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

273 CRASHES IN
CALIFORNIA
MAY 2024

All metrics benchmarked againstMay 2023

In May 2024, there were 273 fatal crashes, a 15.7% decrease from the 324 fatal crashes recorded in May 2023. This overall downward trend was reflected across most key metrics. The most notable year-over-year shift was a significant reduction in pedestrian fatalities, which fell from 98 to 66.

273

▼ -15.7%was 324

Total Crash Events

299

▼ -16.2%was 357

Persons Killed

194

▼ -21.5%was 247

Persons Injured

▼ -20.0%was 45

Hit-and-Run Crashes

Note: "Persons Killed" (299) counts individual fatalities across all crash events. "Fatal" in the severity table below (273) 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 · 2024-05-01 to 2024-05-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Traffic safety metrics improved year-over-year. Fatal crashes decreased by 15.7% from 324 to 273. Concurrently, total fatalities fell by 16.2% (from 357 to 299), and total injuries dropped by 21.5% (from 247 to 194).

Hit-and-Run Crashes — May 2024

▼ -20.0% vs prior (45)

Fatal hit-and-run crashes saw a decrease in both absolute numbers and as a percentage of total crashes. The count of hit-and-run incidents fell from 45 in May 2023 to 36 in May 2024. This corresponds to a slight decrease in the hit-and-run rate from 13.9% to 13.2% of all fatal crashes.

Vulnerable Road User Casualties

Pedestrians Killed

Prior: 98↓-32.7%

Cyclists Killed

Prior: 10↑10.0%

218

Motorists Killed

Prior: 247↓-11.7%

Other Killed

Prior: 2↑100.0%

Pedestrians Injured

Prior: 3↑100.0%

Cyclists Injured

Prior: 0→0.0%

188

Motorists Injured

Prior: 244↓-23.0%

Other Injured

Prior: 0→0.0%

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 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 temporal patterns of fatal crashes shifted between the two periods. The peak day for crashes moved from Sunday (66 crashes) in May 2023 to a tie between Saturday and Sunday (46 crashes each) in May 2024. More significantly, the peak hour for these incidents changed from 9 p.m. (34 crashes) in the prior year to 2 a.m. (23 crashes) in the current year.

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2024-05-01 to 2024-05-31 · Crash date field aggregated by weekday

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2024-05-01 to 2024-05-31 · Crash time field aggregated by hour (0-23)

Road & Environmental Conditions

The distribution of fatal crashes by environmental conditions showed some year-over-year changes. The proportion of incidents in 'Clear' weather increased from 77.8% in May 2023 to 87.5% in May 2024, while the share of crashes in 'Cloudy' weather decreased from 17.3% to 11.0%. Crash distribution by lighting conditions remained largely consistent, with 'Daylight' crashes making up 41.4% of the total in the current period compared to 39.2% in the prior period.

Weather

Clear239 (87.9%)

↓-5.2%prior 252

Cloudy30 (11.0%)

↓-46.4%prior 56

Rain2 (0.7%)

↓-77.8%prior 9

Fog, Smog, Smoke1 (0.4%)

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2024-05-01 to 2024-05-31 · Weather condition at time of crash

Lighting

Daylight113 (41.4%)

↓-11.0%prior 127

Dark - Lighted86 (31.5%)

↓-20.4%prior 108

Dark - Not Lighted55 (20.1%)

↓-23.6%prior 72

Dawn9 (3.3%)

→0.0%prior 9

Dusk7 (2.6%)

Dark - Unknown Lighting3 (1.1%)

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2024-05-01 to 2024-05-31 · Lighting condition field

Vehicles & Demographics

The makes of vehicles involved in fatal crashes were broadly consistent year-over-year. Toyota (67) and Honda (44) remained the top two makes in May 2024, though their counts decreased from 78 and 50, respectively. The age distribution of persons involved was also stable, with the 26-34 age group constituting the largest share in both May 2024 (125 people) and May 2023 (173 people).

Top Vehicle Makes (433 vehicles)

TOYOTA67 (15.5%)

↓-14.1%prior 78

HONDA44 (10.2%)

↓-12.0%prior 50

FORD39 (9%)

↓-2.5%prior 40

CHEVROLET36 (8.3%)

↓-20.0%prior 45

NISSAN/DATSUN31 (7.2%)

↑14.8%prior 27

HARLEY-DAVIDSON20 (4.6%)

↓-16.7%prior 24

DODGE15 (3.5%)

↓-53.1%prior 32

JEEP / KAISER-JEEP / WILLYS- JEEP14 (3.2%)

↓-12.5%prior 16

UNKNOWN MAKE12 (2.8%)

↓-33.3%prior 18

HYUNDAI11 (2.5%)

↓-15.4%prior 13

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2024-05-01 to 2024-05-31 · Vehicle unit records

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

Sex Distribution (664 persons with recorded sex)

Male474 (71.4%)

↓-17.6%prior 575

Female190 (28.6%)

↓-30.9%prior 275

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 2024-05-01 to 2024-05-31 · Person-level records linked to crash events

Speed Limit Zones

The distribution of fatal crashes across different speed zones remained relatively consistent, with decreases in volume across most categories. In both periods, the highest number of crashes occurred in zones with speed limits between 40 and 55 mph, with 138 incidents in May 2024 compared to 179 in May 2023. Crashes in zones of 60 mph or higher also decreased from 77 to 72 year-over-year.

Fatal crashes by zone: 25 mph: 16 of 16 (100%) · 30 mph: 10 of 10 (100%) · 35 mph: 35 of 35 (100%) · 40 mph: 37 of 37 (100%) · 45 mph: 39 of 39 (100%) · 50 mph: 12 of 12 (100%) · 55 mph: 50 of 50 (100%) · 60 mph: 3 of 3 (100%) · 65 mph: 60 of 60 (100%) · 70 mph: 9 of 9 (100%)

Source: NHTSA FARS (Fatal Crashes) · Federal fatal-crash census · 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 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: 2024-05-01 through 2024-05-31
Report generated: August 5, 2026

Data Coverage

Reporting period: 2024-05-01 through 2024-05-31 (31 days)
Geographic scope: California
Total crash records analyzed: 273
Total persons involved: 684
Total vehicles involved: 433

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: May 2024." Published August 5, 2026. Reporting period: 2024-05-01 to 2024-05-31. Data source: NHTSA FARS (Fatal Crashes), NHTSA FARS Open Data. Available at: https://thatcarhitme.com/crash-data/california/fatal/statewide/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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