Monthly Traffic Safety Analysis

21,193 CRASHES IN
OHIO, OH
MARCH 2023

All metrics benchmarked againstMarch 2022

In March 2023, Ohio recorded 21,193 total crashes, a 5.5% increase from the 20,098 crashes reported in March 2022. While total fatalities remained unchanged at 93 for both periods, the data shows a notable 27% increase in crashes involving pedestrians, which rose from 178 to 226 year-over-year. Conversely, motorcycle-involved crashes decreased by 43.6%, from 202 to 114.

21,193

5.4%was 20,098

Total Crash Events

93

Persons Killed

7,404

1.7%was 7,279

Persons Injured

3,877

6.5%was 3,642

Hit-and-Run Crashes

Note: "Persons Killed" (93) counts individual fatalities across all crash events. "Fatal" in the severity table below (86) 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 · 2023-03-01 to 2023-03-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash trends in Ohio for March 2023 show an increase compared to the same month in the prior year. Total crashes rose by 5.5%, from 20,098 to 21,193. While the number of fatalities was identical at 93 in both periods, the total number of injuries saw a slight increase of 1.7%, from 7,279 to 7,404.

3,877

Hit-and-Run Crashes — March 2023

6.5% vs prior (3,642)

Hit-and-run incidents increased in both absolute numbers and as a percentage of total crashes. The count of hit-and-run crashes rose from 3,642 in March 2022 to 3,877 in March 2023, an increase of 6.5%. The hit-and-run rate, which measures the proportion of all crashes that are hit-and-runs, edged up slightly from 18.1% to 18.3% over the same period.

Vulnerable Road User Casualties

12

Pedestrians Killed

Prior: 850.0%

81

Motorists Killed

Prior: 85-4.7%

210

Pedestrians Injured

Prior: 16130.4%

7,194

Motorists Injured

Prior: 7,1181.1%

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2023-03-01 to 2023-03-31 · 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 between March 2022 and March 2023. The peak day for crashes moved from Wednesday (3,433 crashes) in the prior year to Friday (4,075 crashes) in the current period. The peak hour for collisions, however, remained consistent, with the 3 p.m. hour seeing the highest volume in both years (1,683 and 1,712 crashes, respectively).

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2023-03-01 to 2023-03-31 · Crash date field aggregated by weekday

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2023-03-01 to 2023-03-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The distribution of crash severity remained largely stable year-over-year, with minor shifts across categories. The proportion of fatal crashes held steady at 0.4% of all incidents in both March 2022 and March 2023, though the absolute count of fatal crashes rose from 79 to 86. The share of serious injury crashes decreased slightly from 2.3% to 2.1%, while crashes resulting in no injuries increased from 74.7% to 75.2% of the total.

Severity is per crash event (most severe injury). 86 fatal crash events resulted in 93 persons killed.

Outcome by Severity (Crash Events)

Fatal86fatal crashes0.4%
8.9%prior 79
Serious Injury448serious injury crashes2.1%
-2.0%prior 457
Minor Injury2,550minor injury crashes12%
0.2%prior 2,544
Possible Injury2,181possible injury crashes10.3%
9.2%prior 1,998
No Injury15,928no injury crashes75.2%
6.0%prior 15,020

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2023-03-01 to 2023-03-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2023-03-01 to 2023-03-31 · Most severe injury per crash record

Road & Environmental Conditions

A comparison of crash conditions reveals a notable increase in incidents occurring during adverse weather. The proportion of crashes happening in the rain grew from 9.6% of all crashes in March 2022 to 15.1% in March 2023. Correspondingly, crashes on wet road surfaces increased from 18.4% to 26.0% of all incidents. The share of crashes occurring in daylight remained relatively stable, shifting from 63.4% to 64.5% year-over-year.

Weather

Clear11,065 (52.2%)
-3.4%prior 11,460
Cloudy4,851 (22.9%)
-6.7%prior 5,199
Rain3,190 (15.1%)
64.5%prior 1,939
Snow1,616 (7.6%)
39.3%prior 1,160
Other/Unknown248 (1.2%)
18.1%prior 210
Severe Crosswinds75 (0.4%)
150.0%prior 30
Sleet; Hail53 (0.3%)
23.3%prior 43
Fog; Smog; Smoke43 (0.2%)
104.8%prior 21
Freezing Rain or Freezing Drizzle32 (0.2%)
52.4%prior 21
Blowing Sand; Soil; Dirt; Snow20 (0.1%)
33.3%prior 15

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2023-03-01 to 2023-03-31 · Weather condition at time of crash

Lighting

Daylight13,662 (64.5%)
7.2%prior 12,750
Dark - Lighted Roadway3,389 (16.0%)
-0.6%prior 3,408
Dark - Roadway Not Lighted2,528 (11.9%)
-0.6%prior 2,544
Dawn/Dusk1,290 (6.1%)
14.6%prior 1,126
Other/Unknown211 (1.0%)
22.7%prior 172
Dark - Unknown Roadway Lighting113 (0.5%)
15.3%prior 98

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2023-03-01 to 2023-03-31 · Lighting condition field

Road Surface

Dry14,126 (66.7%)
-6.9%prior 15,176
Wet5,506 (26.0%)
49.1%prior 3,694
Snow846 (4.0%)
14.9%prior 736
Ice431 (2.0%)
63.9%prior 263
Other/Unknown196 (0.9%)
21.0%prior 162
Slush42 (0.2%)
27.3%prior 33
Water (Standing; Moving)30 (0.1%)
36.4%prior 22
Sand; Mud; Dirt; Oil; Gravel16 (0.1%)
33.3%prior 12

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2023-03-01 to 2023-03-31 · Road surface condition field

Vehicles & Demographics

The demographic profile of persons involved in crashes and the most common vehicle makes remained consistent year-over-year. The top three vehicle makes involved in crashes were Chevrolet, Ford, and Honda in both March 2022 and March 2023, with each showing an increase in total count consistent with the overall rise in crashes. The age distribution of individuals in crashes also saw minimal change, with the 26-34 age group representing the largest share in both periods (16.0% in 2022 vs. 15.6% in 2023).

Top Vehicle Makes (38,127 vehicles)

1
CHEVROLET5,538 (14.5%)
3.9%prior 5,331
2
FORD5,293 (13.9%)
7.0%prior 4,949
3
HONDA3,450 (9%)
8.8%prior 3,171
4
TOYOTA2,922 (7.7%)
9.4%prior 2,672
5
DODGE1,859 (4.9%)
-0.1%prior 1,861
6
NISSAN1,817 (4.8%)
10.4%prior 1,646
7
JEEP1,613 (4.2%)
10.0%prior 1,466
8
HYUNDAI1,509 (4%)
6.9%prior 1,412
9
KIA1,443 (3.8%)
0.0%prior 1,443
10
GMC1,032 (2.7%)
6.6%prior 968

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2023-03-01 to 2023-03-31 · Vehicle unit records

3,500 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (44,835 persons with recorded sex)

Male24,595 (54.9%)
7.5%prior 22,875
Female20,240 (45.1%)
4.5%prior 19,371

Source: Ohio Crash Data (ODOT TIMS) · Csv Open Data · 2023-03-01 to 2023-03-31 · 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: 2023-03-01 through 2023-03-31
  • Report generated: July 5, 2026

Data Coverage

  • Reporting period: 2023-03-01 through 2023-03-31 (31 days)
  • Geographic scope: ohio, OH
  • Total crash records analyzed: 21,193
  • Total persons involved: 47,631
  • Total vehicles involved: 38,127

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: March 2023." Published July 5, 2026. Reporting period: 2023-03-01 to 2023-03-31. Data source: Ohio Crash Data (ODOT TIMS), Csv Open Data. Available at: https://thatcarhitme.com/crash-data/ohio/statewide/march-2023-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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