Yearly Traffic Safety Analysis

145 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In Montgomery County, total crashes decreased from 167 in 2018 to 145 in 2019, a 13.2% reduction. During this period, the number of injuries also saw a significant decline, falling by 30.9% from 55 to 38. Notably, the count of serious injury crashes was halved, decreasing from 8 in the prior year to 4 in the current year.

145

▼ -13.2%was 167

Total Crash Events

0

Persons Killed

38

▼ -30.9%was 55

Persons Injured

0

Fatal Crash Events

Note: "Persons Killed" (0) counts individual fatalities across all crash events. "Fatal" in the severity table below (0) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall, Montgomery County experienced a downward trend in traffic collisions in 2019 compared to the previous year. The total number of crashes fell by 13.2%, from 167 to 145. This decline was accompanied by a 30.9% drop in total injuries, which decreased from 55 in 2018 to 38 in 2019.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 0→0.0%

38

Motorists Injured

Prior: 54↓-29.6%

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · 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 2018 and 2019. The day with the most crashes moved from Thursday (28 crashes) in the prior year to Wednesday (33 crashes) in the current year. Similarly, the peak hour for collisions changed from 9 p.m. in 2018 to 5 p.m. in 2019, with each period's peak hour recording 15 crashes.

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity decreased in 2019 compared to 2018, with zero fatal crashes reported in either year. The number of serious injury crashes was cut in half, falling from 8 to 4. The overall proportion of crashes involving any level of injury declined from 24.6% of all crashes in 2018 to 16.6% in 2019, while the share of crashes with no injuries increased from 75.4% to 83.4%.

Outcome by Severity (Crash Events)

Serious Injury4serious injury crashes2.8%
↓-50.0%prior 8
Minor Injury7minor injury crashes4.8%
↓-53.3%prior 15
Possible Injury13possible injury crashes9%
↓-27.8%prior 18
No Injury121no injury crashes83.4%
↓-4.0%prior 126

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving an animal remained the top contributing factor in both periods, with the count slightly decreasing from 43 crashes in 2018 to 41 in 2019. Several other leading factors saw a reduction in count, including crashes attributed to 'Lost Control' (from 12 to 7) and 'Followed too close' (from 9 to 7). Conversely, crashes where a driver 'Ran off road - straight' increased from 6 in the prior year to 8 in the current year.

Officer-Reported Primary Contributing Cause

Animal41 (28.3%)↓-4.7%prior 43
Ran off road - straight8 (5.5%)↑33.3%prior 6
Other (explain in narrative): Other8 (5.5%)
Driving too fast for conditions8 (5.5%)↓-11.1%prior 9
Followed too close7 (4.8%)↓-22.2%prior 9
Lost Control7 (4.8%)↓-41.7%prior 12
Improper Backing7 (4.8%)
FTYROW: From stop sign7 (4.8%)→0.0%prior 7
Ran Stop Sign6 (4.1%)↓-14.3%prior 7
Ran off road - left5 (3.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

While the total number of crashes on dry roads decreased from 90 to 70, collisions on snow-covered roads more than tripled, increasing from 4 in 2018 to 14 in 2019. Crashes in daylight conditions decreased from 91 to 76, while those in unlit, dark conditions rose from 16 to 19. The number of crashes during cloudy weather remained relatively stable, with 38 in 2019 compared to 36 in the prior year.

Weather

Clear59 (51.8%)
↓-24.4%prior 78
Cloudy38 (33.3%)
↑5.6%prior 36
Snow6 (5.3%)
→0.0%prior 6
Rain5 (4.4%)
Blowing Snow3 (2.6%)
Fog, smoke, smog2 (1.8%)
Severe Winds1 (0.9%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Weather condition at time of crash

Lighting

Daylight76 (66.7%)
↓-16.5%prior 91
Dark - roadway not lighted19 (16.7%)
↑18.8%prior 16
Dark - roadway lighted12 (10.5%)
→0.0%prior 12
Dawn4 (3.5%)
Dusk3 (2.6%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Lighting condition field

Road Surface

Dry70 (61.4%)
↓-22.2%prior 90
Wet15 (13.2%)
↑7.1%prior 14
Snow14 (12.3%)
Ice/frost9 (7.9%)
↓-43.8%prior 16
Mud, dirt3 (2.6%)
Slush2 (1.8%)
Gravel1 (0.9%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Road surface condition field

Vehicles & Demographics

Ford and Chevrolet (as 'CHEV') remained the top two vehicle makes involved in crashes, though their counts decreased from 53 to 40 and 52 to 35, respectively. The number of Dodge vehicles (as 'DODG') also fell from 19 to 11. Analysis of persons involved shows a slight increase in the 16-20 age group, from 41 individuals in 2018 to 44 in 2019, while the 65+ age group saw a decrease from 51 to 47.

Top Vehicle Makes (216 vehicles)

1
FORD40 (18.5%)
↓-24.5%prior 53
2
CHEV35 (16.2%)
↓-32.7%prior 52
3
DODG11 (5.1%)
↓-42.1%prior 19
4
JEEP11 (5.1%)
↑22.2%prior 9
5
DODGE10 (4.6%)
↑11.1%prior 9
6
CHEVROLET10 (4.6%)
↓-23.1%prior 13
7
GMC8 (3.7%)
↑33.3%prior 6
8
BUIC8 (3.7%)
↓-20.0%prior 10
9
NR7 (3.2%)
10
CHRY7 (3.2%)
↓-50.0%prior 14

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Vehicle unit records

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

Sex Distribution (184 persons with recorded sex)

Male114 (62.0%)
↑4.6%prior 109
Female70 (38.0%)
↓-1.4%prior 71

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · 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: 2019-01-01 through 2019-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2019-01-01 through 2019-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 145
  • Total persons involved: 307
  • Total vehicles involved: 216

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: 2019." Published September 9, 2026. Reporting period: 2019-01-01 to 2019-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2019-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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