Monthly Traffic Safety Analysis

816 CRASHES IN
MONTGOMERY, MD
JUNE 2022

All metrics benchmarked againstJune 2021

In June 2022, Montgomery County recorded 816 total crashes, a 4.1% increase from the 784 crashes documented in June 2021. This rise was accompanied by an increase in total injuries from 281 to 312. The most notable year-over-year change was in crash fatalities, which increased from one in the prior period to four in the current period.

816

4.1%was 784

Total Crash Events

4

300.0%was 1

Persons Killed

312

11.0%was 281

Persons Injured

157

-4.3%was 164

Hit-and-Run Crashes

Note: "Persons Killed" (4) counts individual fatalities across all crash events. "Fatal" in the severity table below (5) 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: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2022-06-01 to 2022-06-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Traffic safety metrics in Montgomery County show a negative trend in June 2022 compared to the previous year. Total crashes rose by 4.1% from 784 to 816. The number of people injured in these incidents increased by 11.0% from 281 to 312, and the number of fatalities quadrupled from one to four.

157

Hit-and-Run Crashes — June 2022

-4.3% vs prior (164)

The number of hit-and-run crashes showed a slight decrease year-over-year, falling from 164 in June 2021 to 157 in June 2022. The hit-and-run rate also declined, dropping from 20.9% of all crashes in the prior period to 19.2% in the current period. This indicates a modest downward trend for this type of incident.

Vulnerable Road User Casualties

2

Pedestrians Killed

Prior: 0%

1

Cyclists Killed

Prior: 0%

1

Motorists Killed

Prior: 10.0%

0

Other Killed

Prior: 00.0%

33

Pedestrians Injured

Prior: 1973.7%

8

Cyclists Injured

Prior: 13-38.5%

265

Motorists Injured

Prior: 2439.1%

6

Other Injured

Prior: 60.0%

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2022-06-01 to 2022-06-30 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The timing of crashes remained consistent year-over-year. Wednesday was the peak day for collisions in both June 2022 (144 crashes) and June 2021 (129 crashes). The 4 p.m. hour was also the peak time in both periods, accounting for 74 crashes in the current month and 78 in the prior year's month.

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2022-06-01 to 2022-06-30 · Crash date field aggregated by weekday

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2022-06-01 to 2022-06-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

There was a significant shift toward more severe outcomes in June 2022. The number of fatal crashes increased from one to five, and the fatal crash rate rose from 0.13% to 0.61%. While crashes involving serious injuries decreased from 20 to 12, those with minor injuries increased from 87 to 112. Consequently, the share of non-injury crashes fell from 70.5% to 66.8% of all incidents.

Severity is per crash event (most severe injury). 5 fatal crash events resulted in 4 persons killed.

Outcome by Severity (Crash Events)

Fatal4fatal crashes0.5%
300.0%prior 1
Serious Injury12serious injury crashes1.5%
-40.0%prior 20
Minor Injury112minor injury crashes13.7%
28.7%prior 87
Possible Injury141possible injury crashes17.3%
16.5%prior 121
No Injury545no injury crashes66.8%
-1.4%prior 553

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2022-06-01 to 2022-06-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2022-06-01 to 2022-06-30 · Most severe injury per crash record

Top Contributing Factors

The primary contributing factors cited in crash reports were consistent across both periods. Crashes attributed to 'RAIN, SNOW, WET' conditions remained the top factor, with the count increasing from 36 to 40. The second-ranked factor, 'N/A, WET', also saw a slight increase in count from 27 to 29 incidents. The overall ranking of the top two factors did not change year-over-year.

Officer-Reported Primary Contributing Cause

RAIN, SNOW, WET40 (4.9%)11.1%prior 36
N/A, WET29 (3.6%)7.4%prior 27
N/A, RAIN, SNOW6 (0.7%)
N/A, ROAD UNDER CONSTRUCTION/MAINTENANCE4 (0.5%)
ANIMAL, N/A3 (0.4%)
BACKUP DUE TO REGULAR CONGESTION, N/A2 (0.2%)
N/A, V EXHAUST SYSTEM|R OTHER ROAD2 (0.2%)
N/A, VISION OBSTRUCTION (INCL. BLINDED BY SUN)1 (0.1%)
BACKUP DUE TO PRIOR CRASH, RAIN, SNOW, WET1 (0.1%)
BACKUP DUE TO REGULAR CONGESTION, N/A, WET1 (0.1%)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2022-06-01 to 2022-06-30 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes in both periods occurred predominantly in clear weather and on dry roads. The proportion of crashes in daylight conditions increased slightly from 75.4% in June 2021 to 77.2% in June 2022. Crashes on wet roads saw an increase in count from 75 to 84, and incidents during rain rose from 60 to 66.

Weather

Clear623 (83.1%)
4.7%prior 595
Rain66 (8.8%)
10.0%prior 60
Cloudy61 (8.1%)
-17.6%prior 74

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2022-06-01 to 2022-06-30 · Weather condition at time of crash

Lighting

Daylight630 (78.5%)
6.6%prior 591
Dark - Lighted139 (17.3%)
6.1%prior 131
Dark - Not Lighted16 (2.0%)
-20.0%prior 20
Dusk9 (1.1%)
-47.1%prior 17
Dawn6 (0.7%)
-33.3%prior 9
Dark - Unknown Lighting3 (0.4%)
-40.0%prior 5

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2022-06-01 to 2022-06-30 · Lighting condition field

Road Surface

Dry586 (87.3%)
2.6%prior 571
Wet84 (12.5%)
12.0%prior 75
Other1 (0.1%)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2022-06-01 to 2022-06-30 · Road surface condition field

Vehicles & Demographics

Passenger cars remained the most frequent vehicle type in crashes, with involvement increasing from 925 in June 2021 to 969 in June 2022. The top three vehicle makes involved were Toyota, Honda, and Ford in both periods. However, their order changed; Ford, which was the second most common make in the prior period with 150 vehicles, moved to third with 130 vehicles in the current period, while Honda moved from third to second.

Top Vehicle Makes (1,423 vehicles)

1
TOYOTA197 (13.8%)
15.2%prior 171
2
HONDA147 (10.3%)
0.0%prior 147
3
FORD130 (9.1%)
-13.3%prior 150
4
TOYT69 (4.8%)
21.1%prior 57
5
NISSAN52 (3.7%)
-31.6%prior 76
6
HOND49 (3.4%)
14.0%prior 43
7
HYUNDAI44 (3.1%)
18.9%prior 37
8
CHEV36 (2.5%)
71.4%prior 21
9
KIA32 (2.2%)
10.3%prior 29
10
JEEP31 (2.2%)
-18.4%prior 38

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2022-06-01 to 2022-06-30 · Vehicle unit records

Data Sources & Methodology

Primary Data Source

All crash data in this report is sourced from Montgomery County Crash Reporting (ACRS) (https://data.montgomerycountymd.gov/d/bhju-22kf), 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)
  • Dataset URL: https://data.montgomerycountymd.gov/d/bhju-22kf
  • Data format: Structured JSON via REST API
  • Record types queried: Crash events, person records, and vehicle unit records
  • Date filter applied: 2022-06-01 through 2022-06-30
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2022-06-01 through 2022-06-30 (30 days)
  • Geographic scope: montgomery, MD
  • Total crash records analyzed: 816
  • Total persons involved: 1,482
  • Total vehicles involved: 1,423

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). "montgomery, MD Crash Intelligence Report: June 2022." Published September 9, 2026. Reporting period: 2022-06-01 to 2022-06-30. Data source: Montgomery County Crash Reporting (ACRS), Socrata Open Data. Dataset: https://data.montgomerycountymd.gov/d/bhju-22kf. Available at: https://thatcarhitme.com/crash-data/maryland/statewide/june-2022-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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