ThatCarHitMe.com
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CRASH INTELLIGENCE REPORT · CHICAGO, IL · APRIL 2016
Purpose: Machine-readable JSON endpoint for AI agents, LLMs, researchers, and programmatic consumers. Returns all underlying crash data and AI-generated commentary without HTML.
Authentication: None required. Public endpoint.
GET: https://thatcarhitme.com/api/crash-data/reports/data/illinois/chicago/april-2016-report
Monthly Traffic Safety Analysis
2,902 CRASHES IN
CHICAGO, IL
APRIL 2016
In April 2016, Chicago recorded a total of 2902 crashes, resulting in 0 fatalities and 240 injuries. A notable finding from this period is that 746 crashes, or 25.7% of all incidents, were classified as hit-and-run events. This data provides a snapshot of traffic safety trends within the city for the specified month.
2,902
Total Crash Events
0
Persons Killed
240
Persons Injured
25.7%
Hit-and-Run Rate
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. 2 crashes with unreported severity are not shown in the severity breakdown.
Source: Chicago Traffic Crashes · Socrata Open Data · 2016-04-01 to 2016-04-30 · Aggregate counts from crash, person, and vehicle records
746
Hit-and-Run Crashes — April 2016
During April 2016, 746 crashes were identified as hit-and-run incidents, accounting for 25.7% of all crashes. This classification is based on the initial determination made by the responding officer at the scene. The data indicates a significant proportion of crashes involved a driver leaving the scene.
Vulnerable Road User Casualties
During April 2016, no pedestrians, cyclists, or motorists were killed in crashes. However, 205 motorists sustained injuries, making them the most impacted group. Additionally, 19 pedestrians and 16 cyclists were injured in crash incidents.
0
Pedestrians Killed
0
Cyclists Killed
0
Motorists Killed
19
Pedestrians Injured
16
Cyclists Injured
205
Motorists Injured
Source: Chicago Traffic Crashes · Socrata Open Data · 2016-04-01 to 2016-04-30 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
Crash incidents in April 2016 peaked on Fridays with 556 crashes, followed closely by Saturdays with 542 crashes. The busiest hour for crashes was 3 PM, recording 268 incidents. The majority of crashes, 2158, occurred during daylight hours, while 425 happened in darkness on lighted roads and 69 in unlit darkness.
Source: Chicago Traffic Crashes · Socrata Open Data · 2016-04-01 to 2016-04-30 · Crash date field aggregated by weekday
Source: Chicago Traffic Crashes · Socrata Open Data · 2016-04-01 to 2016-04-30 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The majority of crashes, 94% (2727 incidents), resulted in no reported injuries. Injury crashes, encompassing serious, minor, and possible injuries, accounted for 6% of all incidents, totaling 173 crashes. There were no fatal crashes reported during this period, meaning no crash events resulted in a death.
Outcome by Severity (Crash Events)
Source: Chicago Traffic Crashes · Socrata Open Data · 2016-04-01 to 2016-04-30 · KABCO injury classification scale
Severity Distribution
Source: Chicago Traffic Crashes · Socrata Open Data · 2016-04-01 to 2016-04-30 · Most severe injury per crash record
Top Contributing Factors
The leading contributing factor to crashes was 'FOLLOWING TOO CLOSELY,' involved in 355 incidents, representing 12.2% of all crashes. 'FAILING TO YIELD RIGHT-OF-WAY' was the second most common factor, contributing to 325 crashes or 11.2%. These two factors collectively account for a significant portion of reported crash causes.
Officer-Reported Primary Contributing Cause
Source: Chicago Traffic Crashes · Socrata Open Data · 2016-04-01 to 2016-04-30 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Most crashes occurred under clear weather conditions (2109 incidents) and on dry road surfaces (2045 incidents). Daylight was the predominant lighting condition for crashes, with 2158 reported incidents. Adverse conditions such as rain, snow, or wet/icy roads accounted for a smaller proportion of crashes.
Weather
Source: Chicago Traffic Crashes · Socrata Open Data · 2016-04-01 to 2016-04-30 · Weather condition at time of crash
Lighting
Source: Chicago Traffic Crashes · Socrata Open Data · 2016-04-01 to 2016-04-30 · Lighting condition field
Road Surface
Source: Chicago Traffic Crashes · Socrata Open Data · 2016-04-01 to 2016-04-30 · Road surface condition field
Vehicles & Demographics
The age group 26-34 was most represented among persons involved in crashes, with 994 individuals. Chevrolet vehicles were involved in the highest number of crashes, totaling 728 incidents. Toyota Motor Company, Ltd. and Ford were the next most frequently involved makes, with 679 and 594 incidents respectively.
Top Vehicle Makes (5,833 vehicles)
Source: Chicago Traffic Crashes · Socrata Open Data · 2016-04-01 to 2016-04-30 · Vehicle unit records
2,028 persons with unknown or unrecorded age excluded from age chart. Age=0 in Chicago records is a sentinel for unknown/unrecorded age (not infants) and is grouped with nulls.
Sex Distribution (6,239 persons with recorded sex)
Source: Chicago Traffic Crashes · Socrata Open Data · 2016-04-01 to 2016-04-30 · Person-level records linked to crash events
Speed Limit Zones
The 30 mph speed limit zone recorded the highest number of crashes, with 2114 incidents, representing 72.8% of all crashes. All reported speed limit zones, including the 30 mph zone, had a fatal crash percentage of 0% during this period. This indicates no fatalities occurred within any of the listed speed zones.
Source: Chicago Traffic Crashes · Socrata Open Data · 2016-04-01 to 2016-04-30 · Posted speed limit at crash location
Crashes by District
District 01 recorded the highest number of crashes with 301 incidents, accounting for 10.4% of all crashes. Other districts with high crash counts include District 03 with 284 crashes and District 08 with 267 crashes. These districts show the highest concentrations of crash activity.
Crashes by District
"Other" combines 15 smaller categories (1,270 records): District 06 (128), District 24 (123), District 09 (118), District 15 (107), District 25 (100), District 02 (92), District 14 (91), District 17 (80), District 04 (77), District 22 (76), District 20 (74), District 19 (57), District 16 (56), District 07 (48), District 05 (43).
Source: Chicago Traffic Crashes · Socrata Open Data · 2016-04-01 to 2016-04-30 · Person-level records
First Crash Type
The most common type of first crash was 'REAR END,' accounting for 826 incidents or 28.5% of all crashes. Collisions with 'PARKED MOTOR VEHICLE' were the second most frequent, with 615 occurrences. 'SIDESWIPE SAME DIRECTION' was also a significant crash type, involved in 569 incidents.
First Crash Type
Showing top 9 of 14 reported. 5 additional (50 total) not shown: PEDALCYCLIST, OTHER OBJECT, OTHER NONCOLLISION, OVERTURNED, ANIMAL.
Source: Chicago Traffic Crashes · Socrata Open Data · 2016-04-01 to 2016-04-30 · Person-level records
Point of Impact
The 'FRONT' of vehicles was the most common point of impact, occurring in 1187 instances, representing 23.1% of all specified impact points. The 'REAR' was the second most frequent impact area with 956 instances. 'FRONT-RIGHT' and 'FRONT-LEFT' impacts were also common, with 717 and 688 instances respectively.
Point of Impact
"Other" combines 5 smaller categories (376 records): REAR-RIGHT (320), OTHER (25), TOTAL (ALL AREAS) (16), UNDER CARRIAGE (11), ROOF (4).
Source: Chicago Traffic Crashes · Socrata Open Data · 2016-04-01 to 2016-04-30 · Person-level records
Pre-Crash Driver Action
The most frequent pre-crash action was vehicles proceeding 'STRAIGHT AHEAD,' accounting for 2472 instances or 49.4% of all specified actions. 'SLOW/STOP IN TRAFFIC' was the second most common action, occurring 648 times. Vehicles that were 'PARKED' at the time of the crash accounted for 644 instances.
Pre-Crash Driver Action
Showing top 9 of 26 reported. 17 additional (483 total) not shown: OTHER, ENTERING TRAFFIC LANE FROM PARKING, STARTING IN TRAFFIC, MERGING, SLOW/STOP - LEFT TURN, ENTER FROM DRIVE/ALLEY, AVOIDING VEHICLES/OBJECTS, LEAVING TRAFFIC LANE TO PARK, U-TURN, SLOW/STOP - LOAD/UNLOAD, SKIDDING/CONTROL LOSS, NEGOTIATING A CURVE, SLOW/STOP - RIGHT TURN, PARKED IN TRAFFIC LANE, DRIVING WRONG WAY, TURNING ON RED, DRIVERLESS.
Source: Chicago Traffic Crashes · Socrata Open Data · 2016-04-01 to 2016-04-30 · Person-level records
Pedestrian/Cyclist Action
Among pedestrians involved in crashes, the most common action was 'WITH TRAFFIC,' occurring in 14 instances, representing 35.9% of specified pedestrian actions. 'CROSSING - WITH SIGNAL' was the next most frequent action, with 7 instances. Four pedestrians were involved in crashes while 'NOT AT INTERSECTION'.
Pedestrian/Cyclist Action
Showing top 9 of 11 reported. 2 additional (2 total) not shown: TURNING RIGHT, NO ACTION.
Source: Chicago Traffic Crashes · Socrata Open Data · 2016-04-01 to 2016-04-30 · Person-level records
Data Sources & Methodology
Primary Data Source
All crash data in this report is sourced from Chicago Traffic Crashes, accessed programmatically via the Socrata 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: Socrata 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: 2016-04-01 through 2016-04-30
- Report generated: June 1, 2026
Data Coverage
- Reporting period: 2016-04-01 through 2016-04-30 (30 days)
- Geographic scope: Chicago, IL
- Total crash records analyzed: 2,902
- Total persons involved: 6,304
- Total vehicles involved: 5,833
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). "Chicago, IL Crash Intelligence Report." Published June 1, 2026. Data source: Chicago Traffic Crashes, Socrata Open Data. Available at: https://thatcarhitme.com/crash-data/illinois/chicago/april-2016-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
ThatCarHitMe.com · An Injuria.ai Company
ThatCarHitMe.com
An Injuria.ai Company
Crash Data Intelligence
Data: Chicago Traffic Crashes · Socrata
Period: 2016-04-01 – 2016-04-30
Generated: June 1, 2026 · All rights reserved