Monthly Traffic Safety Analysis

4,131 CRASHES IN
IOWA, IA
MARCH 2018

All metrics benchmarked againstMarch 2017

In March 2018, there were 4,131 total crashes recorded in Iowa, an increase of 7.8% from the 3,831 crashes documented in March 2017. While total crashes rose, the number of fatalities decreased from 23 to 12. One of the most notable year-over-year shifts occurred in contributing factors, where crashes attributed to 'Driving too fast for conditions' more than doubled, increasing from 164 to 350 incidents.

4,131

▲ 7.8%was 3,831

Total Crash Events

12

▼ -47.8%was 23

Persons Killed

1,327

▲ 2.6%was 1,293

Persons Injured

12

▼ -42.9%was 21

Fatal Crash Events

Note: "Persons Killed" (12) counts individual fatalities across all crash events. "Fatal" in the severity table below (12) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2018-03-01 to 2018-03-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash data from March 2018 indicates an upward trend in collision frequency compared to the same month in the prior year. Total crashes increased by 7.8%, rising from 3,831 to 4,131. While the number of injuries saw a slight rise of 2.6% to 1,327, fatalities decreased substantially by 47.8%, from 23 in March 2017 to 12 in March 2018.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 2↓-50.0%

0

Cyclists Killed

Prior: 1↓-100.0%

11

Motorists Killed

Prior: 20↓-45.0%

0

Other Killed

Prior: 0→0.0%

18

Pedestrians Injured

Prior: 36↓-50.0%

11

Cyclists Injured

Prior: 14↓-21.4%

1,295

Motorists Injured

Prior: 1,242↑4.3%

3

Other Injured

Prior: 1↑200.0%

Source: Iowa Crash Data · ArcGIS Open Data · 2018-03-01 to 2018-03-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes showed some shifts between March 2017 and March 2018. While Friday remained the peak day for crashes in both periods (674 in 2017, 721 in 2018), the peak hour for collisions shifted from the 3 p.m. hour in 2017 (305 crashes) to the 5 p.m. hour in 2018 (319 crashes). Notably, Wednesday crashes decreased from 663 to 513, while crashes on Mondays and Tuesdays increased.

Source: Iowa Crash Data · ArcGIS Open Data · 2018-03-01 to 2018-03-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2018-03-01 to 2018-03-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity of crashes generally decreased in March 2018 compared to the previous year. Fatal crashes dropped from 21 to 12, and their share of all crashes fell from 0.5% to 0.3%. The proportion of injury-related crashes also declined across serious and minor categories. Conversely, crashes resulting in no injury increased in both absolute count, from 2,712 to 3,038, and as a percentage of all incidents, from 70.8% to 73.5%.

Outcome by Severity (Crash Events)

Fatal12fatal crashes0.3%
↓-42.9%prior 21
Serious Injury80serious injury crashes1.9%
↓-2.4%prior 82
Minor Injury330minor injury crashes8%
↓-5.2%prior 348
Possible Injury671possible injury crashes16.2%
↑0.4%prior 668
No Injury3,038no injury crashes73.5%
↑12.0%prior 2,712

Source: Iowa Crash Data · ArcGIS Open Data · 2018-03-01 to 2018-03-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-03-01 to 2018-03-31 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factors shifted between March 2017 and March 2018. Collisions involving an 'Animal' became the top-ranked factor, with the count of such incidents increasing by 24.8% from 399 to 498. 'Followed too close' dropped to the second position with a 3.6% decrease in count from 391 to 377. The most significant change was the rise of 'Driving too fast for conditions,' which more than doubled in count from 164 to 350, moving from the 10th-ranked factor in 2017 to the 3rd in 2018.

Officer-Reported Primary Contributing Cause

Animal498 (12.1%)↑24.8%prior 399
Followed too close377 (9.1%)↓-3.6%prior 391
Driving too fast for conditions350 (8.5%)↑113.4%prior 164
Ran off road - left285 (6.9%)↑22.3%prior 233
Lost Control285 (6.9%)↑26.7%prior 225
Other (explain in narrative): Other278 (6.7%)↑56.2%prior 178
FTYROW: From stop sign208 (5%)↑1.0%prior 206
Ran off road - straight194 (4.7%)↑14.8%prior 169
Ran Traffic Signal144 (3.5%)↓-13.3%prior 166
FTYROW: Making left turn139 (3.4%)↓-26.8%prior 190

Source: Iowa Crash Data · ArcGIS Open Data · 2018-03-01 to 2018-03-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The proportion of crashes occurring in adverse road conditions increased year-over-year. Crashes on dry road surfaces accounted for 58.9% of incidents in March 2018, down from 65.4% in March 2017. Concurrently, the share of crashes on wet, snowy, icy, or slushy roads increased from 23.7% to 29.4%. While clear weather was the most common condition in both periods, the number of crashes in snowy weather increased from 226 to 385.

Weather

Clear2,047 (55.3%)
↑17.7%prior 1,739
Cloudy710 (19.2%)
↓-36.9%prior 1,126
Snow385 (10.4%)
↑70.4%prior 226
Freezing rain/drizzle241 (6.5%)
↑487.8%prior 41
Rain154 (4.2%)
↓-43.6%prior 273
Blowing Snow67 (1.8%)
↑103.0%prior 33
Sleet, hail53 (1.4%)
Fog, smoke, smog27 (0.7%)
↑35.0%prior 20
Severe Winds9 (0.2%)
↓-40.0%prior 15
Other (explain in narrative)5 (0.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-03-01 to 2018-03-31 · Weather condition at time of crash

Lighting

Daylight2,580 (69.4%)
↑8.9%prior 2,370
Dark - roadway lighted489 (13.1%)
↓-6.5%prior 523
Dark - roadway not lighted449 (12.1%)
↑5.6%prior 425
Dusk97 (2.6%)
↑36.6%prior 71
Dawn86 (2.3%)
↑19.4%prior 72
Dark - unknown roadway lighting18 (0.5%)
↓-10.0%prior 20

Source: Iowa Crash Data · ArcGIS Open Data · 2018-03-01 to 2018-03-31 · Lighting condition field

Road Surface

Dry2,434 (65.5%)
↓-2.9%prior 2,507
Wet455 (12.3%)
↓-17.9%prior 554
Snow314 (8.5%)
↑23.1%prior 255
Ice/frost239 (6.4%)
↑231.9%prior 72
Slush208 (5.6%)
↑732.0%prior 25
Gravel40 (1.1%)
↓-27.3%prior 55
Mud, dirt11 (0.3%)
↑37.5%prior 8
Sand10 (0.3%)
↑66.7%prior 6
Other (explain in narrative)2 (0.1%)
Oil1 (0.0%)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-03-01 to 2018-03-31 · Road surface condition field

Vehicles & Demographics

Top Vehicle Makes (7,011 vehicles)

1
FORD1,222 (17.4%)
↑18.8%prior 1,029
2
CHEV910 (13%)
↑26.9%prior 717
3
CHEVROLET447 (6.4%)
↓-28.8%prior 628
4
TOYT343 (4.9%)
↑28.9%prior 266
5
DODG286 (4.1%)
↑26.0%prior 227
6
JEEP244 (3.5%)
↑2.1%prior 239
7
HOND220 (3.1%)
↑28.7%prior 171
8
GMC203 (2.9%)
↑8.6%prior 187
9
DODGE195 (2.8%)
↓-6.7%prior 209
10
NR186 (2.7%)
↑2.8%prior 181

Source: Iowa Crash Data · ArcGIS Open Data · 2018-03-01 to 2018-03-31 · Vehicle unit records

1,257 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (6,154 persons with recorded sex)

Male3,350 (54.4%)
↑3.0%prior 3,254
Female2,804 (45.6%)
↑7.6%prior 2,606

Source: Iowa Crash Data · ArcGIS Open Data · 2018-03-01 to 2018-03-31 · 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: 2018-03-01 through 2018-03-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2018-03-01 through 2018-03-31 (31 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 4,131
  • Total persons involved: 9,323
  • Total vehicles involved: 7,011

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: March 2018." Published September 9, 2026. Reporting period: 2018-03-01 to 2018-03-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/march-2018-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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