Yearly Traffic Safety Analysis

1,366 CRASHES IN
IOWA, IA
2024

All metrics benchmarked against2023

In 2024, Dallas County recorded 1,366 traffic crashes, a 2.2% increase from the 1,337 crashes reported in 2023. Despite the slight rise in total collisions, the number of fatalities saw a substantial decrease, falling from 14 in the prior year to 2 in the current year.

1,366

▲ 2.2%was 1,337

Total Crash Events

2

▼ -85.7%was 14

Persons Killed

445

▼ -6.9%was 478

Persons Injured

2

▼ -83.3%was 12

Fatal Crash Events

Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall traffic crashes in Dallas County showed a slight increase, rising by 2.2% from 1,337 in 2023 to 1,366 in 2024. However, the outcomes of these crashes improved, with total injuries decreasing by 6.9% from 478 to 445 and fatalities dropping from 14 to 2.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 14↓-85.7%

0

Other Killed

Prior: 0→0.0%

3

Pedestrians Injured

Prior: 2↑50.0%

6

Cyclists Injured

Prior: 8↓-25.0%

434

Motorists Injured

Prior: 464↓-6.5%

2

Other Injured

Prior: 4↓-50.0%

Source: Iowa Crash Data · ArcGIS 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 remained largely consistent year-over-year. Friday was the peak day for crashes in both 2024 (233 crashes) and 2023 (229 crashes), and the 3 p.m. hour was the peak hour in both periods, with 113 and 114 crashes, respectively. A notable shift occurred in the monthly distribution, with January 2024 becoming the month with the most crashes (172), a significant increase from 114 crashes in January of the prior year.

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity decreased significantly in 2024 compared to the previous year. The number of fatal crashes dropped from 12 to 2, and the proportion of crashes resulting in any level of injury (Serious, Minor, or Possible) fell from 27.1% in 2023 to 24.3% in 2024. Consequently, the share of crashes with no injuries increased from 72.0% to 75.6% of all incidents.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.1%
↓-83.3%prior 12
Serious Injury24serious injury crashes1.8%
↑4.3%prior 23
Minor Injury125minor injury crashes9.2%
↓-10.1%prior 139
Possible Injury182possible injury crashes13.3%
↓-9.0%prior 200
No Injury1,033no injury crashes75.6%
↑7.3%prior 963

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factors for crashes shifted between the two periods. In 2024, "Followed too close" became the most cited factor with 181 incidents, an increase from 164 in 2023, overtaking "Animal" which decreased from 182 to 165 incidents. Crashes attributed to "Driving too fast for conditions" increased from 69 to 95, and those involving a vehicle running off the road to the left rose from 51 to 70.

Officer-Reported Primary Contributing Cause

Followed too close181 (13.3%)↑10.4%prior 164
Animal165 (12.1%)↓-9.3%prior 182
Driving too fast for conditions95 (7%)↑37.7%prior 69
Other (explain in narrative): Other94 (6.9%)↑9.3%prior 86
FTYROW: From stop sign74 (5.4%)↑1.4%prior 73
Ran off road - left70 (5.1%)↑37.3%prior 51
FTYROW: Making left turn67 (4.9%)↓-4.3%prior 70
Ran Traffic Signal52 (3.8%)↑13.0%prior 46
Driver Distraction: Other interior distraction52 (3.8%)↓-1.9%prior 53
Improper or erratic lane changing39 (2.9%)↑56.0%prior 25

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The conditions under which crashes occurred were broadly similar year-over-year, with most incidents happening in daylight (65.2% in 2024 vs. 63.5% in 2023) and clear weather (66.2% vs. 67.2%). However, there was a noticeable increase in crashes on adverse road surfaces. Collisions on wet roads rose from 97 to 146, and crashes on icy or frosty roads more than doubled, increasing from 24 to 52 incidents.

Weather

Clear904 (74.1%)
↑0.7%prior 898
Cloudy187 (15.3%)
↑8.1%prior 173
Rain60 (4.9%)
↑30.4%prior 46
Snow34 (2.8%)
↓-29.2%prior 48
Blowing Snow15 (1.2%)
↑114.3%prior 7
Fog, smoke, smog10 (0.8%)
↑25.0%prior 8
Freezing rain/drizzle7 (0.6%)
↑16.7%prior 6
Severe Winds2 (0.2%)
Other (explain in narrative)1 (0.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Weather condition at time of crash

Lighting

Daylight891 (72.3%)
↑4.9%prior 849
Dark - roadway lighted167 (13.5%)
↑3.7%prior 161
Dark - roadway not lighted104 (8.4%)
↓-9.6%prior 115
Dusk32 (2.6%)
↓-23.8%prior 42
Dawn25 (2.0%)
↑8.7%prior 23
Dark - unknown roadway lighting14 (1.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Lighting condition field

Road Surface

Dry933 (76.4%)
↓-4.3%prior 975
Wet146 (12.0%)
↑50.5%prior 97
Snow62 (5.1%)
↑12.7%prior 55
Ice/frost52 (4.3%)
↑116.7%prior 24
Gravel17 (1.4%)
↓-39.3%prior 28
Slush10 (0.8%)
↑11.1%prior 9
Mud, dirt1 (0.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Road surface condition field

Vehicles & Demographics

The most common vehicle makes involved in crashes remained consistent year-over-year. After combining abbreviated and full names, Chevrolet (412 vehicles) and Ford (369 vehicles) were the top two makes in 2024, holding similar positions to 2023 (415 and 339 vehicles, respectively). The age distribution of individuals involved in crashes also showed little change, with the 35-44 age group representing the largest cohort in 2024 at 425 individuals, similar to its position in the prior year.

Top Vehicle Makes (2,443 vehicles)

1
FORD369 (15.1%)
↑8.8%prior 339
2
CHEV309 (12.6%)
↑0.7%prior 307
3
TOYT150 (6.1%)
↓-2.0%prior 153
4
HOND144 (5.9%)
↑14.3%prior 126
5
JEEP136 (5.6%)
↑27.1%prior 107
6
NISS113 (4.6%)
↑11.9%prior 101
7
CHEVROLET103 (4.2%)
↓-4.6%prior 108
8
KIA73 (3%)
→0.0%prior 73
9
HYUN70 (2.9%)
↑45.8%prior 48
10
DODG62 (2.5%)
↓-13.9%prior 72

Source: Iowa Crash Data · ArcGIS Open Data · 2024-01-01 to 2024-12-31 · Vehicle unit records

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

Sex Distribution (1,732 persons with recorded sex)

Male959 (55.4%)
↓-24.0%prior 1,262
Female773 (44.6%)
↓-20.9%prior 977

Source: Iowa Crash Data · ArcGIS 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 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: 2024-01-01 through 2024-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2024-01-01 through 2024-12-31 (366 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 1,366
  • Total persons involved: 2,527
  • Total vehicles involved: 2,443

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: 2024." Published September 9, 2026. Reporting period: 2024-01-01 to 2024-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/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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