Monthly Traffic Safety Analysis

143 CRASHES IN
NEWTON, MA
DECEMBER 2023

All metrics benchmarked againstDecember 2022

Total crashes in December 2023 increased to 143, up from 133 crashes in December 2022, representing a 7.5% year-over-year increase. The most notable shift was a significant increase in hit-and-run incidents, which more than doubled from 11 to 23 crashes.

143

7.5%was 133

Total Crash Events

0

Persons Killed

27

-32.5%was 40

Persons Injured

23

109.1%was 11

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. 8 crashes with unreported severity are not shown in the severity breakdown.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 to 2023-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, total crashes increased by 7.5% year-over-year, from 133 in December 2022 to 143 in December 2023. While fatalities remained at zero in both periods, total injuries decreased by 32.5%, from 40 to 27.

23

Hit-and-Run Crashes — December 2023

109.1% vs prior (11)

Hit-and-run crashes increased significantly year-over-year, rising from 11 incidents in December 2022 to 23 incidents in December 2023. This resulted in the hit-and-run rate more than doubling, from 8.3% to 16.1% of all crashes.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

0

Cyclists Killed

Prior: 00.0%

0

Motorists Killed

Prior: 00.0%

3

Pedestrians Injured

Prior: 6-50.0%

1

Cyclists Injured

Prior: 0%

23

Motorists Injured

Prior: 34-32.4%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 to 2023-12-31 · 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 with 28 incidents in December 2022 to Thursday with 25 incidents in December 2023. The peak hour also shifted slightly, from 6 PM with 16 crashes in the prior period to 5 PM with 17 crashes in the current period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 to 2023-12-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 to 2023-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Fatalities remained at zero in both December 2022 and December 2023. Serious injury crashes (severity 'A') decreased from 4 (3.0% of crashes) in the prior period to 1 (0.7%) in the current period. Overall, total injuries decreased from 40 to 27 year-over-year.

Outcome by Severity (Crash Events)

Serious Injury1serious injury crashes0.7%
-75.0%prior 4
Minor Injury16minor injury crashes11.2%
-15.8%prior 19
Possible Injury4possible injury crashes2.8%
-55.6%prior 9
No Injury114no injury crashes79.7%
17.5%prior 97

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 to 2023-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 to 2023-12-31 · Most severe injury per crash record

Top Contributing Factors

Crashes attributed to 'No improper driving' increased from 32 to 39 incidents, a 21.9% increase in count. 'Followed too closely' saw a 54.5% increase in count, rising from 11 to 17 crashes. 'Operating vehicle in erratic, reckless, careless, negligent or aggressive manner' also doubled, increasing from 2 to 4 crashes.

Officer-Reported Primary Contributing Cause

No improper driving39 (27.3%)21.9%prior 32
Inattention25 (17.5%)13.6%prior 22
Followed too closely17 (11.9%)54.5%prior 11
Failed to yield right of way10 (7%)25.0%prior 8
Driving too fast for conditions6 (4.2%)0.0%prior 6
Failure to keep in proper lane or running off road5 (3.5%)-16.7%prior 6
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner4 (2.8%)
Glare3 (2.1%)
Other improper action3 (2.1%)
Visibility obstructed2 (1.4%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 to 2023-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in 'Clear' weather conditions increased from 63 to 90 incidents year-over-year. Incidents on 'Dry' road surfaces also rose, from 78 to 106 crashes. Conversely, crashes during 'Snow' conditions decreased significantly from 10 to 1 incident.

Weather

Clear90 (62.9%)
42.9%prior 63
Rain21 (14.7%)
-4.5%prior 22
Cloudy14 (9.8%)
7.7%prior 13
Clear/Clear6 (4.2%)
-25.0%prior 8
Cloudy/Rain4 (2.8%)
Rain/Cloudy2 (1.4%)
Fog, smog, smoke2 (1.4%)
Rain/Rain1 (0.7%)
Clear/Unknown1 (0.7%)
Cloudy/Snow1 (0.7%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 to 2023-12-31 · Weather condition at time of crash

Lighting

Daylight78 (54.5%)
16.4%prior 67
Dark - lighted roadway53 (37.1%)
1.9%prior 52
Dusk5 (3.5%)
Dark - roadway not lighted4 (2.8%)
-20.0%prior 5
Dawn3 (2.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 to 2023-12-31 · Lighting condition field

Road Surface

Dry106 (74.1%)
35.9%prior 78
Wet35 (24.5%)
-2.8%prior 36
Ice1 (0.7%)
-85.7%prior 7
Snow1 (0.7%)
-88.9%prior 9

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 to 2023-12-31 · Road surface condition field

Vehicles & Demographics

The total number of vehicles involved in crashes increased from 247 in December 2022 to 271 in December 2023. Among top makes, TOYOTA vehicles involved in crashes increased from 42 to 53, and LEXUS vehicles increased from 6 to 13. Conversely, JEEP vehicles involved decreased from 15 to 10.

Top Vehicle Makes (271 vehicles)

1
TOYOTA53 (19.6%)
26.2%prior 42
2
HONDA33 (12.2%)
3.1%prior 32
3
FORD22 (8.1%)
15.8%prior 19
4
LEXUS13 (4.8%)
116.7%prior 6
5
SUBARU12 (4.4%)
50.0%prior 8
6
CHEVROLET12 (4.4%)
-14.3%prior 14
7
VOLKSWAGEN10 (3.7%)
42.9%prior 7
8
JEEP10 (3.7%)
-33.3%prior 15
9
MAZDA10 (3.7%)
10
BMW10 (3.7%)
25.0%prior 8

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 to 2023-12-31 · Vehicle unit records

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

Sex Distribution (307 persons with recorded sex)

Male162 (52.8%)
-16.5%prior 194
Female145 (47.2%)
5.8%prior 137

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 to 2023-12-31 · Person-level records linked to crash events

Speed Limit Zones

Crashes in 25 MPH zones increased from 52 to 57, and those in 30 MPH zones increased from 31 to 36. Conversely, crashes in 55 MPH zones decreased from 25 to 22. No fatal crashes were recorded in any speed zone during either period.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-12-01 to 2023-12-31 · 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: 2023-12-01 through 2023-12-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2023-12-01 through 2023-12-31 (31 days)
  • Geographic scope: NEWTON, MA
  • Total crash records analyzed: 143
  • Total persons involved: 391
  • Total vehicles involved: 271

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). "NEWTON, MA Crash Intelligence Report: December 2023." Published June 21, 2026. Reporting period: 2023-12-01 to 2023-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/newton/december-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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Newton, MA Crash Report — December 2023 | ThatCarHitMe.com