

Daylight12 (70.6%)
-25.0%prior 16
Dark - lighted roadway3 (17.6%)
Dawn1 (5.9%)
Dusk1 (5.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Lighting condition field

Vehicles & Demographics

Top Vehicle Makes (29 vehicles)

1
FORD5 (17.2%)
0.0%prior 5
2
TOYOTA4 (13.8%)
-33.3%prior 6
3
HONDA4 (13.8%)
4
CHEVROLET4 (13.8%)
5
NISSAN3 (10.3%)
6
MAZDA1 (3.4%)
7
OTH1 (3.4%)
8
VERN1 (3.4%)
9
CADI1 (3.4%)
10
VOLVO1 (3.4%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Vehicle unit records

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

Sex Distribution (34 persons with recorded sex)

Male18 (52.9%)
-25.0%prior 24
Female16 (47.1%)
23.1%prior 13

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Person-level records linked to crash events

Speed Limit Zones

Crashes in the 35 mph speed zone significantly decreased from 8 in May 2022 to 2 in May 2023. Conversely, crashes in the 40 mph zone doubled from 2 to 4 year-over-year. The 45 mph speed zone maintained 3 crashes in both periods, and no fatal crashes were reported in any speed zone for either period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · 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: 2023-05-01 through 2023-05-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2023-05-01 through 2023-05-31 (31 days)
  • Geographic scope: LEICESTER, MA
  • Total crash records analyzed: 17
  • Total persons involved: 36
  • Total vehicles involved: 29

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). "LEICESTER, MA Crash Intelligence Report: May 2023." Published June 21, 2026. Reporting period: 2023-05-01 to 2023-05-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/leicester/may-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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Leicester, MA Crash Report — May 2023 | ThatCarHitMe.com