Monthly Traffic Safety Analysis

5,594 CRASHES IN
IOWA, IA
NOVEMBER 2017

All metrics benchmarked againstNovember 2016

In November 2017, there were 5,594 total crashes recorded, representing a 1.2% decrease from the 5,663 crashes in November 2016. While the overall crash volume remained relatively stable, the number of fatalities saw a significant year-over-year decline. Fatalities dropped by 57.1%, from 35 in the prior period to 15 in the current period.

5,594

▼ -1.2%was 5,663

Total Crash Events

15

▼ -57.1%was 35

Persons Killed

1,496

▼ -6.5%was 1,600

Persons Injured

15

▼ -53.1%was 32

Fatal Crash Events

Note: "Persons Killed" (15) counts individual fatalities across all crash events. "Fatal" in the severity table below (15) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2017-11-01 to 2017-11-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall traffic collisions showed a slight decrease in November 2017 compared to the same month in 2016, with total crashes falling by 1.2% from 5,663 to 5,594. This downward trend was more pronounced in crash outcomes, as total injuries fell by 6.5% from 1,600 to 1,496. Most notably, fatalities decreased by 57.1% year-over-year, from 35 to 15.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 2↓-50.0%

0

Cyclists Killed

Prior: 0→0.0%

14

Motorists Killed

Prior: 33↓-57.6%

0

Other Killed

Prior: 0→0.0%

38

Pedestrians Injured

Prior: 52↓-26.9%

8

Cyclists Injured

Prior: 26↓-69.2%

1,447

Motorists Injured

Prior: 1,519↓-4.7%

3

Other Injured

Prior: 3→0.0%

Source: Iowa Crash Data · ArcGIS Open Data · 2017-11-01 to 2017-11-30 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes remained largely consistent year-over-year, with the 5 p.m. hour being the peak time for collisions in both November 2017 (707 crashes) and November 2016 (669 crashes). However, the peak day for crashes shifted from Tuesday in 2016 (1,029 crashes) to Wednesday in 2017 (942 crashes). Weekday afternoons continue to be the period with the highest crash frequency.

Source: Iowa Crash Data · ArcGIS Open Data · 2017-11-01 to 2017-11-30 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2017-11-01 to 2017-11-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity decreased notably in November 2017 compared to the prior year. The number of fatal crashes fell from 32 to 15, causing the fatal crash rate to drop from 0.57% to 0.27% of all crashes. The proportion of crashes resulting in serious injuries also declined from 1.7% to 1.4%. Correspondingly, the share of no-injury crashes increased slightly from 76.0% to 76.9% of all incidents.

Outcome by Severity (Crash Events)

Fatal15fatal crashes0.3%
↓-53.1%prior 32
Serious Injury78serious injury crashes1.4%
↓-20.4%prior 98
Minor Injury380minor injury crashes6.8%
↓-5.5%prior 402
Possible Injury822possible injury crashes14.7%
↓-0.6%prior 827
No Injury4,299no injury crashes76.9%
↓-0.1%prior 4,304

Source: Iowa Crash Data · ArcGIS Open Data · 2017-11-01 to 2017-11-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-11-01 to 2017-11-30 · Most severe injury per crash record

Top Contributing Factors

Collisions involving animals remained the top contributing factor in both periods, with the count of such incidents increasing by 5.5% from 1,650 in November 2016 to 1,740 in November 2017. 'Followed too close' was the second-ranked factor in both years, though its count decreased by 8.3% from 604 to 554 crashes. Crashes attributed to 'Failure to yield from a stop sign' saw a 12.1% drop in count from 289 to 254, moving it from the third to the fifth most common factor.

Officer-Reported Primary Contributing Cause

Animal1,740 (31.1%)↑5.5%prior 1,650
Followed too close554 (9.9%)↓-8.3%prior 604
Other (explain in narrative): Other294 (5.3%)↑14.8%prior 256
Lost Control267 (4.8%)↓-6.0%prior 284
FTYROW: From stop sign254 (4.5%)↓-12.1%prior 289
Ran off road - left230 (4.1%)↓-9.1%prior 253
FTYROW: Making left turn183 (3.3%)↓-13.3%prior 211
Ran Traffic Signal153 (2.7%)↑8.5%prior 141
Ran Stop Sign142 (2.5%)↑5.2%prior 135
Operating vehicle in an reckless, erratic, careless, negligent manner138 (2.5%)↑36.6%prior 101

Source: Iowa Crash Data · ArcGIS Open Data · 2017-11-01 to 2017-11-30 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The majority of crashes in both periods occurred in clear weather and on dry roads. There was a slight shift in lighting conditions, with the proportion of crashes in daylight decreasing from 43.4% in November 2016 to 39.9% in November 2017, while crashes in dark conditions increased from 28.9% to 30.5%. The share of crashes during cloudy weather also grew, rising from 15.5% to 19.4% year-over-year.

Weather

Clear2,815 (66.4%)
↓-6.8%prior 3,019
Cloudy1,083 (25.5%)
↑22.9%prior 881
Rain196 (4.6%)
↓-33.8%prior 296
Fog, smoke, smog101 (2.4%)
↑206.1%prior 33
Freezing rain/drizzle37 (0.9%)
↓-5.1%prior 39
Other (explain in narrative)4 (0.1%)
↓-33.3%prior 6
Severe Winds3 (0.1%)
↓-83.3%prior 18
Sleet, hail2 (0.0%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-11-01 to 2017-11-30 · Weather condition at time of crash

Lighting

Daylight2,233 (52.5%)
↓-9.1%prior 2,456
Dark - roadway lighted924 (21.7%)
↑4.1%prior 888
Dark - roadway not lighted750 (17.6%)
↑1.9%prior 736
Dusk189 (4.4%)
↑15.2%prior 164
Dawn127 (3.0%)
↑13.4%prior 112
Dark - unknown roadway lighting30 (0.7%)
↑150.0%prior 12

Source: Iowa Crash Data · ArcGIS Open Data · 2017-11-01 to 2017-11-30 · Lighting condition field

Road Surface

Dry3,648 (85.7%)
↑1.1%prior 3,609
Wet470 (11.0%)
↓-16.1%prior 560
Gravel106 (2.5%)
↑3.9%prior 102
Ice/frost22 (0.5%)
↓-52.2%prior 46
Mud, dirt7 (0.2%)
→0.0%prior 7
Other (explain in narrative)1 (0.0%)
↓-80.0%prior 5
Sand1 (0.0%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-11-01 to 2017-11-30 · Road surface condition field

Vehicles & Demographics

The makes of vehicles involved in crashes remained consistent, with Ford and Chevrolet (including abbreviated names) being the most common in both November 2017 and November 2016. The count for Ford vehicles involved was 1,526, up from 1,505, while the combined count for Chevrolet vehicles was 1,838, nearly identical to the prior year's 1,846. An analysis of persons involved shows a stable age distribution for most groups, though the number of individuals aged 65 and older increased from 992 to 1,124.

Top Vehicle Makes (8,905 vehicles)

1
FORD1,526 (17.1%)
↑1.4%prior 1,505
2
CHEV1,264 (14.2%)
↑60.4%prior 788
3
CHEVROLET574 (6.4%)
↓-45.7%prior 1,058
4
TOYT432 (4.9%)
↑45.5%prior 297
5
DODG398 (4.5%)
↑42.1%prior 280
6
JEEP291 (3.3%)
↑8.6%prior 268
7
HOND288 (3.2%)
↑47.7%prior 195
8
GMC246 (2.8%)
↓-7.5%prior 266
9
TOYOTA239 (2.7%)
↓-42.0%prior 412
10
CHRY237 (2.7%)
↑94.3%prior 122

Source: Iowa Crash Data · ArcGIS Open Data · 2017-11-01 to 2017-11-30 · Vehicle unit records

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

Sex Distribution (6,033 persons with recorded sex)

Male3,353 (55.6%)
↓-14.6%prior 3,924
Female2,680 (44.4%)
↓-9.9%prior 2,975

Source: Iowa Crash Data · ArcGIS Open Data · 2017-11-01 to 2017-11-30 · 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: 2017-11-01 through 2017-11-30
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2017-11-01 through 2017-11-30 (30 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 5,594
  • Total persons involved: 9,867
  • Total vehicles involved: 8,905

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: November 2017." Published September 9, 2026. Reporting period: 2017-11-01 to 2017-11-30. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/november-2017-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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