Monthly Traffic Safety Analysis

3,738 CRASHES IN
IOWA, IA
MARCH 2016

All metrics benchmarked againstMarch 2015

In March 2016, there were 3,738 total crashes across Iowa, a 1.6% increase from the 3,679 crashes recorded in March 2015. While total injuries saw a slight decrease of 1.8%, the number of fatalities rose from 19 to 25 year-over-year. This represents a 31.6% increase in traffic deaths compared to the same period in the prior year.

3,738

1.6%was 3,679

Total Crash Events

25

31.6%was 19

Persons Killed

1,276

-1.8%was 1,300

Persons Injured

22

15.8%was 19

Fatal Crash Events

Note: "Persons Killed" (25) counts individual fatalities across all crash events. "Fatal" in the severity table below (22) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2016-03-01 to 2016-03-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash totals remained relatively stable, with a slight year-over-year increase of 1.6%, from 3,679 in March 2015 to 3,738 in March 2016. However, the severity of these incidents worsened, as fatalities increased by 31.6% from 19 to 25, while total injuries decreased by 1.8% from 1,300 to 1,276.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 10.0%

1

Cyclists Killed

Prior: 0%

23

Motorists Killed

Prior: 1827.8%

0

Other Killed

Prior: 00.0%

27

Pedestrians Injured

Prior: 28-3.6%

21

Cyclists Injured

Prior: 22-4.5%

1,227

Motorists Injured

Prior: 1,248-1.7%

1

Other Injured

Prior: 2-50.0%

Source: Iowa Crash Data · ArcGIS Open Data · 2016-03-01 to 2016-03-31 · 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 2015 and March 2016. While Tuesday remained the peak day for crashes in both periods (714 and 648 crashes, respectively), the peak hour for collisions moved later in the day. In March 2015, the most crashes occurred during the 3 p.m. hour with 308 incidents, whereas in March 2016, the peak shifted to the 5 p.m. hour with 295 crashes.

Source: Iowa Crash Data · ArcGIS Open Data · 2016-03-01 to 2016-03-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2016-03-01 to 2016-03-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity of crashes increased from March 2015 to March 2016. The number of fatal crashes rose from 19 to 22, and the fatal crash rate increased from 0.52 to 0.59. While the proportion of serious injury crashes decreased slightly from 2.3% to 2.0% of all incidents, crashes resulting in minor or possible injuries saw a marginal increase in their share of the total.

Severity is per crash event (most severe injury). 22 fatal crash events resulted in 25 persons killed.

Outcome by Severity (Crash Events)

Fatal22fatal crashes0.6%
15.8%prior 19
Serious Injury76serious injury crashes2%
-9.5%prior 84
Minor Injury319minor injury crashes8.5%
3.6%prior 308
Possible Injury667possible injury crashes17.8%
2.5%prior 651
No Injury2,654no injury crashes71%
1.4%prior 2,617

Source: Iowa Crash Data · ArcGIS Open Data · 2016-03-01 to 2016-03-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-03-01 to 2016-03-31 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factors shifted between the two periods. In March 2016, 'Followed too close' became the primary factor with 418 incidents, an 11.2% increase in count from 376 the previous year. 'Animal' involvement, which was the top factor in March 2015 with 431 crashes, saw a 23.0% decrease in count to 332 incidents, making it the second-leading factor in the current period. Crashes attributed to 'Lost Control' also decreased in count from 266 to 227.

Officer-Reported Primary Contributing Cause

Followed too close418 (11.2%)11.2%prior 376
Animal332 (8.9%)-23.0%prior 431
Lost Control227 (6.1%)-14.7%prior 266
Ran off road - left226 (6%)9.7%prior 206
Other (explain in narrative): Other224 (6%)6.7%prior 210
FTYROW: From stop sign208 (5.6%)-2.8%prior 214
Driving too fast for conditions178 (4.8%)38.0%prior 129
FTYROW: Making left turn175 (4.7%)1.7%prior 172
Ran off road - straight163 (4.4%)10.1%prior 148
Ran Traffic Signal143 (3.8%)-0.7%prior 144

Source: Iowa Crash Data · ArcGIS Open Data · 2016-03-01 to 2016-03-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Driving conditions in March 2016 were notably more adverse compared to the previous year, which corresponded with shifts in crash patterns. The proportion of crashes occurring in clear weather dropped from 66.6% to 50.4% of all incidents, while crashes in cloudy conditions increased their share from 15.1% to 28.1%. Similarly, incidents on wet roads accounted for 14.3% of all crashes, a significant increase from 4.8% in March 2015, while the share of crashes on dry roads decreased from 75.9% to 68.5%.

Weather

Clear1,884 (54.7%)
-23.1%prior 2,451
Cloudy1,051 (30.5%)
89.7%prior 554
Rain243 (7.1%)
376.5%prior 51
Snow110 (3.2%)
205.6%prior 36
Freezing rain/drizzle63 (1.8%)
-60.4%prior 159
Sleet, hail27 (0.8%)
107.7%prior 13
Fog, smoke, smog26 (0.8%)
-31.6%prior 38
Blowing Snow21 (0.6%)
200.0%prior 7
Severe Winds13 (0.4%)
116.7%prior 6
Other (explain in narrative)4 (0.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2016-03-01 to 2016-03-31 · Weather condition at time of crash

Lighting

Daylight2,461 (71.3%)
2.2%prior 2,407
Dark - roadway lighted458 (13.3%)
15.7%prior 396
Dark - roadway not lighted360 (10.4%)
-0.8%prior 363
Dusk89 (2.6%)
27.1%prior 70
Dawn65 (1.9%)
-13.3%prior 75
Dark - unknown roadway lighting19 (0.6%)
11.8%prior 17

Source: Iowa Crash Data · ArcGIS Open Data · 2016-03-01 to 2016-03-31 · Lighting condition field

Road Surface

Dry2,559 (74.2%)
-8.3%prior 2,792
Wet533 (15.4%)
201.1%prior 177
Snow116 (3.4%)
107.1%prior 56
Ice/frost99 (2.9%)
-49.7%prior 197
Gravel69 (2.0%)
-1.4%prior 70
Slush58 (1.7%)
132.0%prior 25
Mud, dirt8 (0.2%)
33.3%prior 6
Other (explain in narrative)4 (0.1%)
Sand4 (0.1%)
-55.6%prior 9

Source: Iowa Crash Data · ArcGIS Open Data · 2016-03-01 to 2016-03-31 · Road surface condition field

Vehicles & Demographics

The demographic data for vehicles and persons involved in crashes remained largely consistent year-over-year. The top vehicle makes involved in collisions were Ford, Chevrolet, and Dodge in both March 2015 and March 2016, with no significant changes in their rankings or total counts. Similarly, the age distribution of individuals involved in crashes showed little variation, with the 26-34 age group representing the largest cohort in both periods, followed by the 16-20 age group.

Top Vehicle Makes (6,567 vehicles)

1
FORD1,027 (15.6%)
-0.5%prior 1,032
2
CHEV737 (11.2%)
-6.8%prior 791
3
CHEVROLET665 (10.1%)
31.4%prior 506
4
TOYT264 (4%)
-9.3%prior 291
5
DODG245 (3.7%)
-10.9%prior 275
6
DODGE235 (3.6%)
26.3%prior 186
7
HOND196 (3%)
5.9%prior 185
8
GMC192 (2.9%)
7.3%prior 179
9
TOYOTA187 (2.8%)
20.6%prior 155
10
JEEP178 (2.7%)
-14.8%prior 209

Source: Iowa Crash Data · ArcGIS Open Data · 2016-03-01 to 2016-03-31 · Vehicle unit records

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

Sex Distribution (5,262 persons with recorded sex)

Male2,869 (54.5%)
-5.6%prior 3,039
Female2,393 (45.5%)
-2.6%prior 2,458

Source: Iowa Crash Data · ArcGIS Open Data · 2016-03-01 to 2016-03-31 · 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: 2016-03-01 through 2016-03-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2016-03-01 through 2016-03-31 (31 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 3,738
  • Total persons involved: 7,741
  • Total vehicles involved: 6,567

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 2016." Published September 9, 2026. Reporting period: 2016-03-01 to 2016-03-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/march-2016-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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