Yearly Traffic Safety Analysis

1,541 CRASHES IN
IOWA, IA
2023

All metrics benchmarked against2022

In 2023, Story County recorded 1,541 total vehicle crashes, a 2.2% increase from the 1,508 crashes in 2022. Despite the rise in total incidents, the number of fatalities fell from 7 to 4. The most notable shift in contributing factors was the 22% increase in the count of crashes involving animals, which rose from 195 to 238 incidents, becoming the leading reported cause in 2023.

1,541

▲ 2.2%was 1,508

Total Crash Events

4

▼ -42.9%was 7

Persons Killed

367

▼ -5.2%was 387

Persons Injured

4

▼ -20.0%was 5

Fatal Crash Events

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: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall crash trends in Story County were mixed year-over-year. While the total number of crashes increased by 2.2% from 1,508 to 1,541, the severity of these incidents decreased. Total injuries dropped by 5.2% from 387 to 367, and total fatalities saw a reduction from 7 in 2022 to 4 in 2023.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

4

Motorists Killed

Prior: 7↓-42.9%

0

Other Killed

Prior: 0→0.0%

9

Pedestrians Injured

Prior: 12↓-25.0%

20

Cyclists Injured

Prior: 21↓-4.8%

336

Motorists Injured

Prior: 353↓-4.8%

2

Other Injured

Prior: 1↑100.0%

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The timing of crashes shifted between the two periods. In 2023, the peak day for crashes was Thursday with 293 incidents, a change from Friday (269 incidents) in the prior year. Similarly, the peak hour for crashes moved later in the afternoon commute, from the 3 p.m. hour in 2022 (148 crashes) to the 5 p.m. hour in 2023 (151 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity decreased in 2023 compared to the previous year. The number of fatal crashes dropped from 5 to 4, and total fatalities fell from 7 to 4. The number of serious injury crashes also declined from 19 to 14, and the count of possible injury crashes decreased from 197 to 184.

Outcome by Severity (Crash Events)

Fatal4fatal crashes0.3%
↓-20.0%prior 5
Serious Injury14serious injury crashes0.9%
↓-26.3%prior 19
Minor Injury119minor injury crashes7.7%
↓-4.8%prior 125
Possible Injury184possible injury crashes11.9%
↓-6.6%prior 197
No Injury1,220no injury crashes79.2%
↑5.0%prior 1,162

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Most severe injury per crash record

Top Contributing Factors

The ranking of top contributing factors changed year-over-year. Crashes attributed to an "Animal" increased in count by 22% from 195 incidents in 2022 to 238 in 2023, making it the most common factor. "Followed too close" remained a top cause but saw its count slightly decrease from 225 to 223 incidents, moving it to the second-ranked position. Crashes where a driver ran a traffic signal decreased in count from 51 to 36 incidents.

Officer-Reported Primary Contributing Cause

Animal238 (15.4%)↑22.1%prior 195
Followed too close223 (14.5%)↓-0.9%prior 225
FTYROW: Making left turn121 (7.9%)↑7.1%prior 113
Other (explain in narrative): Other95 (6.2%)→0.0%prior 95
Driving too fast for conditions88 (5.7%)↓-8.3%prior 96
FTYROW: From stop sign78 (5.1%)↑27.9%prior 61
Ran off road - left68 (4.4%)↑4.6%prior 65
Improper or erratic lane changing57 (3.7%)↑5.6%prior 54
Lost Control45 (2.9%)→0.0%prior 45
FTYROW: From driveway40 (2.6%)↑42.9%prior 28

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Environmental conditions at the time of crashes were largely consistent between 2022 and 2023. In both years, the vast majority of crashes occurred in daylight (990 in 2023 vs. 986 in 2022) and on dry road surfaces (1,056 vs. 1,003). There was a decrease in crashes occurring on snowy roads, which fell from 97 incidents in 2022 to 91 in 2023, and on wet roads, which dropped from 133 to 108.

Weather

Clear985 (74.0%)
↑2.9%prior 957
Cloudy171 (12.8%)
↑0.6%prior 170
Snow85 (6.4%)
↓-15.0%prior 100
Rain50 (3.8%)
↓-27.5%prior 69
Freezing rain/drizzle19 (1.4%)
↑35.7%prior 14
Blowing Snow9 (0.7%)
↓-18.2%prior 11
Fog, smoke, smog7 (0.5%)
Sleet, hail2 (0.2%)
Other (explain in narrative)2 (0.2%)
↓-60.0%prior 5
Severe Winds1 (0.1%)
↓-88.9%prior 9

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Weather condition at time of crash

Lighting

Daylight990 (73.7%)
↑0.4%prior 986
Dark - roadway lighted181 (13.5%)
→0.0%prior 181
Dark - roadway not lighted94 (7.0%)
↓-2.1%prior 96
Dusk39 (2.9%)
↓-4.9%prior 41
Dawn26 (1.9%)
↓-13.3%prior 30
Dark - unknown roadway lighting13 (1.0%)
↑160.0%prior 5

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Lighting condition field

Road Surface

Dry1,056 (79.2%)
↑5.3%prior 1,003
Wet108 (8.1%)
↓-18.8%prior 133
Snow91 (6.8%)
↓-6.2%prior 97
Ice/frost50 (3.8%)
↓-35.1%prior 77
Slush13 (1.0%)
↓-13.3%prior 15
Gravel12 (0.9%)
→0.0%prior 12
Other (explain in narrative)1 (0.1%)
Water (standing or moving)1 (0.1%)
Mud, dirt1 (0.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Road surface condition field

Vehicles & Demographics

The makes of vehicles and demographics of persons involved in crashes showed little change year-over-year. Ford and Chevrolet remained the top two vehicle makes involved in crashes in both periods, with Ford-involved vehicles increasing from 425 to 480. The age distribution of individuals involved was also stable, with the 21-25 age group being the most represented in both 2023 (601 persons) and 2022 (604 persons).

Top Vehicle Makes (2,709 vehicles)

1
FORD480 (17.7%)
↑12.9%prior 425
2
CHEV347 (12.8%)
↓-10.6%prior 388
3
TOYT212 (7.8%)
↓-6.2%prior 226
4
HOND148 (5.5%)
↑6.5%prior 139
5
CHEVROLET118 (4.4%)
↓-2.5%prior 121
6
JEEP116 (4.3%)
↑16.0%prior 100
7
NISS104 (3.8%)
→0.0%prior 104
8
KIA81 (3%)
↑22.7%prior 66
9
DODG79 (2.9%)
↓-17.7%prior 96
10
GMC61 (2.3%)
↑5.2%prior 58

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Vehicle unit records

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

Sex Distribution (2,509 persons with recorded sex)

Male1,393 (55.5%)
↑0.5%prior 1,386
Female1,116 (44.5%)
↑4.7%prior 1,066

Source: Iowa Crash Data · ArcGIS Open Data · 2023-01-01 to 2023-12-31 · Person-level records linked to crash events

Data Sources & Methodology

Primary Data Source

All crash data in this report is sourced from Iowa Crash Data, accessed programmatically via the ArcGIS 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: ArcGIS 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: 2023-01-01 through 2023-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2023-01-01 through 2023-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 1,541
  • Total persons involved: 3,440
  • Total vehicles involved: 2,709

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). "iowa, IA Crash Intelligence Report: 2023." Published September 9, 2026. Reporting period: 2023-01-01 to 2023-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2023-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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