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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Monthly Traffic Safety Analysis

283 CRASHES IN
CALIFORNIA
JUNE 2024

All metrics benchmarked againstJune 2023

In June 2024, California recorded 283 fatal crashes, a 6.3% decrease from the 302 fatal crashes in June 2023. While overall fatal incidents and related fatalities declined, the number of fatal crashes involving bicyclists saw a notable year-over-year increase, rising from 7 to 19.

283

▼ -6.3%was 302

Total Crash Events

316

▼ -6.2%was 337

Persons Killed

193

▼ -14.6%was 226

Persons Injured

▲ 17.2%was 29

Hit-and-Run Crashes

Note: "Persons Killed" (316) counts individual fatalities across all crash events. "Fatal" in the severity table below (283) 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 · 2024-06-01 to 2024-06-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Fatal traffic incidents in California showed a downward trend in June 2024 compared to the same month last year. The total number of fatal crashes decreased by 6.3%, from 302 to 283. Correspondingly, fatalities fell by 6.2% from 337 to 316, and injuries saw a 14.6% reduction from 226 to 193.

Hit-and-Run Crashes — June 2024

▲ 17.2% vs prior (29)

Hit-and-run incidents trended upward in June 2024 compared to the same month in 2023. The total number of fatal hit-and-run crashes increased from 29 to 34. This represents a rise in the hit-and-run rate from 9.6% of all fatal crashes in the prior period to 12.0% in the current period.

Vulnerable Road User Casualties

Pedestrians Killed

Prior: 76↓-27.6%

Cyclists Killed

Prior: 7↑171.4%

240

Motorists Killed

Prior: 253↓-5.1%

Other Killed

Prior: 1↑100.0%

Pedestrians Injured

Prior: 6↓-50.0%

Cyclists Injured

Prior: 0→0.0%

190

Motorists Injured

Prior: 220↓-13.6%

Other Injured

Prior: 0→0.0%

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2024-06-01 to 2024-06-30 · 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 remained broadly consistent year-over-year, with incidents concentrated in the late evening and on weekends. The peak day for crashes shifted slightly from Saturday (60 crashes) in June 2023 to Sunday (61 crashes) in June 2024. Similarly, the peak hour moved from 9 p.m. in the prior year to 10 p.m. in the current year, with both hours recording 25 crashes.

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2024-06-01 to 2024-06-30 · Crash date field aggregated by weekday

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2024-06-01 to 2024-06-30 · Crash time field aggregated by hour (0-23)

Road & Environmental Conditions

The vast majority of fatal crashes in both periods occurred in 'Clear' weather, with this condition accounting for 94.0% of incidents in June 2024, up from 84.1% in June 2023. Regarding lighting, the distribution remained relatively stable, though there was a proportional increase in crashes occurring in 'Dark - Not Lighted' areas, rising from 20.5% of crashes in the prior year to 25.1% in the current year. Data on road surface conditions was not available for either period.

Weather

Clear266 (95.0%)

↑4.7%prior 254

Cloudy12 (4.3%)

↓-70.0%prior 40

Reported as Unknown1 (0.4%)

Severe Crosswinds1 (0.4%)

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2024-06-01 to 2024-06-30 · Weather condition at time of crash

Lighting

Daylight122 (43.3%)

↓-10.9%prior 137

Dark - Lighted78 (27.7%)

↓-9.3%prior 86

Dark - Not Lighted71 (25.2%)

↑14.5%prior 62

Dusk6 (2.1%)

↓-25.0%prior 8

Dawn4 (1.4%)

↓-20.0%prior 5

Reported as Unknown1 (0.4%)

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2024-06-01 to 2024-06-30 · Lighting condition field

Vehicles & Demographics

The top three vehicle makes involved in fatal crashes remained consistent year-over-year, with Toyota (70 vehicles), Honda (44), and Ford (39) leading in June 2024, all showing a decrease in raw numbers from June 2023. Analysis of persons involved shows the 26-34 age group was the most represented in both periods, with 129 individuals in the current year compared to 147 in the prior year. Notably, the number of individuals in the 35-44 age group involved in fatal crashes increased from 111 to 125.

Top Vehicle Makes (434 vehicles)

TOYOTA70 (16.1%)

↓-9.1%prior 77

HONDA44 (10.1%)

↓-6.4%prior 47

FORD39 (9%)

↓-17.0%prior 47

NISSAN/DATSUN29 (6.7%)

→0.0%prior 29

CHEVROLET26 (6%)

↓-21.2%prior 33

OTHER MAKE16 (3.7%)

↑220.0%prior 5

BMW16 (3.7%)

↑14.3%prior 14

FREIGHTLINER14 (3.2%)

↑133.3%prior 6

HYUNDAI14 (3.2%)

↑100.0%prior 7

HARLEY-DAVIDSON14 (3.2%)

↓-26.3%prior 19

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2024-06-01 to 2024-06-30 · Vehicle unit records

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

Sex Distribution (665 persons with recorded sex)

Male457 (68.7%)

↓-14.9%prior 537

Female208 (31.3%)

↓-11.1%prior 234

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2024-06-01 to 2024-06-30 · Person-level records linked to crash events

Speed Limit Zones

The distribution of fatal crashes across speed zones remained largely similar between June 2023 and June 2024. Roads with a 55 mph speed limit saw the highest number of fatal crashes in both periods, increasing slightly from 78 to 82 incidents. The number of crashes in 65 mph zones held steady at 63. Meanwhile, zones with a 45 mph limit recorded a decrease in fatal crashes from 44 to 35.

Fatal crashes by zone: 15 mph: 1 of 1 (100%) · 25 mph: 18 of 18 (100%) · 30 mph: 2 of 2 (100%) · 35 mph: 23 of 23 (100%) · 40 mph: 26 of 26 (100%) · 45 mph: 35 of 35 (100%) · 50 mph: 16 of 16 (100%) · 55 mph: 82 of 82 (100%) · 65 mph: 63 of 63 (100%) · 70 mph: 12 of 12 (100%)

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2024-06-01 to 2024-06-30 · Posted speed limit at crash location

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: 2024-06-01 through 2024-06-30
Report generated: August 5, 2026

Data Coverage

Reporting period: 2024-06-01 through 2024-06-30 (30 days)
Geographic scope: California
Total crash records analyzed: 283
Total persons involved: 684
Total vehicles involved: 434

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). "California Crash Intelligence Report: June 2024." Published August 5, 2026. Reporting period: 2024-06-01 to 2024-06-30. Data source: NHTSA FARS (Fatal Crashes), NHTSA FARS Open Data. Available at: https://thatcarhitme.com/crash-data/california/fatal/statewide/june-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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