Fatal Crashes Only

This report covers fatal crashes only, from the NHTSA Fatality Analysis Reporting System — the federal census of every crash on a US public road that killed someone within 30 days. It does not include injury or property-damage-only crashes, and its totals are not comparable with the all-severity crash reports published elsewhere on this site.

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Yearly Traffic Safety Analysis

28 CRASHES IN
NEWARK, NEW JERSEY
2023

All metrics benchmarked against2022

Fatal crashes in NEWARK increased by 21.7% year-over-year, rising from 23 fatal crashes in the prior year to 28 fatal crashes in the current year. The most notable shift was a 100% increase in motorcycle fatal crashes, which doubled from 2 to 4.

28

21.7%was 23

Total Crash Events

31

10.7%was 28

Persons Killed

27

17.4%was 23

Persons Injured

4

-33.3%was 6

Hit-and-Run Crashes

Note: "Persons Killed" (31) counts individual fatalities across all crash events. "Fatal" in the severity table below (28) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2023-01-01 to 2023-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend indicates an increase in fatal crash activity, with fatal crashes rising by 21.7% from 23 to 28. Total fatalities also increased by 10.7%, from 28 to 31 year-over-year.

4

Hit-and-Run Crashes — 2023

-33.3% vs prior (6)

Hit-and-run fatal crashes decreased by 33.3% year-over-year, falling from 6 in the prior year to 4 in the current year. Consequently, the hit-and-run rate among all fatal crashes declined from 26.1% to 14.3%.

Vulnerable Road User Casualties

10

Pedestrians Killed

Prior: 911.1%

1

Cyclists Killed

Prior: 0%

19

Motorists Killed

Prior: 185.6%

1

Other Killed

Prior: 10.0%

4

Pedestrians Injured

Prior: 0%

0

Cyclists Injured

Prior: 00.0%

23

Motorists Injured

Prior: 230.0%

0

Other Injured

Prior: 00.0%

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2023-01-01 to 2023-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of fatal crashes shifted year-over-year. The peak day for fatal crashes moved from Monday (6 crashes) in the prior year to Sunday (6 crashes) in the current year. The peak hour also shifted, with the prior year's peak at 4 AM (4 crashes) moving to 6 PM (3 crashes) in the current year.

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2023-01-01 to 2023-12-31 · Crash date field aggregated by weekday

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2023-01-01 to 2023-12-31 · Crash time field aggregated by hour (0-23)

Road & Environmental Conditions

Fatal crashes occurring in clear weather conditions increased from 21 to 23 year-over-year, while those in rain increased from 2 to 3. Daylight fatal crashes rose from 5 to 9, and fatal crashes in dark but lighted conditions remained stable at 12 for both periods.

Weather

Clear23 (82.1%)
9.5%prior 21
Rain3 (10.7%)
Cloudy2 (7.1%)

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2023-01-01 to 2023-12-31 · Weather condition at time of crash

Lighting

Dark - Lighted12 (42.9%)
0.0%prior 12
Daylight9 (32.1%)
80.0%prior 5
Dark - Not Lighted3 (10.7%)
Reported as Unknown1 (3.6%)
Dawn1 (3.6%)
Dark - Unknown Lighting1 (3.6%)
Dusk1 (3.6%)

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2023-01-01 to 2023-12-31 · Lighting condition field

Vehicles & Demographics

Top Vehicle Makes (38 vehicles)

1
HONDA5 (13.2%)
-28.6%prior 7
2
HYUNDAI4 (10.5%)
3
TOYOTA4 (10.5%)
4
FORD3 (7.9%)
5
YAMAHA2 (5.3%)
6
DODGE2 (5.3%)
-60.0%prior 5
7
JEEP / KAISER-JEEP / WILLYS- JEEP2 (5.3%)
8
KAWASAKI2 (5.3%)
9
KIA2 (5.3%)
10
NISSAN/DATSUN2 (5.3%)

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2023-01-01 to 2023-12-31 · Vehicle unit records

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

Sex Distribution (71 persons with recorded sex)

Male50 (70.4%)
11.1%prior 45
Female21 (29.6%)
31.3%prior 16

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2023-01-01 to 2023-12-31 · Person-level records linked to crash events

Speed Limit Zones

Fatal crashes in 25 mph speed zones saw a significant increase, rising from 9 in the prior year to 17 in the current year. Conversely, fatal crashes in 55 mph speed zones decreased from 5 to 2, and those in 45 mph zones decreased from 3 to 2.

Fatal crashes by zone: 25 mph: 17 of 17 (100%) · 35 mph: 5 of 5 (100%) · 45 mph: 2 of 2 (100%) · 55 mph: 2 of 2 (100%) · 65 mph: 1 of 1 (100%)

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2023-01-01 to 2023-12-31 · Posted speed limit at crash location

Manner of Collision

The dominant manner of collision for fatal crashes remained 'The First Harmful Event was Not a Collision with a Motor Vehicle in Transport,' increasing from 16 in the prior year to 19 in the current year. Angle collisions also increased from 4 to 5, and front-to-front collisions doubled from 1 to 2.

Manner of Collision

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2023-01-01 to 2023-12-31 · Crash-level records

Vehicle Type

The most frequent vehicle body type in fatal crashes was '4-door sedan, hardtop,' which decreased from 16 in the prior year to 12 in the current year. Fatal crashes involving two-wheel motorcycles increased from 1 to 3, contributing to an overall increase in motorcycle-related fatal crashes from 2 to 4.

Vehicle Type

1
4-door sedan, hardtop12 (31.6%)
2
Compact Utility (Utility Vehicle Categories "Small" and "Midsize")7 (18.4%)
3
Light Pickup5 (13.2%)
4
Two Wheel Motorcycle (excluding motor scooters)3 (7.9%)
5
Large utility (ANSI D16.1 Utility Vehicle Categories and "Full Size" and "Large")3 (7.9%)
6
5-door/4-door hatchback2 (5.3%)
7
Motor Scooter1 (2.6%)
8
2-door sedan,hardtop,coupe1 (2.6%)
9
Other or Unknown automobile type1 (2.6%)

Showing top 9 of 12 reported. 3 additional (3 total) not shown: Single-unit straight truck or Cab-Chassis (GVWR greater than 26,000 lbs.), Convertible(excludes sun-roof,t-bar), Off-road Motorcycle.

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2023-01-01 to 2023-12-31 · Vehicle unit records

Roadway Functional Class

Fatal crashes on 'Other Principal Arterial' roads increased from 7 to 10, and on 'Minor Arterial' roads from 2 to 6. Conversely, fatal crashes on 'Local' roads decreased from 5 to 1, and on 'Interstate' roads from 4 to 2.

Roadway Functional Class

1
Other Principal Arterial10 (35.7%)
2
Minor Arterial6 (21.4%)
3
Major Collector5 (17.9%)
4
Other Freeways and Expressways4 (14.3%)
5
Interstate2 (7.1%)
6
Local1 (3.6%)

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2023-01-01 to 2023-12-31 · Crash-level records

Roadway Ownership

Fatal crashes on roads owned by 'City or Municipal Highway Agency' increased from 8 in the prior year to 15 in the current year, making it the dominant ownership type. Fatal crashes on 'State Highway Agency' roads decreased from 12 to 10, while those on 'County Highway Agency' roads increased from 2 to 3.

Roadway Ownership

1
City or Municipal Highway Agency15 (53.6%)
2
State Highway Agency10 (35.7%)
3
County Highway Agency3 (10.7%)

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2023-01-01 to 2023-12-31 · Crash-level records

Person Type

Drivers remained the dominant person type involved in fatal crashes, increasing from 36 to 38 year-over-year. Pedestrians involved in fatal crashes increased from 9 to 14, and bicyclists increased from 0 to 1.

Person Type

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2023-01-01 to 2023-12-31 · Crash-level records

Person Injury Severity

The number of fatalities (K) increased from 28 to 31 year-over-year, while the number of serious injuries (A) increased from 3 to 7. Minor injuries (B) also rose from 5 to 7, and possible injuries (C) increased from 3 to 7.

Person Injury Severity

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2023-01-01 to 2023-12-31 · Crash-level records

Occupant Safety Equipment

Among persons with known safety equipment use, those reported with 'None Used/Not Applicable' increased from 10 to 15. Conversely, the number of persons using 'Shoulder and Lap Belt Used' decreased from 21 to 13 year-over-year.

Occupant Safety Equipment

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2023-01-01 to 2023-12-31 · Person-level records linked to crash events

Point of Impact

The dominant point of impact for vehicles in fatal crashes was the '12 Clock Point' (front), which increased from 15 in the prior year to 22 in the current year. Impacts at the '1 Clock Point' also increased from 3 to 4.

Point of Impact

"Other" combines 3 smaller categories (3 records): 4 Clock Point (1), 6 Clock Point (1), 9 Clock Point (1).

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2023-01-01 to 2023-12-31 · Vehicle unit records

Vehicles Per Crash

Single-vehicle fatal crashes increased from 14 to 17 year-over-year, and two-vehicle fatal crashes significantly increased from 4 to 10. Crashes involving 3 or more vehicles decreased, with 3-vehicle crashes falling from 2 to 0 and 4-vehicle crashes from 2 to 1.

Vehicles Per Crash

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2023-01-01 to 2023-12-31 · Crash-level records

Data Sources & Methodology

Primary Data Source

All crash data in this report is sourced from NHTSA FARS (Fatal Crashes), accessed programmatically via the NHTSA FARS 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: NHTSA FARS 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: 2023-01-01 through 2023-12-31
  • Report generated: August 5, 2026

Data Coverage

  • Reporting period: 2023-01-01 through 2023-12-31 (365 days)
  • Geographic scope: Newark, New Jersey
  • Total crash records analyzed: 28
  • Total persons involved: 72
  • Total vehicles involved: 38

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). "Newark, New Jersey Crash Intelligence Report: 2023." Published August 5, 2026. Reporting period: 2023-01-01 to 2023-12-31. Data source: NHTSA FARS (Fatal Crashes), NHTSA FARS Open Data. Available at: https://thatcarhitme.com/crash-data/new-jersey/fatal/newark/2023-annual-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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