Monthly Traffic Safety Analysis

8 CRASHES IN
LITTLETON, MA
APRIL 2023

All metrics benchmarked againstApril 2022

In April 2023, Littleton experienced 8 crashes, a 47% decrease from the 15 crashes reported in April 2022. Despite the overall reduction in crashes, total injuries increased by 100%, rising from 2 in the prior year to 4 in the current period. Fatalities remained at zero in both periods.

8

-46.7%was 15

Total Crash Events

0

Persons Killed

4

100.0%was 2

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.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-04-01 to 2023-04-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall crash incidents in Littleton showed a significant downward trend year-over-year, decreasing by 47%. The total number of crashes fell from 15 in April 2022 to 8 in April 2023.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

0

Other Killed

Prior: 00.0%

2

Motorists Injured

Prior: 20.0%

2

Other Injured

Prior: 0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-04-01 to 2023-04-30 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes shifted year-over-year, with the peak day moving from Friday in April 2022 (4 crashes) to Saturday in April 2023 (2 crashes). Similarly, the peak crash hour shifted from 5 PM (4 crashes) in the prior period to 4 PM (2 crashes) in the current period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-04-01 to 2023-04-30 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-04-01 to 2023-04-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Fatalities remained at zero in both April 2022 and April 2023. While overall crashes decreased, the proportion of crashes resulting in injuries increased significantly, with 25% of crashes in April 2023 leading to minor injuries, compared to 6.7% resulting in possible injuries in April 2022. Consequently, the share of crashes with no injuries decreased from 93.3% to 75% year-over-year.

Outcome by Severity (Crash Events)

Minor Injury2minor injury crashes25%
No Injury6no injury crashes75%
-57.1%prior 14

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-04-01 to 2023-04-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-04-01 to 2023-04-30 · Most severe injury per crash record

Top Contributing Factors

Inattention, which was the leading contributing factor in April 2022 with 6 crashes, decreased to 2 crashes in April 2023, representing a 67% reduction in count. The factor 'Distracted' and 'Failure to keep in proper lane or running off road' remained constant at 1 crash each across both periods. Notably, 'Failed to yield right of way' emerged as a new top factor in April 2023 with 2 crashes, while 'Followed too closely' (3 crashes in April 2022) was not reported in the current period.

Officer-Reported Primary Contributing Cause

Failed to yield right of way2 (25%)
Inattention2 (25%)-66.7%prior 6
Distracted1 (12.5%)
Failure to keep in proper lane or running off road1 (12.5%)
No improper driving1 (12.5%)
Operating defective equipment1 (12.5%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-04-01 to 2023-04-30 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The distribution of crashes by weather conditions remained largely consistent, with 'Clear' conditions accounting for the majority of incidents (13 in April 2022, 7 in April 2023) and 'Rain' conditions decreasing from 2 to 1 crash. Crashes occurring in 'Daylight' conditions increased in proportion, accounting for 87.5% of crashes in April 2023 (7 crashes) compared to 73.3% in April 2022 (11 crashes). Conversely, crashes in 'Dark - roadway not lighted' conditions decreased from 3 to 1 crash year-over-year.

Weather

Clear7 (87.5%)
-46.2%prior 13
Rain1 (12.5%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-04-01 to 2023-04-30 · Weather condition at time of crash

Lighting

Daylight7 (87.5%)
-36.4%prior 11
Dark - roadway not lighted1 (12.5%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-04-01 to 2023-04-30 · Lighting condition field

Road Surface

Dry7 (87.5%)
-46.2%prior 13
Wet1 (12.5%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-04-01 to 2023-04-30 · Road surface condition field

Vehicles & Demographics

Top Vehicle Makes (15 vehicles)

1
HONDA3 (20%)
2
NISSAN3 (20%)
3
TOYOTA3 (20%)
-62.5%prior 8
4
SUBARU2 (13.3%)
5
MAZDA1 (6.7%)
6
FORD1 (6.7%)
7
HYUNDAI1 (6.7%)
8
DODGE1 (6.7%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-04-01 to 2023-04-30 · Vehicle unit records

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

Sex Distribution (19 persons with recorded sex)

Female11 (57.9%)
0.0%prior 11
Male8 (42.1%)
-61.9%prior 21

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-04-01 to 2023-04-30 · Person-level records linked to crash events

Speed Limit Zones

Crashes in the 65 mph speed zone remained constant at 5 crashes in both April 2022 and April 2023. This led to a significant increase in their proportion of total crashes, rising from 33.3% in the prior period to 62.5% in the current period. Crashes in lower speed zones such as 10 mph, 20 mph, 35 mph, 40 mph, 45 mph, and 50 mph were reported in April 2022 but not in April 2023, indicating a shift in crash distribution towards higher speed zones. No fatalities were recorded in any speed zone in either period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-04-01 to 2023-04-30 · 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: 2023-04-01 through 2023-04-30
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2023-04-01 through 2023-04-30 (30 days)
  • Geographic scope: LITTLETON, MA
  • Total crash records analyzed: 8
  • Total persons involved: 20
  • Total vehicles involved: 15

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). "LITTLETON, MA Crash Intelligence Report: April 2023." Published June 21, 2026. Reporting period: 2023-04-01 to 2023-04-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/littleton/april-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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Littleton, MA Crash Report — April 2023 | ThatCarHitMe.com