Monthly Traffic Safety Analysis

733 CRASHES IN
MONTGOMERY, MD
JANUARY 2022

All metrics benchmarked againstJanuary 2021

In January 2022, Montgomery County recorded 733 total crashes, a 29.7% increase from the 565 crashes in January 2021. This rise was accompanied by an increase in both injuries and fatalities. The most notable year-over-year shift was the increase in total fatalities from one to five.

733

29.7%was 565

Total Crash Events

5

400.0%was 1

Persons Killed

225

29.3%was 174

Persons Injured

156

38.1%was 113

Hit-and-Run Crashes

Note: "Persons Killed" (5) 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. 4 crashes with unreported severity are not shown in the severity breakdown.

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2022-01-01 to 2022-01-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Traffic safety metrics worsened in a year-over-year comparison for January. Total crashes rose by 29.7%, from 565 to 733. Concurrently, the number of people injured increased by 29.3% from 174 to 225, and the number of fatalities increased from 1 to 5.

156

Hit-and-Run Crashes — January 2022

38.1% vs prior (113)

Hit-and-run crashes increased in both count and rate year-over-year. The number of hit-and-run incidents rose by 38.1%, from 113 in January 2021 to 156 in January 2022. The corresponding hit-and-run rate also increased, climbing from 20.0% to 21.3% of all reported crashes.

Vulnerable Road User Casualties

2

Pedestrians Killed

Prior: 1100.0%

1

Cyclists Killed

Prior: 0%

2

Motorists Killed

Prior: 0%

0

Other Killed

Prior: 00.0%

34

Pedestrians Injured

Prior: 2347.8%

2

Cyclists Injured

Prior: 5-60.0%

187

Motorists Injured

Prior: 14628.1%

2

Other Injured

Prior: 0%

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2022-01-01 to 2022-01-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes shifted between the two periods. In January 2022, the peak day for crashes was Monday with 147 incidents, a change from Friday (103 incidents) in the prior year. The peak hour also moved earlier, from the 5 p.m. hour in 2021 (56 crashes) to the 3 p.m. hour in 2022 (64 crashes).

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2022-01-01 to 2022-01-31 · Crash date field aggregated by weekday

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2022-01-01 to 2022-01-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity of crashes increased year-over-year. The number of fatal crashes rose from 1 to 5, which increased the fatal crash rate from 0.18% to 0.68%. While the overall proportion of crashes involving any injury remained stable at approximately 26.5%, the number of persons sustaining possible injuries grew from 105 to 140. Conversely, the share of crashes resulting in serious or minor injuries saw a slight decrease.

Outcome by Severity (Crash Events)

Fatal5fatal crashes0.7%
400.0%prior 1
Serious Injury15serious injury crashes2%
15.4%prior 13
Minor Injury59minor injury crashes8%
13.5%prior 52
Possible Injury116possible injury crashes15.8%
39.8%prior 83
No Injury534no injury crashes72.9%
28.7%prior 415

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2022-01-01 to 2022-01-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2022-01-01 to 2022-01-31 · Most severe injury per crash record

Top Contributing Factors

The top contributing factors in both periods were related to environmental conditions. The count of crashes attributed to "N/A, WET" increased by 100%, from 19 to 38 incidents, making it the top-ranked factor in January 2022. Crashes involving "ICY OR SNOW-COVERED, RAIN, SNOW" increased in count from 12 to 37, while those associated with "RAIN, SNOW, WET" grew from 26 to 32.

Officer-Reported Primary Contributing Cause

N/A, WET38 (5.2%)100.0%prior 19
ICY OR SNOW-COVERED, RAIN, SNOW37 (5%)208.3%prior 12
RAIN, SNOW, WET32 (4.4%)23.1%prior 26
ICY OR SNOW-COVERED, N/A31 (4.2%)138.5%prior 13
ANIMAL, N/A5 (0.7%)
BACKUP DUE TO REGULAR CONGESTION, N/A5 (0.7%)
ICY OR SNOW-COVERED, RAIN, SNOW, SLEET, HAIL, FREEZ. RAIN4 (0.5%)
SLEET, HAIL, FREEZ. RAIN, WET4 (0.5%)
ICY OR SNOW-COVERED, RAIN, SNOW, WET3 (0.4%)
ICY OR SNOW-COVERED, V WIPERS|W OTHER ENVIRONMENTAL3 (0.4%)-40.0%prior 5

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2022-01-01 to 2022-01-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

While clear weather and dry roads remained the most common conditions, their share of total crashes decreased in January 2022 compared to the prior year. Crashes occurring on snowy road surfaces increased from 14 to 42, and incidents on icy or frosty roads rose from 24 to 34. The proportion of crashes happening in darkness on lighted roads increased slightly, from 34.5% in January 2021 to 36.0% in January 2022.

Weather

Clear474 (71.5%)
25.1%prior 379
Cloudy75 (11.3%)
25.0%prior 60
Snow58 (8.7%)
314.3%prior 14
Rain33 (5.0%)
-15.4%prior 39
Wintry Mix10 (1.5%)
11.1%prior 9
Fog, Smog, Smoke4 (0.6%)
Blowing Snow4 (0.6%)
Other3 (0.5%)
-40.0%prior 5
Sleet Or Hail2 (0.3%)
-60.0%prior 5

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2022-01-01 to 2022-01-31 · Weather condition at time of crash

Lighting

Daylight377 (52.3%)
27.8%prior 295
Dark - Lighted264 (36.6%)
35.4%prior 195
Dark - Not Lighted39 (5.4%)
77.3%prior 22
Dawn15 (2.1%)
25.0%prior 12
Dusk12 (1.7%)
-20.0%prior 15
Dark - Unknown Lighting9 (1.2%)
80.0%prior 5
Other5 (0.7%)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2022-01-01 to 2022-01-31 · Lighting condition field

Road Surface

Dry436 (71.0%)
15.6%prior 377
Wet99 (16.1%)
37.5%prior 72
Snow42 (6.8%)
200.0%prior 14
Ice/Frost34 (5.5%)
41.7%prior 24
Water (standing, moving)1 (0.2%)
Other1 (0.2%)
Slush1 (0.2%)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2022-01-01 to 2022-01-31 · Road surface condition field

Vehicles & Demographics

The top three vehicle makes involved in collisions—Toyota, Honda, and Ford—remained the same across both periods, with the count for each increasing in January 2022. Toyota involvements rose from 124 to 167, and Honda involvements grew from 121 to 142. Passenger cars were the most prevalent vehicle type in both years, with their numbers increasing from 643 to 835, followed by (Sport) Utility Vehicles, which rose from 106 to 123.

Top Vehicle Makes (1,264 vehicles)

1
TOYOTA167 (13.2%)
34.7%prior 124
2
HONDA142 (11.2%)
17.4%prior 121
3
FORD114 (9%)
2.7%prior 111
4
TOYT64 (5.1%)
120.7%prior 29
5
NISSAN64 (5.1%)
23.1%prior 52
6
HYUNDAI45 (3.6%)
80.0%prior 25
7
DODGE38 (3%)
31.0%prior 29
8
HOND36 (2.8%)
50.0%prior 24
9
JEEP34 (2.7%)
54.5%prior 22
10
CHEVY30 (2.4%)
30.4%prior 23

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2022-01-01 to 2022-01-31 · 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: 2022-01-01 through 2022-01-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2022-01-01 through 2022-01-31 (31 days)
  • Geographic scope: montgomery, MD
  • Total crash records analyzed: 733
  • Total persons involved: 1,313
  • Total vehicles involved: 1,264

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: January 2022." Published September 9, 2026. Reporting period: 2022-01-01 to 2022-01-31. 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/january-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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