Yearly Traffic Safety Analysis

142 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In 2017, Montgomery County recorded 142 crashes, a slight increase of 1.4% from the 140 crashes reported in 2016. The most significant year-over-year change was the increase in traffic fatalities, which rose from zero in 2016 to three in 2017. These fatalities resulted from two separate fatal crash events.

142

▲ 1.4%was 140

Total Crash Events

3

Persons Killed

33

▼ -13.2%was 38

Persons Injured

2

Fatal Crash Events

Note: "Persons Killed" (3) 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 · 2017-01-01 to 2017-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall crash volume in Montgomery County remained relatively stable, increasing by just two incidents from 140 in 2016 to 142 in 2017. While the total number of crashes was nearly unchanged, the outcomes worsened with three fatalities recorded in 2017 compared to none the previous year. In contrast, the total number of persons injured decreased by 13.2%, from 38 in 2016 to 33 in 2017.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

3

Motorists Killed

Prior: 0→%

1

Cyclists Injured

Prior: 0→%

32

Motorists Injured

Prior: 38↓-15.8%

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes shifted between the two years. In 2017, Friday was the most frequent day for crashes with 32 incidents, a change from 2016 when Wednesday saw the highest volume at 24 crashes. The peak hour for collisions also shifted slightly earlier, moving from the 6 p.m. hour in 2016 (15 crashes) to the 5 p.m. hour in 2017 (19 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity worsened in 2017 compared to the prior year. The county recorded two fatal crashes, accounting for 1.4% of all incidents in 2017, whereas there were no fatal crashes in 2016. The count of serious injury crashes more than doubled, increasing from 3 in 2016 to 7 in 2017. Conversely, crashes resulting in minor or possible injuries saw a combined decrease in their share of total incidents.

Severity is per crash event (most severe injury). 2 fatal crash events resulted in 3 persons killed.

Outcome by Severity (Crash Events)

Fatal2fatal crashes1.4%
Serious Injury7serious injury crashes4.9%
↑133.3%prior 3
Minor Injury7minor injury crashes4.9%
↓-41.7%prior 12
Possible Injury16possible injury crashes11.3%
↓-27.3%prior 22
No Injury110no injury crashes77.5%
↑6.8%prior 103

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving an animal remained the top contributing factor in both periods, with the count of such crashes increasing by 29.2% from 24 in 2016 to 31 in 2017. The factor "Lost Control" moved up in the rankings to become the second-most cited cause in 2017 with 15 incidents, a 66.7% increase in count from 9 incidents the previous year. Crashes attributed to "Followed too close" saw a notable rise, jumping from 4 incidents in 2016 to 14 in 2017.

Officer-Reported Primary Contributing Cause

Animal31 (21.8%)↑29.2%prior 24
Lost Control15 (10.6%)↑66.7%prior 9
Followed too close14 (9.9%)
Ran Stop Sign13 (9.2%)↑18.2%prior 11
FTYROW: From stop sign8 (5.6%)↓-20.0%prior 10
Other (explain in narrative): Other6 (4.2%)↓-25.0%prior 8
Ran off road - straight5 (3.5%)↓-50.0%prior 10
Traveling wrong way or on wrong side of road4 (2.8%)
Driving too fast for conditions4 (2.8%)
Ran off road - left4 (2.8%)↓-42.9%prior 7

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

In both 2016 and 2017, the majority of crashes occurred in clear weather on dry roads. However, there was a notable shift in lighting conditions; crashes on dark, unlighted roadways doubled from 11 in 2016 to 22 in 2017. Incidents on roads with ice or frost also increased from 5 to 9, and crashes on gravel surfaces rose from 2 in 2016 to 9 in 2017.

Weather

Clear77 (67.5%)
↓-2.5%prior 79
Cloudy22 (19.3%)
↓-21.4%prior 28
Rain4 (3.5%)
Blowing Snow3 (2.6%)
Fog, smoke, smog3 (2.6%)
Freezing rain/drizzle3 (2.6%)
Severe Winds1 (0.9%)
Snow1 (0.9%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Weather condition at time of crash

Lighting

Daylight73 (63.5%)
↓-18.9%prior 90
Dark - roadway not lighted22 (19.1%)
↑100.0%prior 11
Dark - roadway lighted13 (11.3%)
→0.0%prior 13
Dusk4 (3.5%)
Dawn2 (1.7%)
Dark - unknown roadway lighting1 (0.9%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Lighting condition field

Road Surface

Dry78 (68.4%)
↓-16.1%prior 93
Wet12 (10.5%)
↑33.3%prior 9
Ice/frost9 (7.9%)
↑80.0%prior 5
Gravel9 (7.9%)
Snow6 (5.3%)
↓-25.0%prior 8

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Road surface condition field

Vehicles & Demographics

The most common vehicle makes involved in crashes remained consistent, with Ford (40 vehicles) and Chevrolet (a combined 49 vehicles) leading in 2017, nearly identical to their counts of 41 and 50 in 2016. Analysis of persons involved shows a shift in age demographics, as the number of individuals aged 65 and older involved in crashes increased from 30 in 2016 to 38 in 2017. There was also a change in the sex distribution of persons involved, with males increasing from 86 to 94 and females decreasing from 73 to 63.

Top Vehicle Makes (218 vehicles)

1
FORD40 (18.3%)
↓-2.4%prior 41
2
CHEV34 (15.6%)
↑36.0%prior 25
3
DODG17 (7.8%)
→0.0%prior 17
4
CHEVROLET15 (6.9%)
↓-40.0%prior 25
5
DODGE10 (4.6%)
↓-16.7%prior 12
6
BUIC9 (4.1%)
7
CHRY9 (4.1%)
↑50.0%prior 6
8
KIA8 (3.7%)
↑60.0%prior 5
9
JEEP7 (3.2%)
↓-12.5%prior 8
10
NISS5 (2.3%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Vehicle unit records

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

Sex Distribution (157 persons with recorded sex)

Male94 (59.9%)
↑9.3%prior 86
Female63 (40.1%)
↓-13.7%prior 73

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · 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: 2017-01-01 through 2017-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2017-01-01 through 2017-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 142
  • Total persons involved: 246
  • Total vehicles involved: 218

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: 2017." Published September 9, 2026. Reporting period: 2017-01-01 to 2017-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2017-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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