Yearly Traffic Safety Analysis

843 CRASHES IN
OHIO, OH
2024

All metrics benchmarked against2023

In Ottawa County, total traffic crashes remained nearly stable, with 843 incidents in 2024 compared to 837 in 2023, an increase of less than 1%. Despite the steady crash volume, the number of people injured rose by over 18% from 233 to 276. The most significant shift was this increase in injury severity, alongside a rise in total fatalities from three to four.

843

0.7%was 837

Total Crash Events

4

33.3%was 3

Persons Killed

276

18.5%was 233

Persons Injured

47

-13.0%was 54

Hit-and-Run Crashes

Note: "Persons Killed" (4) counts individual fatalities across all crash events. "Fatal" in the severity table below (4) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2024-01-01 to 2024-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Year-over-year data for Ottawa County shows a stable trend in the total number of crashes, which increased by only six incidents from 837 to 843. However, the outcomes of these crashes worsened, with total fatalities increasing from three to four and the number of people injured rising by 18.5% from 233 to 276. This indicates that while crash frequency was consistent, the severity of crash outcomes increased.

47

Hit-and-Run Crashes — 2024

-13.0% vs prior (54)

The number of hit-and-run incidents in Ottawa County decreased in 2024 compared to the previous year. There were 47 hit-and-run crashes, down from 54 in 2023. This represents a drop in the hit-and-run rate from 6.5% of all crashes in the prior period to 5.6% in the current period.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

4

Motorists Killed

Prior: 333.3%

7

Pedestrians Injured

Prior: 1600.0%

269

Motorists Injured

Prior: 23215.9%

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 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 crashes shifted slightly between the two periods. In 2024, the peak day for crashes was Friday with 147 incidents, a change from Wednesday (143 incidents) in the prior year. Similarly, the peak hour for collisions moved later into the afternoon, from the 3 PM hour in 2023 (59 crashes) to the 5 PM hour in 2024 (61 crashes).

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2024-01-01 to 2024-12-31 · Crash date field aggregated by weekday

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2024-01-01 to 2024-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While the overall proportion of crashes involving an injury remained stable at approximately 20%, the severity of those injuries increased in 2024. The number of fatal crashes rose from three to four, and crashes resulting in serious injuries increased from 24 to 30. This shift towards more severe outcomes is reflected in the 18.5% year-over-year increase in the total number of people injured.

Outcome by Severity (Crash Events)

Fatal4fatal crashes0.5%
33.3%prior 3
Serious Injury30serious injury crashes3.6%
25.0%prior 24
Minor Injury97minor injury crashes11.5%
6.6%prior 91
Possible Injury44possible injury crashes5.2%
-21.4%prior 56
No Injury668no injury crashes79.2%
0.8%prior 663

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2024-01-01 to 2024-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2024-01-01 to 2024-12-31 · Most severe injury per crash record

Road & Environmental Conditions

The vast majority of crashes in both 2024 and 2023 occurred under ideal conditions: on dry roads, during daylight, and in clear weather. In 2024, the proportion of crashes on dry roads (82.9% vs. 80.3%) and in clear weather (73.1% vs. 68.0%) both saw slight increases compared to the prior year. Conversely, the share of crashes occurring on wet roads and in rainy weather decreased, indicating no correlation between worsening road conditions and the observed increase in crash severity.

Weather

Clear616 (73.1%)
8.3%prior 569
Cloudy120 (14.2%)
-7.0%prior 129
Rain63 (7.5%)
-21.3%prior 80
Snow21 (2.5%)
-46.2%prior 39
Fog; Smog; Smoke11 (1.3%)
0.0%prior 11
Other/Unknown8 (0.9%)
14.3%prior 7
Freezing Rain or Freezing Drizzle3 (0.4%)
Sleet; Hail1 (0.1%)

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2024-01-01 to 2024-12-31 · Weather condition at time of crash

Lighting

Daylight465 (55.2%)
2.0%prior 456
Dark - Roadway Not Lighted228 (27.0%)
-12.0%prior 259
Dark - Lighted Roadway67 (7.9%)
31.4%prior 51
Dawn/Dusk67 (7.9%)
8.1%prior 62
Dark - Unknown Roadway Lighting10 (1.2%)
100.0%prior 5
Other/Unknown6 (0.7%)

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2024-01-01 to 2024-12-31 · Lighting condition field

Road Surface

Dry699 (82.9%)
4.0%prior 672
Wet110 (13.0%)
-11.3%prior 124
Snow19 (2.3%)
-26.9%prior 26
Ice7 (0.8%)
-30.0%prior 10
Other/Unknown6 (0.7%)
Sand; Mud; Dirt; Oil; Gravel1 (0.1%)
Slush1 (0.1%)

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2024-01-01 to 2024-12-31 · Road surface condition field

Vehicles & Demographics

The top vehicle makes involved in crashes remained consistent, with Ford and Chevrolet leading in both years, although both saw a slight decrease in total counts in 2024. An analysis of persons involved in crashes shows a notable demographic shift, with the 65+ age group's representation increasing from 14.9% of all persons in 2023 to 18.7% in 2024. The 16-20 age group also saw a proportional increase from 10.5% to 11.6% of total persons involved.

Top Vehicle Makes (1,237 vehicles)

1
FORD267 (21.6%)
-2.2%prior 273
2
CHEVROLET209 (16.9%)
-4.1%prior 218
3
HONDA88 (7.1%)
39.7%prior 63
4
JEEP74 (6%)
27.6%prior 58
5
TOYOTA71 (5.7%)
26.8%prior 56
6
DODGE56 (4.5%)
-11.1%prior 63
7
GMC41 (3.3%)
-10.9%prior 46
8
KIA35 (2.8%)
29.6%prior 27
9
BUICK31 (2.5%)
0.0%prior 31
10
CHRYSLER30 (2.4%)
-18.9%prior 37

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2024-01-01 to 2024-12-31 · Vehicle unit records

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

Sex Distribution (1,622 persons with recorded sex)

Male952 (58.7%)
-1.9%prior 970
Female670 (41.3%)
-1.6%prior 681

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2024-01-01 to 2024-12-31 · Person-level records linked to crash events

Data Sources & Methodology

Primary Data Source

All crash data in this report is sourced from Ohio Crash Data (ODOT TIMS), accessed programmatically via the Csv 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: Csv 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: July 6, 2026

Data Coverage

  • Reporting period: 2024-01-01 through 2024-12-31 (366 days)
  • Geographic scope: ohio, OH
  • Total crash records analyzed: 843
  • Total persons involved: 1,649
  • Total vehicles involved: 1,237

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). "ohio, OH Crash Intelligence Report: 2024." Published July 6, 2026. Reporting period: 2024-01-01 to 2024-12-31. Data source: Ohio Crash Data (ODOT TIMS), Csv Open Data. Available at: https://thatcarhitme.com/crash-data/ohio/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

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Ottawa County, OH Crash Report — 2024 | ThatCarHitMe.com