Yearly Traffic Safety Analysis

597 CRASHES IN
OHIO, OH
2022

All metrics benchmarked against2021

In 2022, Pike County recorded 597 total crashes, a 4.0% increase from the 574 crashes reported in 2021. While the overall crash volume saw a modest rise, the number of resulting fatalities increased by 50%, from 4 in 2021 to 6 in 2022. Total injuries also rose by 18.5% year-over-year, from 211 to 250.

597

4.0%was 574

Total Crash Events

6

50.0%was 4

Persons Killed

250

18.5%was 211

Persons Injured

61

13.0%was 54

Hit-and-Run Crashes

Note: "Persons Killed" (6) counts individual fatalities across all crash events. "Fatal" in the severity table below (6) 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 · 2022-01-01 to 2022-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash trends in Pike County showed an upward trajectory from 2021 to 2022. Total crashes increased by 4.0%, from 574 to 597. This increase was accompanied by a more significant rise in crash severity, with total fatalities increasing from 4 to 6 and total injuries climbing from 211 to 250.

61

Hit-and-Run Crashes — 2022

13.0% vs prior (54)

Hit-and-run incidents increased in both count and as a percentage of total crashes from 2021 to 2022. The number of hit-and-run crashes rose from 54 to 61, a 13.0% increase. This pushed the hit-and-run rate up from 9.4% of all crashes in 2021 to 10.2% in 2022, indicating a rising trend for this type of collision.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 10.0%

5

Motorists Killed

Prior: 366.7%

0

Pedestrians Injured

Prior: 3-100.0%

250

Motorists Injured

Prior: 20820.2%

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2022-01-01 to 2022-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes in Pike County shifted slightly between 2021 and 2022. The peak day for crashes moved from Thursday (100 crashes) in 2021 to Tuesday (105 crashes) in 2022. Similarly, the peak hour for collisions shifted from 3 p.m. in the prior year (50 crashes) to a tie between 4 p.m. and 5 p.m. in the current year (45 crashes each).

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2022-01-01 to 2022-12-31 · Crash date field aggregated by weekday

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2022-01-01 to 2022-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity worsened in 2022 compared to the previous year, with the number of fatal crashes increasing from 4 to 6, raising the fatal crash rate from 0.70 to 1.01 per 100 crashes. While the proportion of serious injury crashes decreased from 5.2% to 3.5%, the share of minor injury crashes grew from 13.2% to 18.8% of all incidents. Consequently, the percentage of crashes with no reported injuries fell from 74.0% in 2021 to 69.7% in 2022.

Outcome by Severity (Crash Events)

Fatal6fatal crashes1%
50.0%prior 4
Serious Injury21serious injury crashes3.5%
-30.0%prior 30
Minor Injury112minor injury crashes18.8%
47.4%prior 76
Possible Injury42possible injury crashes7%
7.7%prior 39
No Injury416no injury crashes69.7%
-2.1%prior 425

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2022-01-01 to 2022-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2022-01-01 to 2022-12-31 · Most severe injury per crash record

Road & Environmental Conditions

The majority of crashes in both 2022 and 2021 occurred in clear weather and on dry roads. However, there was a notable increase in the proportion of crashes happening in dark, unlighted conditions, which rose from 28.2% of all crashes in 2021 to 33.5% in 2022. Crashes during cloudy weather also saw a proportional increase, accounting for 29.6% of incidents in 2022 compared to 23.7% in the prior year.

Weather

Clear336 (56.3%)
0.6%prior 334
Cloudy177 (29.6%)
30.1%prior 136
Rain53 (8.9%)
-8.6%prior 58
Snow18 (3.0%)
0.0%prior 18
Fog; Smog; Smoke11 (1.8%)
-45.0%prior 20
Freezing Rain or Freezing Drizzle1 (0.2%)
Blowing Sand; Soil; Dirt; Snow1 (0.2%)

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2022-01-01 to 2022-12-31 · Weather condition at time of crash

Lighting

Daylight342 (57.3%)
0.3%prior 341
Dark - Roadway Not Lighted200 (33.5%)
23.5%prior 162
Dawn/Dusk27 (4.5%)
-22.9%prior 35
Dark - Lighted Roadway26 (4.4%)
-10.3%prior 29
Dark - Unknown Roadway Lighting2 (0.3%)

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2022-01-01 to 2022-12-31 · Lighting condition field

Road Surface

Dry469 (78.6%)
9.1%prior 430
Wet100 (16.8%)
-6.5%prior 107
Snow19 (3.2%)
90.0%prior 10
Ice8 (1.3%)
-63.6%prior 22
Slush1 (0.2%)

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2022-01-01 to 2022-12-31 · Road surface condition field

Vehicles & Demographics

Chevrolet and Ford vehicles were the most frequently involved in crashes during both periods, with their positions swapping year-over-year; Chevrolet took the top spot in 2022 with 185 vehicles, up from 162, while Ford dropped to second with 158 vehicles, down from 173. The distribution of vehicle types remained stable, with passenger cars, SUVs, and pickups consistently being the three most common types involved. Among persons involved in crashes, the 26-34 age group was the largest demographic in both years, and its count increased from 173 in 2021 to 219 in 2022.

Top Vehicle Makes (887 vehicles)

1
CHEVROLET185 (20.9%)
14.2%prior 162
2
FORD158 (17.8%)
-8.7%prior 173
3
TOYOTA55 (6.2%)
-9.8%prior 61
4
HONDA51 (5.7%)
-1.9%prior 52
5
DODGE51 (5.7%)
-13.6%prior 59
6
JEEP44 (5%)
12.8%prior 39
7
HYUNDAI43 (4.8%)
16.2%prior 37
8
KIA37 (4.2%)
19.4%prior 31
9
GMC33 (3.7%)
17.9%prior 28
10
CHRYSLER26 (2.9%)
36.8%prior 19

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2022-01-01 to 2022-12-31 · Vehicle unit records

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

Sex Distribution (1,183 persons with recorded sex)

Male705 (59.6%)
4.8%prior 673
Female478 (40.4%)
10.4%prior 433

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2022-01-01 to 2022-12-31 · 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: 2022-01-01 through 2022-12-31
  • Report generated: July 5, 2026

Data Coverage

  • Reporting period: 2022-01-01 through 2022-12-31 (365 days)
  • Geographic scope: ohio, OH
  • Total crash records analyzed: 597
  • Total persons involved: 1,213
  • Total vehicles involved: 887

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: 2022." Published July 5, 2026. Reporting period: 2022-01-01 to 2022-12-31. Data source: Ohio Crash Data (ODOT TIMS), Csv Open Data. Available at: https://thatcarhitme.com/crash-data/ohio/statewide/2022-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

Pike County, OH Crash Report — 2022 | ThatCarHitMe.com