Yearly Traffic Safety Analysis

112 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In 2020, Montgomery County recorded 112 total crashes, a 22.8% decrease from the 145 crashes reported in 2019. Despite the overall reduction in collisions, the most significant change was the occurrence of 3 fatalities in 2020, following a year with zero fatalities.

112

▼ -22.8%was 145

Total Crash Events

3

Persons Killed

36

▼ -5.3%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 · 2020-01-01 to 2020-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash trends in Montgomery County showed a significant year-over-year decline, with total collisions falling from 145 in 2019 to 112 in 2020. This represents a 22.8% decrease in crash volume. However, while total injuries remained relatively stable (38 in 2019 vs. 36 in 2020), the number of fatalities rose from zero to three.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

3

Motorists Killed

Prior: 0→%

1

Pedestrians Injured

Prior: 0→%

35

Motorists Injured

Prior: 38↓-7.9%

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The timing of crashes shifted between the two periods. In 2020, the peak day for crashes was Monday with 23 incidents, a change from 2019 when Wednesday was the peak day with 33 incidents. The peak hour for collisions remained consistent year-over-year, with the 5 PM hour recording the highest number of crashes in both 2020 and 2019, with 15 crashes each year.

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While total crashes decreased, the severity of outcomes worsened in 2020. The county recorded 2 fatal crashes resulting in 3 fatalities, compared to zero fatal crashes in 2019. The share of crashes involving any level of injury increased from 16.6% of all crashes in 2019 to 24.1% in 2020. Consequently, the proportion of crashes resulting in no injuries decreased from 83.4% to 74.1%.

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

Outcome by Severity (Crash Events)

Fatal2fatal crashes1.8%
Serious Injury5serious injury crashes4.5%
↑25.0%prior 4
Minor Injury11minor injury crashes9.8%
↑57.1%prior 7
Possible Injury11possible injury crashes9.8%
↓-15.4%prior 13
No Injury83no injury crashes74.1%
↓-31.4%prior 121

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving animals remained the top contributing factor in both periods, though the count decreased from 41 crashes in 2019 to 28 in 2020. 'Lost Control' emerged as a more prominent factor in 2020, with its count increasing from 7 crashes in 2019 to 13. Meanwhile, factors like 'Ran off road - straight' and 'Driving too fast for conditions' saw their crash counts decrease from 8 incidents each in 2019 to 4 and 6 incidents, respectively, in 2020.

Officer-Reported Primary Contributing Cause

Animal28 (25%)↓-31.7%prior 41
Lost Control13 (11.6%)↑85.7%prior 7
Driving too fast for conditions6 (5.4%)↓-25.0%prior 8
Other (explain in narrative): Other5 (4.5%)↓-37.5%prior 8
FTYROW: From stop sign5 (4.5%)↓-28.6%prior 7
Exceeded authorized speed4 (3.6%)
Ran Stop Sign4 (3.6%)↓-33.3%prior 6
Driver Distraction: Other interior distraction4 (3.6%)
Ran off road - straight4 (3.6%)↓-50.0%prior 8
Followed too close3 (2.7%)↓-57.1%prior 7

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes in both years predominantly occurred in clear weather and daylight on dry roads. The proportion of crashes under these ideal conditions increased in 2020, with 59.8% of incidents occurring on dry roads compared to 48.3% in 2019. Conversely, crashes on adverse road surfaces saw a notable decrease, with the number of collisions on snow or ice-covered roads falling from 23 in 2019 to 10 in 2020.

Weather

Clear57 (61.3%)
↓-3.4%prior 59
Cloudy22 (23.7%)
↓-42.1%prior 38
Snow6 (6.5%)
→0.0%prior 6
Rain4 (4.3%)
↓-20.0%prior 5
Blowing Snow1 (1.1%)
Fog, smoke, smog1 (1.1%)
Severe Winds1 (1.1%)
Blowing sand, soil, dirt1 (1.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Weather condition at time of crash

Lighting

Daylight68 (73.1%)
↓-10.5%prior 76
Dark - roadway not lighted11 (11.8%)
↓-42.1%prior 19
Dark - roadway lighted10 (10.8%)
↓-16.7%prior 12
Dawn3 (3.2%)
Dark - unknown roadway lighting1 (1.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Lighting condition field

Road Surface

Dry67 (72.8%)
↓-4.3%prior 70
Snow8 (8.7%)
↓-42.9%prior 14
Wet7 (7.6%)
↓-53.3%prior 15
Gravel6 (6.5%)
Slush2 (2.2%)
Ice/frost2 (2.2%)
↓-77.8%prior 9

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Road surface condition field

Vehicles & Demographics

Ford and Chevrolet were the most common vehicle makes involved in crashes for both years. In 2020, Fords were involved in 34 crashes (down from 40) and Chevrolets were in 37 (down from 45). An analysis of persons involved shows a notable decrease in the 16-20 age group, which accounted for 29 individuals in 2020 compared to 44 in 2019. The 26-34 age group was the most represented in 2020, with 39 individuals involved in collisions.

Top Vehicle Makes (168 vehicles)

1
FORD34 (20.2%)
↓-15.0%prior 40
2
CHEV26 (15.5%)
↓-25.7%prior 35
3
CHEVROLET11 (6.5%)
↑10.0%prior 10
4
DODGE11 (6.5%)
↑10.0%prior 10
5
NISS10 (6%)
6
JEEP9 (5.4%)
↓-18.2%prior 11
7
DODG7 (4.2%)
↓-36.4%prior 11
8
CHRY5 (3%)
↓-28.6%prior 7
9
KIA3 (1.8%)
10
DEER3 (1.8%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Vehicle unit records

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

Sex Distribution (150 persons with recorded sex)

Male91 (60.7%)
↓-20.2%prior 114
Female59 (39.3%)
↓-15.7%prior 70

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · 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: 2020-01-01 through 2020-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2020-01-01 through 2020-12-31 (366 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 112
  • Total persons involved: 240
  • Total vehicles involved: 168

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: 2020." Published September 9, 2026. Reporting period: 2020-01-01 to 2020-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2020-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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