Monthly Traffic Safety Analysis

973 CRASHES IN
MONTGOMERY, MD
OCTOBER 2024

All metrics benchmarked againstOctober 2023

In October 2024, Montgomery County recorded 973 total traffic crashes, a 1.8% decrease from the 991 crashes reported in October 2023. While the overall number of crashes remained relatively stable, the most notable year-over-year change was a significant reduction in reported hit-and-run incidents, which fell from 213 to 23.

973

-1.8%was 991

Total Crash Events

4

33.3%was 3

Persons Killed

342

6.9%was 320

Persons Injured

23

-89.2%was 213

Hit-and-Run Crashes

Note: "Persons Killed" (4) counts individual fatalities across all crash events. "Fatal" in the severity table below (4) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 30 crashes with unreported severity are not shown in the severity breakdown.

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-10-01 to 2024-10-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Total crashes in Montgomery County saw a slight decline of 1.8% year-over-year, from 991 in October 2023 to 973 in October 2024. Despite this small decrease in crash volume, the human cost increased, with total fatalities rising from 3 to 4 and total injuries increasing by 6.9% from 320 to 342.

23

Hit-and-Run Crashes — October 2024

-89.2% vs prior (213)

Hit-and-run crashes saw a substantial year-over-year decrease. The number of reported incidents fell from 213 in October 2023 to 23 in October 2024. As a result, the hit-and-run rate as a percentage of all crashes dropped significantly, from 21.5% in the prior period to 2.4% in the current period.

Vulnerable Road User Casualties

2

Pedestrians Killed

Prior: 1100.0%

0

Cyclists Killed

Prior: 00.0%

2

Motorists Killed

Prior: 20.0%

0

Other Killed

Prior: 00.0%

61

Pedestrians Injured

Prior: 4052.5%

13

Cyclists Injured

Prior: 128.3%

265

Motorists Injured

Prior: 2650.0%

3

Other Injured

Prior: 30.0%

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-10-01 to 2024-10-31 · 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. The day with the most crashes changed from Saturday (150 crashes) in the prior year to Wednesday (190 crashes) in the current period. The peak hour for collisions remained within the afternoon commute, shifting slightly from 3 p.m. (82 crashes) in October 2023 to 4 p.m. (75 crashes) in October 2024.

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-10-01 to 2024-10-31 · Crash date field aggregated by weekday

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-10-01 to 2024-10-31 · 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 3 to 4, and the count of serious injury crashes more than doubled, increasing from 15 to 31. Consequently, the share of crashes resulting in serious injuries grew from 1.5% to 3.2% of all incidents, while the proportion of no-injury crashes decreased from 71.2% to 66.2%.

Outcome by Severity (Crash Events)

Fatal4fatal crashes0.4%
33.3%prior 3
Serious Injury31serious injury crashes3.2%
106.7%prior 15
Minor Injury152minor injury crashes15.6%
31.0%prior 116
Possible Injury112possible injury crashes11.5%
-21.7%prior 143
No Injury644no injury crashes66.2%
-8.8%prior 706

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-10-01 to 2024-10-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-10-01 to 2024-10-31 · Most severe injury per crash record

Top Contributing Factors

A direct comparison of top contributing factors is not possible due to changes in data reporting methodology between the two periods. The current period's data focuses on driver actions, with 'Failed to Yield Right-of-Way' being the most cited factor in 75 crashes. In contrast, the prior period's data highlighted environmental conditions, with 'RAIN, SNOW, WET' listed as a factor in 48 crashes.

Officer-Reported Primary Contributing Cause

Failed to Yield Right-of-Way75 (7.7%)
Other Improper Action44 (4.5%)
Followed Too Closely37 (3.8%)
Improper Backing22 (2.3%)
Operated Motor Vehicle in Inattentive, Careless, Negligent, or Erratic Manner20 (2.1%)
Failed to Keep in Proper Lane15 (1.5%)
Too Fast For Conditions13 (1.3%)
Improper Passing13 (1.3%)
Improper Turn10 (1%)
Ran Off Roadway9 (0.9%)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-10-01 to 2024-10-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

A higher proportion of crashes in October 2024 occurred in favorable conditions compared to the same month in 2023. Crashes in clear weather made up 93.1% of the total, up from 72.9% the prior year. Similarly, incidents on dry roads accounted for 82.1% of crashes, compared to 74.2% previously, while the share of crashes on wet roads decreased from 10.6% to 5.0%.

Weather

Clear906 (93.5%)
25.5%prior 722
Rain37 (3.8%)
-59.8%prior 92
Cloudy26 (2.7%)
-69.4%prior 85

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-10-01 to 2024-10-31 · Weather condition at time of crash

Lighting

Daylight643 (66.3%)
9.2%prior 589
Dark - Lighted231 (23.8%)
-15.7%prior 274
Dark - Not Lighted51 (5.3%)
24.4%prior 41
Dusk18 (1.9%)
-28.0%prior 25
Dawn16 (1.6%)
-42.9%prior 28
Dark - Unknown Lighting8 (0.8%)
-52.9%prior 17
Other3 (0.3%)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-10-01 to 2024-10-31 · Lighting condition field

Road Surface

Dry799 (93.7%)
8.7%prior 735
Wet49 (5.7%)
-53.3%prior 105
Other4 (0.5%)
Water (standing, moving)1 (0.1%)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-10-01 to 2024-10-31 · Road surface condition field

Vehicles & Demographics

The top vehicle makes involved in crashes, Toyota and Honda, remained consistent across both periods. A notable shift occurred in the types of vehicles involved; the number of passenger cars decreased from 1,200 to 1,106, while the number of Sport Utility Vehicles involved in crashes increased from 192 to 277.

Top Vehicle Makes (1,722 vehicles)

1
TOYOTA337 (19.6%)
56.7%prior 215
2
HONDA254 (14.8%)
58.8%prior 160
3
FORD172 (10%)
19.4%prior 144
4
NISSAN92 (5.3%)
9.5%prior 84
5
CHEVROLET91 (5.3%)
203.3%prior 30
6
HYUNDAI61 (3.5%)
52.5%prior 40
7
LEXUS50 (2.9%)
51.5%prior 33
8
BMW46 (2.7%)
43.8%prior 32
9
SUBARU42 (2.4%)
90.9%prior 22
10
THOMAS BUILT42 (2.4%)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-10-01 to 2024-10-31 · 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: 2024-10-01 through 2024-10-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2024-10-01 through 2024-10-31 (31 days)
  • Geographic scope: montgomery, MD
  • Total crash records analyzed: 973
  • Total persons involved: 1,812
  • Total vehicles involved: 1,722

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: October 2024." Published September 9, 2026. Reporting period: 2024-10-01 to 2024-10-31. 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/october-2024-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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