Monthly Traffic Safety Analysis

852 CRASHES IN
MONTGOMERY, MD
JULY 2021

All metrics benchmarked againstJuly 2020

In July 2021, Montgomery County recorded 852 total crashes, a 37.9% increase from the 618 crashes reported in July 2020. This period also saw a rise in total injuries from 235 to 307 and an increase in fatalities from one to two. The most significant year-over-year change was the overall increase in crash volume across the county.

852

37.9%was 618

Total Crash Events

2

100.0%was 1

Persons Killed

307

30.6%was 235

Persons Injured

204

27.5%was 160

Hit-and-Run Crashes

Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) 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 · 2021-07-01 to 2021-07-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash trends in Montgomery County show a significant increase when comparing July 2021 to the same month in the prior year. Total crashes rose by 37.9% from 618 to 852. Similarly, the number of people injured increased by 30.6% from 235 to 307, and total fatalities rose from one to two.

204

Hit-and-Run Crashes — July 2021

27.5% vs prior (160)

The number of hit-and-run crashes increased from 160 in July 2020 to 204 in July 2021, a 27.5% rise in volume. However, the hit-and-run rate as a percentage of all crashes saw a decrease, dropping from 25.9% in the prior year to 23.9% in the current period. This indicates that while the absolute number of hit-and-runs grew, they constituted a smaller proportion of total crashes.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

0

Cyclists Killed

Prior: 00.0%

2

Motorists Killed

Prior: 1100.0%

0

Other Killed

Prior: 00.0%

27

Pedestrians Injured

Prior: 258.0%

17

Cyclists Injured

Prior: 166.3%

262

Motorists Injured

Prior: 19037.9%

1

Other Injured

Prior: 4-75.0%

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2021-07-01 to 2021-07-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes showed a mix of consistency and change. Thursday remained the peak day for crashes in both July 2020 (112 crashes) and July 2021 (162 crashes). However, the peak hour shifted earlier from 5 PM in 2020 (50 crashes) to 3 PM in 2021 (69 crashes).

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2021-07-01 to 2021-07-31 · Crash date field aggregated by weekday

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2021-07-01 to 2021-07-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

In July 2021, there were 2 fatal crashes resulting in 2 deaths, compared to 1 fatal crash and 1 death in July 2020. The overall proportion of crashes involving any level of injury remained stable at approximately 31.6%. Within that, the share of crashes involving minor injuries increased from 10.8% to 14.4%, while the share of possible injury crashes decreased from 18.1% to 15.4%.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.2%
100.0%prior 1
Serious Injury15serious injury crashes1.8%
-11.8%prior 17
Minor Injury123minor injury crashes14.4%
83.6%prior 67
Possible Injury131possible injury crashes15.4%
17.0%prior 112
No Injury579no injury crashes68%
37.9%prior 420

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2021-07-01 to 2021-07-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2021-07-01 to 2021-07-31 · Most severe injury per crash record

Top Contributing Factors

The top contributing factors remained consistent year-over-year, with wet conditions being a primary theme. The count of crashes attributed to 'RAIN, SNOW, WET' increased from 26 to 34, a 30.8% rise. Similarly, incidents citing 'N/A, WET' rose from 22 to 23. The top three factors were identical in both periods, with each seeing a year-over-year increase in the number of associated crashes.

Officer-Reported Primary Contributing Cause

RAIN, SNOW, WET34 (4%)30.8%prior 26
N/A, WET23 (2.7%)4.5%prior 22
ANIMAL, N/A8 (0.9%)14.3%prior 7
N/A, ROAD UNDER CONSTRUCTION/MAINTENANCE5 (0.6%)
BACKUP DUE TO REGULAR CONGESTION, N/A4 (0.5%)-20.0%prior 5
DEBRIS OR OBSTRUCTION, N/A3 (0.4%)
BACKUP DUE TO PRIOR CRASH, N/A2 (0.2%)
SLEET, HAIL, FREEZ. RAIN, WET2 (0.2%)
N/A, NON-HIGHWAY WORK1 (0.1%)
BACKUP DUE TO NON-RECURRING INCIDENT, N/A1 (0.1%)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2021-07-01 to 2021-07-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The distribution of environmental conditions during crashes remained largely stable year-over-year. In both July 2021 and July 2020, the majority of crashes occurred in clear weather (76.5% and 78.8%, respectively) and on dry road surfaces. The proportion of crashes happening during daylight hours saw a slight increase from 68.9% in July 2020 to 73.1% in July 2021.

Weather

Clear652 (84.3%)
33.9%prior 487
Cloudy67 (8.7%)
148.1%prior 27
Rain53 (6.9%)
29.3%prior 41
Other1 (0.1%)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2021-07-01 to 2021-07-31 · Weather condition at time of crash

Lighting

Daylight623 (74.6%)
46.2%prior 426
Dark - Lighted162 (19.4%)
25.6%prior 129
Dark - Not Lighted16 (1.9%)
-27.3%prior 22
Dawn15 (1.8%)
36.4%prior 11
Dark - Unknown Lighting10 (1.2%)
25.0%prior 8
Dusk8 (1.0%)
-20.0%prior 10
Other1 (0.1%)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2021-07-01 to 2021-07-31 · Lighting condition field

Road Surface

Dry634 (90.2%)
34.9%prior 470
Wet69 (9.8%)
32.7%prior 52

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2021-07-01 to 2021-07-31 · Road surface condition field

Vehicles & Demographics

The most common vehicle makes involved in collisions remained consistent between July 2020 and July 2021. Toyota, Honda, and Ford were the top three makes in both periods, with the number of vehicles from each make increasing in line with the overall rise in crashes. For example, Toyota-made vehicles involved in crashes increased from 148 to 194, but the relative ranking of the top makes did not change.

Top Vehicle Makes (1,474 vehicles)

1
TOYOTA194 (13.2%)
31.1%prior 148
2
HONDA174 (11.8%)
25.2%prior 139
3
FORD142 (9.6%)
30.3%prior 109
4
TOYT77 (5.2%)
92.5%prior 40
5
HOND55 (3.7%)
96.4%prior 28
6
NISSAN54 (3.7%)
-18.2%prior 66
7
CHEVROLET43 (2.9%)
53.6%prior 28
8
DODGE40 (2.7%)
21.2%prior 33
9
CHEVY39 (2.6%)
85.7%prior 21
10
CHEV37 (2.5%)
105.6%prior 18

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2021-07-01 to 2021-07-31 · 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: 2021-07-01 through 2021-07-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2021-07-01 through 2021-07-31 (31 days)
  • Geographic scope: montgomery, MD
  • Total crash records analyzed: 852
  • Total persons involved: 1,527
  • Total vehicles involved: 1,474

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: July 2021." Published September 9, 2026. Reporting period: 2021-07-01 to 2021-07-31. 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/july-2021-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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