Monthly Traffic Safety Analysis

48 CRASHES IN
BILLERICA, MA
MAY 2024

All metrics benchmarked againstMay 2023

In May 2024, BILLERICA experienced 48 total crashes, a 2.13% increase from the 47 crashes reported in May 2023. The most notable year-over-year shift was in hit-and-run incidents, which increased by 200%, rising from 1 crash to 3 crashes.

48

2.1%was 47

Total Crash Events

0

Persons Killed

16

-5.9%was 17

Persons Injured

3

200.0%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 · 2024-05-01 to 2024-05-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, crashes in BILLERICA showed a slight upward trend, increasing from 47 crashes in May 2023 to 48 crashes in May 2024. This represents a 2.13% increase in total crash volume year-over-year.

3

Hit-and-Run Crashes — May 2024

200.0% vs prior (1)

Hit-and-run crashes increased significantly year-over-year, rising from 1 crash in May 2023 to 3 crashes in May 2024, a 200% increase in count. Consequently, the hit-and-run rate more than doubled, increasing from 2.1% to 6.3% of total crashes.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

16

Motorists Injured

Prior: 17-5.9%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · 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 Friday in May 2023, which saw 10 crashes, to Thursday in May 2024, which recorded 11 crashes. The peak hour also changed, moving from 4 p.m. with 11 crashes in May 2023 to 5 p.m. with 5 crashes in May 2024, indicating a shift in high-frequency crash times.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

There were no fatal crashes reported in either May 2023 or May 2024. Serious injury crashes increased from 0 in May 2023 to 1 in May 2024, representing 2.1% of crashes. Minor injury crashes decreased from 9 (19.1% share) to 7 (14.6% share), while possible injury crashes increased from 5 (10.6% share) to 6 (12.5% share).

Outcome by Severity (Crash Events)

Serious Injury1serious injury crashes2.1%
Minor Injury7minor injury crashes14.6%
-22.2%prior 9
Possible Injury6possible injury crashes12.5%
20.0%prior 5
No Injury34no injury crashes70.8%
6.3%prior 32

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Most severe injury per crash record

Top Contributing Factors

Among contributing factors present in both periods, 'Followed too closely' saw the largest increase, rising from 2 crashes in May 2023 to 8 crashes in May 2024, an increase of 6 crashes. Conversely, 'Inattention' decreased by 4 crashes, from 11 in May 2023 to 7 in May 2024, and 'No improper driving' decreased by 5 crashes, from 9 to 4. 'Failed to yield right of way' and 'Disregarded traffic signs, signals, road markings' each increased by 2 crashes.

Officer-Reported Primary Contributing Cause

Failed to yield right of way8 (16.7%)33.3%prior 6
Followed too closely8 (16.7%)
Inattention7 (14.6%)-36.4%prior 11
Disregarded traffic signs, signals, road markings5 (10.4%)
No improper driving4 (8.3%)-55.6%prior 9
Failure to keep in proper lane or running off road3 (6.3%)
Distracted2 (4.2%)
Operating defective equipment2 (4.2%)
Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway1 (2.1%)
Visibility obstructed1 (2.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Regarding weather conditions, the number of crashes occurring in 'Clear' conditions remained stable at 34 for both periods, while 'Clear/Clear' conditions saw a decrease of 1 crash. Crashes on 'Dry' road surfaces decreased from 42 to 39, while crashes on 'Wet' road surfaces increased from 5 to 8. In terms of lighting, 'Daylight' crashes increased by 2, from 38 to 40, and crashes in 'Dark - lighted roadway' conditions decreased by 4, from 7 to 3.

Weather

Clear34 (70.8%)
0.0%prior 34
Clear/Clear5 (10.4%)
-16.7%prior 6
Cloudy3 (6.3%)
Cloudy/Rain3 (6.3%)
Rain2 (4.2%)
Cloudy/Cloudy1 (2.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Weather condition at time of crash

Lighting

Daylight40 (83.3%)
5.3%prior 38
Dark - lighted roadway3 (6.3%)
-57.1%prior 7
Dusk3 (6.3%)
Dark - roadway not lighted2 (4.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Lighting condition field

Road Surface

Dry39 (83.0%)
-7.1%prior 42
Wet8 (17.0%)
60.0%prior 5

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Road surface condition field

Vehicles & Demographics

The total number of persons involved in crashes decreased from 154 in May 2023 to 119 in May 2024. The 0-15 age group saw a significant decrease in involvement, from 47 persons to 6 persons. Toyota vehicles involved in crashes increased from 10 to 18, while Ford vehicles decreased from 11 to 7.

Top Vehicle Makes (94 vehicles)

1
TOYOTA18 (19.1%)
80.0%prior 10
2
HONDA13 (13.8%)
8.3%prior 12
3
NISSAN9 (9.6%)
12.5%prior 8
4
CHEVROLET8 (8.5%)
-11.1%prior 9
5
FORD7 (7.4%)
-36.4%prior 11
6
JEEP4 (4.3%)
7
SUBARU4 (4.3%)
8
RAM3 (3.2%)
9
HYUNDAI3 (3.2%)
10
CADI2 (2.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Vehicle unit records

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

Sex Distribution (117 persons with recorded sex)

Male64 (54.7%)
-15.8%prior 76
Female52 (44.4%)
-29.7%prior 74
X / Unspecified1 (0.9%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · Person-level records linked to crash events

Speed Limit Zones

Crashes in 30 mph zones increased by 6, from 9 in May 2023 to 15 in May 2024, and 35 mph zones saw an increase of 7 crashes, from 7 to 14. Conversely, crashes in 55 mph zones decreased by 2, from 8 to 6. There were no fatal crashes reported in any speed zone during either period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-05-01 to 2024-05-31 · 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: 2024-05-01 through 2024-05-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2024-05-01 through 2024-05-31 (31 days)
  • Geographic scope: BILLERICA, MA
  • Total crash records analyzed: 48
  • Total persons involved: 119
  • Total vehicles involved: 94

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). "BILLERICA, MA Crash Intelligence Report: May 2024." Published June 21, 2026. Reporting period: 2024-05-01 to 2024-05-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/billerica/may-2024-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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Billerica, MA Crash Report — May 2024 | ThatCarHitMe.com