Yearly Traffic Safety Analysis

1,374 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In Dallas County, total traffic crashes increased by 22.0% from 1,126 in 2017 to 1,374 in 2018. This rise was accompanied by a 15.3% increase in injuries, from 372 to 429. The most significant year-over-year change was the number of fatalities, which tripled from 2 in 2017 to 6 in 2018.

1,374

▲ 22.0%was 1,126

Total Crash Events

6

▲ 200.0%was 2

Persons Killed

429

▲ 15.3%was 372

Persons Injured

6

▲ 200.0%was 2

Fatal Crash Events

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: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Traffic safety trends in Dallas County worsened from 2017 to 2018. Total crashes rose by 22.0%, from 1,126 to 1,374. This upward trend was also observed in crash-related injuries, which increased by 15.3% to 429, and fatalities, which rose from 2 to 6.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 0→%

0

Cyclists Killed

Prior: 0→0.0%

5

Motorists Killed

Prior: 2↑150.0%

0

Pedestrians Injured

Prior: 5↓-100.0%

6

Cyclists Injured

Prior: 9↓-33.3%

423

Motorists Injured

Prior: 358↑18.2%

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes showed some shifts between the two periods. Friday remained the day with the most crashes, with the count increasing from 204 in 2017 to 261 in 2018. The daily peak hour for crashes shifted slightly earlier, from 5 p.m. in 2017 (119 crashes) to 4 p.m. in 2018 (125 crashes), though the afternoon commute remains the highest-frequency period for collisions.

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity increased from 2017 to 2018. The number of fatal crashes tripled from 2 to 6, and the fatal crash rate as a percentage of all crashes doubled from 0.2% to 0.4%. While the count of serious injury crashes decreased slightly from 19 to 16, crashes resulting in minor injuries (106 to 120) and possible injuries (149 to 198) both increased. The proportion of crashes involving no injuries remained stable at approximately 75% for both years.

Outcome by Severity (Crash Events)

Fatal6fatal crashes0.4%
↑200.0%prior 2
Serious Injury16serious injury crashes1.2%
↓-15.8%prior 19
Minor Injury120minor injury crashes8.7%
↑13.2%prior 106
Possible Injury198possible injury crashes14.4%
↑32.9%prior 149
No Injury1,034no injury crashes75.3%
↑21.6%prior 850

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factors for crashes were consistent year-over-year, with collisions involving an animal and following too closely ranking as the top two causes in both periods. However, the count of crashes attributed to following too closely grew by 27.8%, from 158 incidents in 2017 to 202 in 2018. Incidents involving 'Driving too fast for conditions' increased by 60% (from 60 to 96), and crashes where a driver 'Lost Control' rose by 58% (from 50 to 79).

Officer-Reported Primary Contributing Cause

Animal203 (14.8%)↑3.0%prior 197
Followed too close202 (14.7%)↑27.8%prior 158
Other (explain in narrative): Other112 (8.2%)↑5.7%prior 106
Driving too fast for conditions96 (7%)↑60.0%prior 60
Lost Control79 (5.7%)↑58.0%prior 50
FTYROW: From stop sign68 (4.9%)↑30.8%prior 52
FTYROW: Making left turn62 (4.5%)↑24.0%prior 50
Ran off road - left60 (4.4%)↑57.9%prior 38
Ran off road - straight59 (4.3%)↑15.7%prior 51
Driver Distraction: Other interior distraction55 (4%)↑10.0%prior 50

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

While most crashes in both years occurred during daylight in clear weather, there was a significant increase in crashes under adverse road conditions. The number of collisions on road surfaces with snow, ice, or slush more than doubled, rising from 79 in 2017 to 199 in 2018. This corresponds with an increase in crashes reported during snow (from 41 to 75) and freezing rain (from 11 to 47), indicating a greater number of weather-related incidents in 2018.

Weather

Clear739 (61.4%)
↑14.8%prior 644
Cloudy253 (21.0%)
↑33.2%prior 190
Snow75 (6.2%)
↑82.9%prior 41
Rain70 (5.8%)
↑9.4%prior 64
Freezing rain/drizzle47 (3.9%)
↑327.3%prior 11
Blowing Snow10 (0.8%)
↑100.0%prior 5
Fog, smoke, smog5 (0.4%)
↓-37.5%prior 8
Severe Winds2 (0.2%)
Other (explain in narrative)2 (0.2%)
Sleet, hail1 (0.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Weather condition at time of crash

Lighting

Daylight862 (71.7%)
↑31.0%prior 658
Dark - roadway lighted149 (12.4%)
↑0.7%prior 148
Dark - roadway not lighted128 (10.6%)
↑25.5%prior 102
Dusk32 (2.7%)
→0.0%prior 32
Dawn31 (2.6%)
↑10.7%prior 28
Dark - unknown roadway lighting1 (0.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Lighting condition field

Road Surface

Dry827 (68.6%)
↑10.6%prior 748
Wet146 (12.1%)
↑29.2%prior 113
Snow110 (9.1%)
↑134.0%prior 47
Ice/frost69 (5.7%)
↑122.6%prior 31
Gravel27 (2.2%)
↓-6.9%prior 29
Slush20 (1.7%)
Other (explain in narrative)3 (0.2%)
Mud, dirt2 (0.2%)
Water (standing or moving)1 (0.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Road surface condition field

Vehicles & Demographics

The top vehicle makes involved in crashes remained consistent, with Ford and Chevrolet vehicles being the most prevalent in both 2017 and 2018. The number of Fords involved in crashes increased from 248 to 369, and the combined count for Chevrolet vehicles rose from 347 to 450. An analysis of persons involved in crashes shows increases across all reported age groups, with a notable rise in the 16-20 age group (from 336 to 434) and the 21-25 age group (from 204 to 284).

Top Vehicle Makes (2,394 vehicles)

1
FORD369 (15.4%)
↑48.8%prior 248
2
CHEV330 (13.8%)
↑38.1%prior 239
3
TOYT161 (6.7%)
↑45.0%prior 111
4
CHEVROLET120 (5%)
↑11.1%prior 108
5
HOND118 (4.9%)
→0.0%prior 118
6
DODG96 (4%)
↑29.7%prior 74
7
NISS94 (3.9%)
↑30.6%prior 72
8
JEEP89 (3.7%)
↑23.6%prior 72
9
TOYOTA57 (2.4%)
↓-3.4%prior 59
10
GMC54 (2.3%)
↑42.1%prior 38

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Vehicle unit records

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

Sex Distribution (2,021 persons with recorded sex)

Male1,094 (54.1%)
↑30.2%prior 840
Female927 (45.9%)
↑24.9%prior 742

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · 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: 2018-01-01 through 2018-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2018-01-01 through 2018-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 1,374
  • Total persons involved: 2,802
  • Total vehicles involved: 2,394

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: 2018." Published September 9, 2026. Reporting period: 2018-01-01 to 2018-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2018-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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