Yearly Traffic Safety Analysis

3,834 CRASHES IN
DAYTON, OH
2025

All metrics benchmarked against2024

In 2025, Dayton experienced a total of 3834 crashes, an increase of 11.94% from the 3425 crashes recorded in 2024. The most notable year-over-year shift was in total fatalities, which rose by 56.25% from 16 in 2024 to 25 in 2025. Total injuries also saw a slight increase, from 1804 to 1821.

3,834

11.9%was 3,425

Total Crash Events

25

56.3%was 16

Persons Killed

1,821

0.9%was 1,804

Persons Injured

1,599

20.8%was 1,324

Hit-and-Run Crashes

Note: "Persons Killed" (25) counts individual fatalities across all crash events. "Fatal" in the severity table below (24) 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 · 2025-01-01 to 2025-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend indicates an increase in crash activity year-over-year. Total crashes rose by 11.94%, from 3425 in 2024 to 3834 in 2025. This was accompanied by a substantial 56.25% increase in total fatalities, from 16 to 25, while total injuries increased by 0.94% from 1804 to 1821.

1,599

Hit-and-Run Crashes — 2025

20.8% vs prior (1,324)

Hit-and-run crashes increased significantly, rising by 20.77% from 1324 in 2024 to 1599 in 2025. Correspondingly, the hit-and-run rate increased from 38.7% of total crashes in 2024 to 41.7% in 2025. This indicates an upward trend in hit-and-run incidents.

Vulnerable Road User Casualties

4

Pedestrians Killed

Prior: 40.0%

21

Motorists Killed

Prior: 1275.0%

77

Pedestrians Injured

Prior: 85-9.4%

1,744

Motorists Injured

Prior: 1,7191.5%

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-01-01 to 2025-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The peak day for crashes remained Friday in both periods, with 598 crashes in 2025 compared to 578 in 2024. The peak hour for crashes shifted from 3 p.m. in 2024 (260 crashes) to 4 p.m. in 2025 (304 crashes). Crash counts increased across all days of the week and most hours of the day, indicating a general rise in activity.

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-01-01 to 2025-12-31 · Crash date field aggregated by weekday

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-01-01 to 2025-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The fatal crash rate increased from 0.44% in 2024 to 0.63% in 2025. While the proportion of minor injury crashes decreased from 19.7% to 18.9%, the proportion of serious injury crashes slightly increased from 2.7% to 2.9%. The proportion of no injury crashes also increased, rising from 64.9% to 67% of all crashes.

Severity is per crash event (most severe injury). 24 fatal crash events resulted in 25 persons killed.

Outcome by Severity (Crash Events)

Fatal24fatal crashes0.6%
60.0%prior 15
Serious Injury113serious injury crashes2.9%
21.5%prior 93
Minor Injury724minor injury crashes18.9%
7.4%prior 674
Possible Injury403possible injury crashes10.5%
-4.3%prior 421
No Injury2,570no injury crashes67%
15.7%prior 2,222

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-01-01 to 2025-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-01-01 to 2025-12-31 · Most severe injury per crash record

Road & Environmental Conditions

Crashes occurring in clear weather conditions increased from 2407 to 2672, and those in cloudy conditions rose from 472 to 544. A notable shift occurred in snow-related crashes, which increased from 83 to 132 under snow weather conditions and from 69 to 158 on snow-covered road surfaces. Crashes during Dawn/Dusk lighting also increased, from 138 to 201.

Weather

Clear2,672 (69.7%)
11.0%prior 2,407
Cloudy544 (14.2%)
15.3%prior 472
Rain419 (10.9%)
-1.9%prior 427
Snow132 (3.4%)
59.0%prior 83
Other/Unknown53 (1.4%)
152.4%prior 21
Freezing Rain or Freezing Drizzle4 (0.1%)
Sleet; Hail4 (0.1%)
Blowing Sand; Soil; Dirt; Snow2 (0.1%)
Severe Crosswinds2 (0.1%)
Fog; Smog; Smoke2 (0.1%)
-75.0%prior 8

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-01-01 to 2025-12-31 · Weather condition at time of crash

Lighting

Daylight2,357 (61.5%)
13.2%prior 2,082
Dark - Lighted Roadway1,082 (28.2%)
0.9%prior 1,072
Dawn/Dusk201 (5.2%)
45.7%prior 138
Dark - Roadway Not Lighted107 (2.8%)
44.6%prior 74
Other/Unknown50 (1.3%)
61.3%prior 31
Dark - Unknown Roadway Lighting37 (1.0%)
32.1%prior 28

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-01-01 to 2025-12-31 · Lighting condition field

Road Surface

Dry2,940 (76.7%)
10.7%prior 2,655
Wet653 (17.0%)
-0.5%prior 656
Snow158 (4.1%)
129.0%prior 69
Other/Unknown46 (1.2%)
84.0%prior 25
Ice29 (0.8%)
107.1%prior 14
Slush8 (0.2%)

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-01-01 to 2025-12-31 · Road surface condition field

Vehicles & Demographics

The age distribution of persons involved in crashes saw increases across most groups, with a significant rise in the 0-15 age group (from 650 to 853) and the 35-44 age group (from 1099 to 1323). The top three vehicle makes involved in crashes remained Chevrolet, Ford, and Honda in both periods. While Chevrolet counts slightly decreased from 1159 to 1156, Ford and Honda counts increased from 751 to 825 and 444 to 536, respectively.

Top Vehicle Makes (7,457 vehicles)

1
CHEVROLET1,156 (15.5%)
-0.3%prior 1,159
2
FORD825 (11.1%)
9.9%prior 751
3
HONDA536 (7.2%)
20.7%prior 444
4
NISSAN426 (5.7%)
23.1%prior 346
5
DODGE413 (5.5%)
20.4%prior 343
6
TOYOTA402 (5.4%)
3.3%prior 389
7
HYUNDAI261 (3.5%)
2.4%prior 255
8
KIA257 (3.4%)
8.4%prior 237
9
JEEP223 (3%)
16.8%prior 191
10
BUICK218 (2.9%)
4.3%prior 209

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-01-01 to 2025-12-31 · Vehicle unit records

1,448 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (7,714 persons with recorded sex)

Male4,233 (54.9%)
9.2%prior 3,876
Female3,481 (45.1%)
10.3%prior 3,156

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2025-01-01 to 2025-12-31 · 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: 2025-01-01 through 2025-12-31
  • Report generated: July 6, 2026

Data Coverage

  • Reporting period: 2025-01-01 through 2025-12-31 (365 days)
  • Geographic scope: Dayton, OH
  • Total crash records analyzed: 3,834
  • Total persons involved: 8,943
  • Total vehicles involved: 7,457

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). "Dayton, OH Crash Intelligence Report: 2025." Published July 6, 2026. Reporting period: 2025-01-01 to 2025-12-31. Data source: Ohio Crash Data (ODOT TIMS), Csv Open Data. Available at: https://thatcarhitme.com/crash-data/ohio/dayton/2025-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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