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.

SponsoredThatCarHitMe.com

If someone you love is part of this data, call us.

We'll connect you with a vetted California attorney who handles fatal-crash and wrongful death cases and will evaluate whether you have a case.

(888) 988-8341Free for accident victims

Yearly Traffic Safety Analysis

3,583 CRASHES IN
CALIFORNIA
2024

All metrics benchmarked against2023

In 2024, there were 3,583 fatal crashes, a 6.4% decrease from the 3,826 fatal crashes recorded in 2023. Overall, key metrics including total fatalities and injuries also saw a decline. One of the most significant year-over-year shifts was a 13.7% reduction in fatal crashes involving speeding, which fell from 1,195 to 1,031.

3,583

▼ -6.4%was 3,826

Total Crash Events

3,876

▼ -7.0%was 4,169

Persons Killed

2,501

▼ -4.9%was 2,630

Persons Injured

448

▼ -8.0%was 487

Hit-and-Run Crashes

Note: "Persons Killed" (3,876) counts individual fatalities across all crash events. "Fatal" in the severity table below (3,583) 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 · 2024-01-01 to 2024-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend in fatal traffic incidents is downward year-over-year. Total fatal crashes decreased by 6.4%, from 3,826 to 3,583. Correspondingly, the number of fatalities fell by 7.0% (from 4,169 to 3,876), and total injuries in these crashes declined by 4.9% (from 2,630 to 2,501).

448

Hit-and-Run Crashes — 2024

▼ -8.0% vs prior (487)

Fatal hit-and-run crashes showed a downward trend year-over-year. The total number of such incidents decreased from 487 in 2023 to 448 in 2024. The hit-and-run rate, representing the proportion of all fatal crashes that were hit-and-runs, also saw a slight dip from 12.7% to 12.5%.

Vulnerable Road User Casualties

1,090

Pedestrians Killed

Prior: 1,150↓-5.2%

163

Cyclists Killed

Prior: 151↑7.9%

2,573

Motorists Killed

Prior: 2,822↓-8.8%

Other Killed

Prior: 46↑8.7%

Pedestrians Injured

Prior: 64↓-21.9%

Cyclists Injured

Prior: 8↓-37.5%

2,445

Motorists Injured

Prior: 2,556↓-4.3%

Other Injured

Prior: 2↓-50.0%

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2024-01-01 to 2024-12-31 · 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 remained largely consistent between the two periods. Sunday was the peak day for crashes in both 2024 (617 crashes) and 2023 (690 crashes). However, the peak hour for these incidents shifted from 8 p.m. in the prior year (264 crashes) to midnight in the current year (271 crashes). Both periods show that weekends and late-night hours are the most frequent times for fatal collisions.

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2024-01-01 to 2024-12-31 · Crash date field aggregated by weekday

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2024-01-01 to 2024-12-31 · Crash time field aggregated by hour (0-23)

Road & Environmental Conditions

The conditions under which fatal crashes occurred were broadly similar year-over-year. In both periods, the vast majority of incidents happened in clear weather, accounting for 83.0% of crashes in 2024 and 81.1% in 2023. The proportion of crashes occurring in low-light conditions (dark, dusk, or dawn) also remained stable, representing 64.2% of crashes in the current period compared to 64.6% in the prior period.

Weather

Clear2,974 (83.4%)

↓-4.1%prior 3,102

Cloudy368 (10.3%)

↓-12.6%prior 421

Rain169 (4.7%)

↓-18.8%prior 208

Fog, Smog, Smoke38 (1.1%)

↑137.5%prior 16

Severe Crosswinds8 (0.2%)

↑14.3%prior 7

Snow5 (0.1%)

→0.0%prior 5

Reported as Unknown2 (0.1%)

Other1 (0.0%)

↓-80.0%prior 5

Blowing Snow1 (0.0%)

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2024-01-01 to 2024-12-31 · Weather condition at time of crash

Lighting

Daylight1,271 (35.5%)

↓-5.5%prior 1,345

Dark - Lighted1,224 (34.2%)

↓-6.2%prior 1,305

Dark - Not Lighted919 (25.7%)

↓-3.2%prior 949

Dusk82 (2.3%)

↓-15.5%prior 97

Dawn58 (1.6%)

↓-24.7%prior 77

Dark - Unknown Lighting19 (0.5%)

↓-56.8%prior 44

Reported as Unknown3 (0.1%)

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2024-01-01 to 2024-12-31 · Lighting condition field

Vehicles & Demographics

The top vehicle makes involved in fatal crashes remained consistent across both years. Toyota, Honda, Ford, and Chevrolet were the four most frequent makes in both 2024 and 2023. While Toyota's involvement was stable (829 vehicles in 2024 vs. 819 in 2023), the number of Hondas (567 vs. 651) and Fords (478 vs. 530) involved in fatal crashes decreased.

Top Vehicle Makes (5,390 vehicles)

TOYOTA829 (15.4%)

↑1.2%prior 819

HONDA567 (10.5%)

↓-12.9%prior 651

FORD478 (8.9%)

↓-9.8%prior 530

CHEVROLET478 (8.9%)

↑1.5%prior 471

NISSAN/DATSUN294 (5.5%)

↓-18.3%prior 360

DODGE210 (3.9%)

↓-21.1%prior 266

UNKNOWN MAKE176 (3.3%)

↓-6.9%prior 189

HARLEY-DAVIDSON163 (3%)

↓-20.9%prior 206

GMC144 (2.7%)

↑11.6%prior 129

HYUNDAI137 (2.5%)

↑12.3%prior 122

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2024-01-01 to 2024-12-31 · Vehicle unit records

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

Sex Distribution (8,817 persons with recorded sex)

Male6,092 (69.1%)

↓-6.3%prior 6,505

Female2,725 (30.9%)

↓-6.6%prior 2,917

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2024-01-01 to 2024-12-31 · Person-level records linked to crash events

Speed Limit Zones

The distribution of fatal crashes across different speed zones showed a similar pattern year-over-year, with 55 mph zones having the highest number of incidents in both 2024 (867 crashes) and 2023 (882 crashes). There was a general decrease in crashes across most zones, including a drop in 35 mph zones (from 550 to 467) and 65 mph zones (from 765 to 724). Because this dataset exclusively contains fatal crashes, the fatality rate for incidents in every reported speed zone was 100% for both periods.

Fatal crashes by zone: 15 mph: 11 of 11 (100%) · 20 mph: 2 of 2 (100%) · 25 mph: 206 of 206 (100%) · 30 mph: 88 of 88 (100%) · 35 mph: 467 of 467 (100%) · 40 mph: 399 of 399 (100%) · 45 mph: 434 of 434 (100%) · 50 mph: 176 of 176 (100%) · 55 mph: 867 of 867 (100%) · 60 mph: 28 of 28 (100%) · 65 mph: 724 of 724 (100%) · 70 mph: 131 of 131 (100%)

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2024-01-01 to 2024-12-31 · 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: 2024-01-01 through 2024-12-31
Report generated: August 5, 2026

Data Coverage

Reporting period: 2024-01-01 through 2024-12-31 (366 days)
Geographic scope: California
Total crash records analyzed: 3,583
Total persons involved: 9,093
Total vehicles involved: 5,390

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: 2024." Published August 5, 2026. Reporting period: 2024-01-01 to 2024-12-31. Data source: NHTSA FARS (Fatal Crashes), NHTSA FARS Open Data. Available at: https://thatcarhitme.com/crash-data/california/fatal/statewide/2024-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

ThatCarHitMe.com · An Injuria.ai Company