Monthly Traffic Safety Analysis

973 CRASHES IN
MONTGOMERY, MD
SEPTEMBER 2023

All metrics benchmarked againstSeptember 2022

In September 2023, Montgomery County recorded 973 total traffic crashes, a 13.0% increase from the 861 crashes documented in September 2022. The total number of people injured also rose by 10.5%, from 325 to 359. The most significant year-over-year change was the increase in fatalities, which rose from zero in the prior period to three in the current period.

973

13.0%was 861

Total Crash Events

3

Persons Killed

359

10.5%was 325

Persons Injured

193

11.6%was 173

Hit-and-Run Crashes

Note: "Persons Killed" (3) counts individual fatalities across all crash events. "Fatal" in the severity table below (3) 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 · 2023-09-01 to 2023-09-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Traffic crashes in Montgomery County showed an upward trend in September 2023 compared to the same month in the previous year. Total crashes increased by 13.0% from 861 to 973. Similarly, the number of people injured in these incidents grew by 10.5%, from 325 to 359.

193

Hit-and-Run Crashes — September 2023

11.6% vs prior (173)

The number of hit-and-run incidents increased from 173 in September 2022 to 193 in September 2023, an 11.6% rise in count. However, as a proportion of all crashes, the hit-and-run rate saw a slight decrease. It fell from 20.1% of all crashes in the prior period to 19.8% in the current period.

Vulnerable Road User Casualties

2

Pedestrians Killed

Prior: 0%

0

Cyclists Killed

Prior: 00.0%

1

Motorists Killed

Prior: 0%

0

Other Killed

Prior: 00.0%

38

Pedestrians Injured

Prior: 2835.7%

16

Cyclists Injured

Prior: 1323.1%

298

Motorists Injured

Prior: 2825.7%

7

Other Injured

Prior: 2250.0%

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-09-01 to 2023-09-30 · 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 some shifts between September 2022 and September 2023. While Friday remained the peak day for crashes in both periods (167 and 186, respectively), the peak hour shifted slightly from 4 p.m. in 2022 to 3 p.m. in 2023. Weekend crashes saw a notable increase, with the combined total for Saturday and Sunday rising from 191 in the prior period to 281 in the current period.

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-09-01 to 2023-09-30 · Crash date field aggregated by weekday

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-09-01 to 2023-09-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity increased in September 2023 compared to the previous year, with three fatal crashes recorded, up from zero in September 2022. Consequently, the fatal crash rate rose from 0% to 0.31%. The count of crashes involving serious injuries also increased from 16 to 23. The overall proportion of crashes involving any type of injury remained stable at approximately 32% across both periods.

Outcome by Severity (Crash Events)

Fatal3fatal crashes0.3%
Serious Injury23serious injury crashes2.4%
43.8%prior 16
Minor Injury129minor injury crashes13.3%
21.7%prior 106
Possible Injury155possible injury crashes15.9%
2.0%prior 152
No Injury659no injury crashes67.7%
13.8%prior 579

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-09-01 to 2023-09-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-09-01 to 2023-09-30 · Most severe injury per crash record

Top Contributing Factors

Environmental conditions were a more prominent contributing factor in crashes this year. The count of crashes where 'RAIN, SNOW, WET' was cited as a factor increased by 167%, from 36 in September 2022 to 96 in September 2023, making it the top contributing factor. Similarly, crashes with 'N/A, WET' as a factor rose by 54% from a count of 37 to 57. These two factors, both related to wet conditions, represented the most significant growth among all cited contributing factors.

Officer-Reported Primary Contributing Cause

RAIN, SNOW, WET96 (9.9%)166.7%prior 36
N/A, WET57 (5.9%)54.1%prior 37
SLEET, HAIL, FREEZ. RAIN, WET7 (0.7%)
N/A, VISION OBSTRUCTION (INCL. BLINDED BY SUN)7 (0.7%)-36.4%prior 11
N/A, RAIN, SNOW6 (0.6%)
DEBRIS OR OBSTRUCTION, N/A3 (0.3%)
BACKUP DUE TO REGULAR CONGESTION, N/A3 (0.3%)-50.0%prior 6
N/A, PHYSICAL OBSTRUCTION(S)2 (0.2%)
BACKUP DUE TO PRIOR CRASH, RAIN, SNOW, WET2 (0.2%)
ANIMAL, N/A2 (0.2%)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-09-01 to 2023-09-30 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in adverse weather and on wet roads increased significantly year-over-year. The number of crashes in the rain rose from 69 in September 2022 to 162 in September 2023, with their share of total crashes increasing from 8.0% to 16.6%. Correspondingly, incidents on wet road surfaces increased from 89 to 193. The proportion of crashes in daylight conditions remained the dominant category for both periods, at 65.4% in 2023 versus 68.2% in 2022.

Weather

Clear661 (73.5%)
-0.8%prior 666
Rain162 (18.0%)
134.8%prior 69
Cloudy74 (8.2%)
42.3%prior 52
Other1 (0.1%)
Fog, Smog, Smoke1 (0.1%)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-09-01 to 2023-09-30 · Weather condition at time of crash

Lighting

Daylight636 (66.5%)
8.3%prior 587
Dark - Lighted223 (23.3%)
21.2%prior 184
Dark - Not Lighted31 (3.2%)
24.0%prior 25
Dusk26 (2.7%)
100.0%prior 13
Dawn21 (2.2%)
0.0%prior 21
Dark - Unknown Lighting18 (1.9%)
5.9%prior 17
Other2 (0.2%)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-09-01 to 2023-09-30 · Lighting condition field

Road Surface

Dry639 (76.5%)
-0.2%prior 640
Wet193 (23.1%)
116.9%prior 89
Other1 (0.1%)
Mud, Dirt, Gravel1 (0.1%)
Water (standing, moving)1 (0.1%)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-09-01 to 2023-09-30 · Road surface condition field

Vehicles & Demographics

Top Vehicle Makes (1,698 vehicles)

1
TOYOTA200 (11.8%)
-13.8%prior 232
2
HONDA173 (10.2%)
4.8%prior 165
3
FORD151 (8.9%)
-0.7%prior 152
4
TOYT116 (6.8%)
56.8%prior 74
5
NISSAN83 (4.9%)
31.7%prior 63
6
HOND72 (4.2%)
46.9%prior 49
7
HYUNDAI52 (3.1%)
23.8%prior 42
8
KIA39 (2.3%)
34.5%prior 29
9
DODGE37 (2.2%)
0.0%prior 37
10
JEEP37 (2.2%)
32.1%prior 28

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-09-01 to 2023-09-30 · 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: 2023-09-01 through 2023-09-30
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2023-09-01 through 2023-09-30 (30 days)
  • Geographic scope: montgomery, MD
  • Total crash records analyzed: 973
  • Total persons involved: 1,767
  • Total vehicles involved: 1,698

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: September 2023." Published September 9, 2026. Reporting period: 2023-09-01 to 2023-09-30. 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/september-2023-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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