Monthly Traffic Safety Analysis

2,831 CRASHES IN
CHICAGO, IL
JUNE 2016

In June 2016, Chicago recorded 2831 crashes, resulting in 0 fatalities and 205 injuries. A notable finding is that 804 crashes, or 28.4% of the total, were hit-and-run incidents.

2,831

Total Crash Events

0

Persons Killed

205

Persons Injured

28.4%

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. 8 crashes with unreported severity are not shown in the severity breakdown.

Source: Chicago Traffic Crashes · Socrata Open Data · 2016-06-01 to 2016-06-30 · Aggregate counts from crash, person, and vehicle records

804

Hit-and-Run Crashes — June 2016

There were 804 hit-and-run crashes in June 2016, accounting for 28.4% of all reported crashes. This status is based on the initial determination by the responding officer at the scene.

Vulnerable Road User Casualties

During June 2016, there were 0 pedestrian fatalities, 0 cyclist fatalities, and 0 motorist fatalities. The highest number of injuries occurred among motorists, with 171 injured, followed by pedestrians with 19 injured and cyclists with 15 injured.

0

Pedestrians Killed

0

Cyclists Killed

0

Motorists Killed

19

Pedestrians Injured

15

Cyclists Injured

171

Motorists Injured

Source: Chicago Traffic Crashes · Socrata Open Data · 2016-06-01 to 2016-06-30 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

Crashes peaked on Wednesday with 481 incidents and during the 4 PM hour with 276 incidents. The majority of crashes, 78.0%, occurred during daylight conditions, while 14.0% occurred in darkness and 3.0% during dusk or dawn.

Source: Chicago Traffic Crashes · Socrata Open Data · 2016-06-01 to 2016-06-30 · Crash date field aggregated by weekday

Source: Chicago Traffic Crashes · Socrata Open Data · 2016-06-01 to 2016-06-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The majority of crashes, 94.0%, resulted in no injuries. Injury crashes, including serious, minor, and possible injuries, accounted for 5.8% of all incidents. There were 0 fatal crashes, and it is noted that fatalities (persons killed) may differ from fatal crashes as one crash can involve multiple fatalities.

Outcome by Severity (Crash Events)

Serious Injury14serious injury crashes0.5%
Minor Injury79minor injury crashes2.8%
Possible Injury70possible injury crashes2.5%
No Injury2,660no injury crashes94%

Source: Chicago Traffic Crashes · Socrata Open Data · 2016-06-01 to 2016-06-30 · KABCO injury classification scale

Severity Distribution

Source: Chicago Traffic Crashes · Socrata Open Data · 2016-06-01 to 2016-06-30 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factors were 'FOLLOWING TOO CLOSELY' (411 crashes, 14.5%) and 'FAILING TO YIELD RIGHT-OF-WAY' (278 crashes, 9.8%). 'IMPROPER OVERTAKING/PASSING' was also a significant factor, contributing to 161 crashes or 5.7% of the total.

Officer-Reported Primary Contributing Cause

FOLLOWING TOO CLOSELY411 (14.5%)
FAILING TO YIELD RIGHT-OF-WAY278 (9.8%)
IMPROPER OVERTAKING/PASSING161 (5.7%)
IMPROPER BACKING153 (5.4%)
DRIVING SKILLS/KNOWLEDGE/EXPERIENCE143 (5.1%)
IMPROPER LANE USAGE129 (4.6%)
IMPROPER TURNING/NO SIGNAL89 (3.1%)
FAILING TO REDUCE SPEED TO AVOID CRASH79 (2.8%)
DISREGARDING TRAFFIC SIGNALS31 (1.1%)
OPERATING VEHICLE IN ERRATIC, RECKLESS, CARELESS, NEGLIGENT OR AGGRESSIVE MANNER30 (1.1%)

Source: Chicago Traffic Crashes · Socrata Open Data · 2016-06-01 to 2016-06-30 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Most crashes occurred under clear weather (87.8%), dry road surfaces (86.2%), and daylight conditions (78.0%). Adverse conditions such as rain contributed to 119 crashes, and wet road surfaces were present in 146 crashes.

Weather

CLEAR2,487 (94.0%)
RAIN119 (4.5%)
CLOUDY/OVERCAST38 (1.4%)
FOG/SMOKE/HAZE1 (0.0%)
OTHER1 (0.0%)

Source: Chicago Traffic Crashes · Socrata Open Data · 2016-06-01 to 2016-06-30 · Weather condition at time of crash

Lighting

DAYLIGHT2,208 (82.1%)
DARKNESS, LIGHTED ROAD331 (12.3%)
DARKNESS66 (2.5%)
DUSK61 (2.3%)
DAWN25 (0.9%)

Source: Chicago Traffic Crashes · Socrata Open Data · 2016-06-01 to 2016-06-30 · Lighting condition field

Road Surface

DRY2,440 (94.2%)
WET146 (5.6%)
OTHER3 (0.1%)
SNOW OR SLUSH1 (0.0%)

Source: Chicago Traffic Crashes · Socrata Open Data · 2016-06-01 to 2016-06-30 · Road surface condition field

Vehicles & Demographics

The age group 26-34 was most represented among persons involved in crashes with 888 individuals, followed by 35-44 with 757 individuals. Chevrolet vehicles were involved in 709 incidents, Toyota in 662, and Ford in 586, making them the most frequently involved vehicle makes.

Top Vehicle Makes (5,709 vehicles)

1
CHEVROLET709 (12.4%)
2
TOYOTA MOTOR COMPANY, LTD.662 (11.6%)
3
FORD586 (10.3%)
4
NISSAN416 (7.3%)
5
HONDA325 (5.7%)
6
DODGE269 (4.7%)
7
JEEP207 (3.6%)
8
HYUNDAI190 (3.3%)
9
CHRYSLER147 (2.6%)
10
KIA MOTORS CORP109 (1.9%)

Source: Chicago Traffic Crashes · Socrata Open Data · 2016-06-01 to 2016-06-30 · Vehicle unit records

2,056 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 (5,992 persons with recorded sex)

Male3,055 (51.0%)
Female2,404 (40.1%)
Non-Binary533 (8.9%)

Source: Chicago Traffic Crashes · Socrata Open Data · 2016-06-01 to 2016-06-30 · Person-level records linked to crash events

Speed Limit Zones

The 30 mph speed limit zone accounted for the highest number of crashes, with 2061 incidents, representing 72.8% of all crashes. No crashes in any speed limit zone were recorded as fatal in this period.

Source: Chicago Traffic Crashes · Socrata Open Data · 2016-06-01 to 2016-06-30 · Posted speed limit at crash location

Crashes by District

District 03 recorded the highest number of crashes, with 303 incidents, accounting for 10.7% of the total 2831 crashes. District 01 followed with 273 crashes, indicating a concentration of incidents in these areas.

Crashes by District

"Other" combines 15 smaller categories (1,342 records): District 10 (131), District 24 (129), District 22 (114), District 17 (109), District 14 (100), District 15 (96), District 05 (91), District 02 (91), District 09 (86), District 04 (83), District 20 (77), District 19 (77), District 25 (62), District 16 (59), District 07 (37).

Source: Chicago Traffic Crashes · Socrata Open Data · 2016-06-01 to 2016-06-30 · Person-level records

First Crash Type

Rear-end collisions were the most common first crash type, accounting for 820 incidents or 29.0% of all crashes. Crashes involving parked motor vehicles (654 incidents) and sideswipes in the same direction (563 incidents) were also prominent.

First Crash Type

1
REAR END820 (29%)
2
PARKED MOTOR VEHICLE654 (23.1%)
3
SIDESWIPE SAME DIRECTION563 (19.9%)
4
TURNING329 (11.6%)
5
ANGLE263 (9.3%)
6
FIXED OBJECT60 (2.1%)
7
PEDALCYCLIST32 (1.1%)
8
SIDESWIPE OPPOSITE DIRECTION32 (1.1%)
9
PEDESTRIAN25 (0.9%)

Showing top 9 of 12 reported. 3 additional (53 total) not shown: HEAD ON, OTHER OBJECT, OTHER NONCOLLISION.

Source: Chicago Traffic Crashes · Socrata Open Data · 2016-06-01 to 2016-06-30 · Person-level records

Point of Impact

The front of vehicles was the most frequent point of impact, representing 22.4% of all recorded impacts. Rear impacts accounted for 18.6% of incidents, and front-right impacts made up 13.1% of recorded impacts.

Point of Impact

"Other" combines 5 smaller categories (389 records): REAR-RIGHT (322), TOTAL (ALL AREAS) (28), OTHER (23), UNDER CARRIAGE (11), ROOF (5).

Source: Chicago Traffic Crashes · Socrata Open Data · 2016-06-01 to 2016-06-30 · Person-level records

Pre-Crash Driver Action

The most common pre-crash action recorded was 'STRAIGHT AHEAD', accounting for 44.1% of known actions. 'PARKED' was the second most frequent action at 12.1%, followed by 'SLOW/STOP IN TRAFFIC' at 9.9%.

Pre-Crash Driver Action

1
STRAIGHT AHEAD2,458 (43.6%)
2
PARKED676 (12%)
3
SLOW/STOP IN TRAFFIC550 (9.8%)
4
UNKNOWN/NA485 (8.6%)
5
BACKING303 (5.4%)
6
TURNING LEFT258 (4.6%)
7
CHANGING LANES157 (2.8%)
8
TURNING RIGHT154 (2.7%)
9
PASSING/OVERTAKING144 (2.6%)

Showing top 9 of 26 reported. 17 additional (449 total) not shown: OTHER, ENTERING TRAFFIC LANE FROM PARKING, STARTING IN TRAFFIC, MERGING, LEAVING TRAFFIC LANE TO PARK, ENTER FROM DRIVE/ALLEY, SLOW/STOP - LEFT TURN, SLOW/STOP - RIGHT TURN, U-TURN, AVOIDING VEHICLES/OBJECTS, PARKED IN TRAFFIC LANE, SLOW/STOP - LOAD/UNLOAD, NEGOTIATING A CURVE, SKIDDING/CONTROL LOSS, DRIVING WRONG WAY, TURNING ON RED, DRIVERLESS.

Source: Chicago Traffic Crashes · Socrata Open Data · 2016-06-01 to 2016-06-30 · Person-level records

Pedestrian/Cyclist Action

Among known pedestrian actions, 'WITH TRAFFIC' was the most frequent, representing 28.0% of recorded actions. 'OTHER ACTION' accounted for 18.0%, and 'CROSSING - WITH SIGNAL' for 16.0%.

Pedestrian/Cyclist Action

1
UNKNOWN/NA16 (23.9%)
2
WITH TRAFFIC14 (20.9%)
3
OTHER ACTION9 (13.4%)
4
CROSSING - WITH SIGNAL8 (11.9%)
5
CROSSING - AGAINST SIGNAL6 (9%)
6
NO ACTION4 (6%)
7
WORKING IN ROADWAY3 (4.5%)
8
ENTER FROM DRIVE/ALLEY2 (3%)
9
PARKED VEHICLE1 (1.5%)

Showing top 9 of 13 reported. 4 additional (4 total) not shown: PLAYING/WORKING ON VEHICLE, STANDING IN ROADWAY, NOT AT INTERSECTION, AGAINST TRAFFIC.

Source: Chicago Traffic Crashes · Socrata Open Data · 2016-06-01 to 2016-06-30 · 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: 2016-06-01 through 2016-06-30
  • Report generated: June 1, 2026

Data Coverage

  • Reporting period: 2016-06-01 through 2016-06-30 (30 days)
  • Geographic scope: Chicago, IL
  • Total crash records analyzed: 2,831
  • Total persons involved: 6,083
  • Total vehicles involved: 5,709

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/june-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

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Chicago, IL Crash Report — June 2016 | ThatCarHitMe.com