Monthly Traffic Safety Analysis

943 CRASHES IN
MONTGOMERY, MD
MAY 2023

All metrics benchmarked againstMay 2022

In May 2023, Montgomery County recorded 943 total crashes, an 11.7% increase from the 844 crashes reported in May 2022. While total injuries remained unchanged, the most significant year-over-year change was in crash fatalities, which rose from one in the prior period to four in the current period.

943

11.7%was 844

Total Crash Events

4

300.0%was 1

Persons Killed

291

Persons Injured

202

12.8%was 179

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. 2 crashes with unreported severity are not shown in the severity breakdown.

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-05-01 to 2023-05-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash totals in Montgomery County showed an upward trend in May 2023 compared to the same month in the previous year, increasing by 99 incidents from 844 to 943. While the total number of injuries remained unchanged at 291 for both periods, fatalities quadrupled from one to four.

202

Hit-and-Run Crashes — May 2023

12.8% vs prior (179)

The number of hit-and-run incidents increased from 179 in May 2022 to 202 in May 2023, a 12.8% rise. The hit-and-run rate, which measures the percentage of total crashes that were hit-and-runs, remained relatively stable, showing a slight upward trend from 21.2% to 21.4%.

Vulnerable Road User Casualties

2

Pedestrians Killed

Prior: 1100.0%

0

Cyclists Killed

Prior: 00.0%

2

Motorists Killed

Prior: 0%

0

Other Killed

Prior: 00.0%

36

Pedestrians Injured

Prior: 2263.6%

16

Cyclists Injured

Prior: 1060.0%

238

Motorists Injured

Prior: 257-7.4%

1

Other Injured

Prior: 2-50.0%

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-05-01 to 2023-05-31 · 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 some shifts between May 2022 and May 2023. While Tuesday remained the peak day for crashes in both periods (150 and 166 crashes, respectively), the peak hour moved later in the afternoon from 2 p.m. in 2022 (63 crashes) to 4 p.m. in 2023 (87 crashes). Crashes on Mondays and Wednesdays saw notable increases, rising from 118 to 156 and 88 to 151, respectively.

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-05-01 to 2023-05-31 · Crash date field aggregated by weekday

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-05-01 to 2023-05-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity of crashes shifted year-over-year, with a notable increase in fatal incidents. The number of fatal crashes rose from one in May 2022 to four in May 2023, and the fatal crash rate increased from 0.24 to 0.42 per 100 crashes. Conversely, the overall proportion of crashes resulting in any level of injury decreased from 28.9% to 26.6%, while the share of no-injury crashes grew from 70.5% to 72.7%.

Outcome by Severity (Crash Events)

Fatal4fatal crashes0.4%
300.0%prior 1
Serious Injury16serious injury crashes1.7%
14.3%prior 14
Minor Injury107minor injury crashes11.3%
10.3%prior 97
Possible Injury128possible injury crashes13.6%
-3.8%prior 133
No Injury686no injury crashes72.7%
15.3%prior 595

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-05-01 to 2023-05-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-05-01 to 2023-05-31 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factors for crashes changed significantly between May 2022 and May 2023. In the prior period, "RAIN, SNOW, WET" was the top factor, cited in 85 crashes (10.1% share); this factor's count decreased by 64.7% to 30 crashes (3.2% share) in the current period. The most cited factor in May 2023 was "N/A, WET" with 35 crashes, a small decline from 38 crashes in the previous year.

Officer-Reported Primary Contributing Cause

N/A, WET35 (3.7%)-7.9%prior 38
RAIN, SNOW, WET30 (3.2%)-64.7%prior 85
BACKUP DUE TO REGULAR CONGESTION, N/A4 (0.4%)
N/A, RAIN, SNOW4 (0.4%)-42.9%prior 7
SLEET, HAIL, FREEZ. RAIN, WET4 (0.4%)-42.9%prior 7
BACKUP DUE TO NON-RECURRING INCIDENT, N/A3 (0.3%)
ANIMAL, N/A3 (0.3%)-40.0%prior 5
N/A, V EXHAUST SYSTEM|R OTHER ROAD2 (0.2%)
BACKUP DUE TO PRIOR CRASH, N/A2 (0.2%)
N/A, PHYSICAL OBSTRUCTION(S)2 (0.2%)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-05-01 to 2023-05-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The prevalence of adverse weather and road conditions in crashes decreased notably from May 2022 to May 2023. Crashes occurring in rain dropped from 145 to 70, and those on wet roads fell from 175 to 98. Consequently, the share of crashes on dry roads increased from 70.5% (528 crashes) to 74.9% (706 crashes). The proportion of crashes in daylight also rose from 72.0% to 76.2%, corresponding to an increase from 608 to 719 incidents.

Weather

Clear720 (82.9%)
39.8%prior 515
Cloudy76 (8.7%)
-37.7%prior 122
Rain70 (8.1%)
-51.7%prior 145
Fog, Smog, Smoke2 (0.2%)
Other1 (0.1%)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-05-01 to 2023-05-31 · Weather condition at time of crash

Lighting

Daylight719 (77.4%)
18.3%prior 608
Dark - Lighted155 (16.7%)
2.0%prior 152
Dark - Not Lighted22 (2.4%)
-24.1%prior 29
Dawn11 (1.2%)
0.0%prior 11
Dark - Unknown Lighting11 (1.2%)
-15.4%prior 13
Dusk10 (1.1%)
-16.7%prior 12
Other1 (0.1%)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-05-01 to 2023-05-31 · Lighting condition field

Road Surface

Dry706 (87.8%)
33.7%prior 528
Wet98 (12.2%)
-44.0%prior 175

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-05-01 to 2023-05-31 · Road surface condition field

Vehicles & Demographics

The top three vehicle makes involved in crashes—Toyota, Honda, and Ford—retained their rankings from May 2022 to May 2023, with each showing an increase in total counts. Toyota-made vehicles increased from 215 to 231, Hondas from 166 to 189, and Fords from 131 to 156. Notably, the number of school buses involved in collisions rose from 28 to 51, an 82% year-over-year increase.

Top Vehicle Makes (1,666 vehicles)

1
TOYOTA231 (13.9%)
7.4%prior 215
2
HONDA189 (11.3%)
13.9%prior 166
3
FORD156 (9.4%)
19.1%prior 131
4
TOYT92 (5.5%)
26.0%prior 73
5
NISSAN76 (4.6%)
1.3%prior 75
6
HOND62 (3.7%)
8.8%prior 57
7
HYUNDAI47 (2.8%)
42.4%prior 33
8
BMW39 (2.3%)
14.7%prior 34
9
CHEVROLET39 (2.3%)
30.0%prior 30
10
KIA38 (2.3%)
31.0%prior 29

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2023-05-01 to 2023-05-31 · 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: 2023-05-01 through 2023-05-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2023-05-01 through 2023-05-31 (31 days)
  • Geographic scope: montgomery, MD
  • Total crash records analyzed: 943
  • Total persons involved: 1,728
  • Total vehicles involved: 1,666

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: May 2023." Published September 9, 2026. Reporting period: 2023-05-01 to 2023-05-31. 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/may-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

ThatCarHitMe.com · An Injuria.ai Company