Yearly Traffic Safety Analysis

704 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In Jasper County, total traffic crashes increased by 21.0% from 582 in 2017 to 704 in 2018. While the number of fatalities remained unchanged at four, the number of serious injury crashes doubled from 11 to 22. The most notable shift was the significant increase in crashes occurring on non-dry road surfaces and during adverse weather conditions.

704

▲ 21.0%was 582

Total Crash Events

4

Persons Killed

207

▲ 2.5%was 202

Persons Injured

4

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 · 2018-01-01 to 2018-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Traffic crashes in Jasper County showed a rising trend, with total collisions increasing from 582 in 2017 to 704 in 2018. This 21.0% increase in crash volume was accompanied by a slight 2.5% rise in total injuries, from 202 to 207. The number of fatalities was stable year-over-year at four.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

4

Motorists Killed

Prior: 4→0.0%

2

Pedestrians Injured

Prior: 3↓-33.3%

4

Cyclists Injured

Prior: 3↑33.3%

201

Motorists Injured

Prior: 196↑2.6%

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 peak hour for crashes remained consistent at 5 p.m. in both 2017 and 2018. However, the peak day for collisions shifted from Friday (104 crashes) in 2017 to Saturday (124 crashes) in 2018. The number of crashes occurring on Saturdays more than doubled, increasing from 61 in the prior year to 124 in the current year.

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

While the number of fatal crashes remained constant at four, the fatal crash rate per 100 collisions decreased from 0.69 to 0.57 due to the overall increase in crash volume. A significant change occurred in serious injury crashes, which doubled in number from 11 to 22, increasing their share of all crashes from 1.9% to 3.1%. The count of no-injury crashes also rose from 423 to 531, making up 75.4% of all incidents in 2018 compared to 72.7% in 2017.

Outcome by Severity (Crash Events)

Fatal4fatal crashes0.6%
→0.0%prior 4
Serious Injury22serious injury crashes3.1%
↑100.0%prior 11
Minor Injury62minor injury crashes8.8%
↓-4.6%prior 65
Possible Injury85possible injury crashes12.1%
↑7.6%prior 79
No Injury531no injury crashes75.4%
↑25.5%prior 423

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

Collisions involving an animal remained the leading contributing factor in both periods, with the count increasing by 16.4% from 134 to 156 crashes. 'Ran off road - straight' moved from the third to the second-ranked factor, with its count rising 34.0% from 53 to 71. 'Driving too fast for conditions' also saw a notable increase in volume, growing by 36.6% from 41 crashes in 2017 to 56 crashes in 2018.

Officer-Reported Primary Contributing Cause

Animal156 (22.2%)↑16.4%prior 134
Ran off road - straight71 (10.1%)↑34.0%prior 53
Lost Control62 (8.8%)↑3.3%prior 60
Driving too fast for conditions56 (8%)↑36.6%prior 41
Other (explain in narrative): Other47 (6.7%)↑74.1%prior 27
FTYROW: From stop sign37 (5.3%)↑15.6%prior 32
Followed too close33 (4.7%)↑10.0%prior 30
Ran off road - left32 (4.5%)↑14.3%prior 28
Ran Stop Sign18 (2.6%)↓-18.2%prior 22
Driver Distraction: Inattentive/lost in thought17 (2.4%)↑54.5%prior 11

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

Road & Environmental Conditions

The proportion of crashes on non-dry road surfaces increased substantially, from 15.5% of all crashes in 2017 to 27.4% in 2018. This was driven by a rise in crashes on wet surfaces (from 31 to 83) and snow-covered roads (from 28 to 64). Similarly, the share of crashes occurring in adverse weather conditions like snow or rain grew from 10.0% to 17.8% of the total. The distribution of crashes by lighting conditions remained stable.

Weather

Clear353 (59.4%)
↑5.4%prior 335
Cloudy110 (18.5%)
↑26.4%prior 87
Snow57 (9.6%)
↑103.6%prior 28
Rain41 (6.9%)
↑173.3%prior 15
Blowing Snow11 (1.9%)
↑120.0%prior 5
Freezing rain/drizzle11 (1.9%)
↑22.2%prior 9
Sleet, hail5 (0.8%)
Other (explain in narrative)3 (0.5%)
Fog, smoke, smog3 (0.5%)
↓-66.7%prior 9

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

Lighting

Daylight381 (64.0%)
↑19.4%prior 319
Dark - roadway not lighted148 (24.9%)
↑28.7%prior 115
Dark - roadway lighted25 (4.2%)
↓-19.4%prior 31
Dawn20 (3.4%)
↑81.8%prior 11
Dusk13 (2.2%)
→0.0%prior 13
Dark - unknown roadway lighting8 (1.3%)

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

Road Surface

Dry380 (63.9%)
↑4.1%prior 365
Wet83 (13.9%)
↑167.7%prior 31
Snow64 (10.8%)
↑128.6%prior 28
Ice/frost36 (6.1%)
↑20.0%prior 30
Gravel18 (3.0%)
↓-48.6%prior 35
Slush10 (1.7%)
Mud, dirt2 (0.3%)
Sand1 (0.2%)
Other (explain in narrative)1 (0.2%)

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

Vehicles & Demographics

Ford and Chevrolet remained the top two vehicle makes involved in crashes, with both seeing an increase in their total counts year-over-year. Demographically, there was a notable increase in the number of persons aged 21-25 involved in crashes, which rose from 87 to 142. The 26-34 age group also saw a significant increase in involvement, from 137 to 226 persons.

Top Vehicle Makes (1,027 vehicles)

1
FORD183 (17.8%)
↑28.0%prior 143
2
CHEV117 (11.4%)
↑28.6%prior 91
3
DODG56 (5.5%)
↑27.3%prior 44
4
CHEVROLET53 (5.2%)
↓-3.6%prior 55
5
FREIGHTLINER36 (3.5%)
↑89.5%prior 19
6
GMC33 (3.2%)
↑65.0%prior 20
7
HOND32 (3.1%)
↑52.4%prior 21
8
TOYT29 (2.8%)
↑61.1%prior 18
9
JEEP28 (2.7%)
↓-6.7%prior 30
10
CHRY26 (2.5%)
↑44.4%prior 18

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

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

Sex Distribution (803 persons with recorded sex)

Male493 (61.4%)
↑27.7%prior 386
Female310 (38.6%)
↑20.6%prior 257

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: 704
  • Total persons involved: 1,237
  • Total vehicles involved: 1,027

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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