ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · MONTGOMERY, MD · MAY 2024
Purpose: Machine-readable JSON endpoint for AI agents, LLMs, researchers, and programmatic consumers. Returns all underlying crash data and AI-generated commentary without HTML.
Authentication: None required. Public endpoint.
GET: https://thatcarhitme.com/api/crash-data/reports/data/maryland/statewide/may-2024-report
Monthly Traffic Safety Analysis
957 CRASHES IN
MONTGOMERY, MD
MAY 2024
In May 2024, Montgomery County recorded 957 traffic crashes, a 1.5% increase from the 943 crashes reported in May 2023. While the total number of crashes remained relatively stable, the most significant year-over-year change was a substantial decrease in reported hit-and-run incidents, which fell from 202 to 22. The number of injuries rose from 291 to 332, while fatalities decreased from 4 to 2.
957
▲ 1.5%was 943
Total Crash Events
2
▼ -50.0%was 4
Persons Killed
332
▲ 14.1%was 291
Persons Injured
22
▼ -89.1%was 202
Hit-and-Run Crashes
Note: "Persons Killed" (2) 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. 41 crashes with unreported severity are not shown in the severity breakdown.
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-05-01 to 2024-05-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
Year-over-year, the total number of crashes in Montgomery County remained stable, increasing by 1.5% from 943 in May 2023 to 957 in May 2024. However, the number of people injured in these crashes rose by 14.1% to 332. In a positive trend, the number of fatalities was halved, decreasing from 4 to 2.
22
Hit-and-Run Crashes — May 2024
▼ -89.1% vs prior (202)
There was a significant year-over-year decrease in hit-and-run incidents. The number of hit-and-run crashes fell by 89.1%, from 202 in May 2023 to 22 in May 2024. Correspondingly, the hit-and-run rate dropped from 21.4% of all crashes in the prior period to 2.3% in the current period.
Vulnerable Road User Casualties
1
Pedestrians Killed
0
Cyclists Killed
1
Motorists Killed
0
Other Killed
30
Pedestrians Injured
14
Cyclists Injured
284
Motorists Injured
4
Other Injured
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-05-01 to 2024-05-31 · 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 a shift in the peak day of the week, moving from Tuesday (166 crashes) in May 2023 to Friday (176 crashes) in May 2024. The peak hour for collisions remained consistent at 4 p.m. in both periods, with 87 crashes in the prior year and 84 in the current year. Crashes during the 3 p.m. hour also saw a notable increase from 72 to 84.
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-05-01 to 2024-05-31 · Crash date field aggregated by weekday
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-05-01 to 2024-05-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
Crash severity saw a mixed trend year-over-year. While the number of fatalities decreased from 4 to 2 and the fatal crash rate dropped from 0.42% to 0.31%, the proportion of crashes resulting in minor or serious injuries increased. Crashes involving serious injuries rose from 1.7% to 2.1% of all incidents, and minor injury crashes increased from 11.3% to 16.7% of the total.
Severity is per crash event (most severe injury). 3 fatal crash events resulted in 2 persons killed.
Outcome by Severity (Crash Events)
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-05-01 to 2024-05-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-05-01 to 2024-05-31 · Most severe injury per crash record
Top Contributing Factors
A direct year-over-year comparison of contributing factors is not possible due to a significant change in how this data was categorized between the two periods. In May 2023, the top reported factors were related to environmental and traffic conditions, such as 'WET' (35 crashes). In contrast, the May 2024 data focuses on driver actions, with 'Failed to Yield Right-of-Way' (97 crashes, 10.1% share) and 'Other Improper Action' (51 crashes, 5.3% share) as the leading causes.
Officer-Reported Primary Contributing Cause
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-05-01 to 2024-05-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes in May 2024 occurred more frequently in adverse weather compared to the previous year. The number of crashes on wet roads more than doubled, increasing from 98 to 230, and collisions during rainfall rose from 70 to 189. Consequently, the share of crashes on wet surfaces grew from 10.4% in May 2023 to 24.0% in May 2024. The distribution of crashes by lighting conditions remained largely stable, with most incidents in both periods occurring during daylight.
Weather
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-05-01 to 2024-05-31 · Weather condition at time of crash
Lighting
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-05-01 to 2024-05-31 · Lighting condition field
Road Surface
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-05-01 to 2024-05-31 · Road surface condition field
Vehicles & Demographics
The top vehicle makes involved in crashes, Toyota and Honda, remained consistent year-over-year, with 330 and 245 vehicles respectively in May 2024, compared to a combined 323 and 251 in May 2023. A notable shift occurred in vehicle types, where the number of Sport Utility Vehicles involved in crashes increased by 65.9% from 182 to 302. Conversely, the involvement of Passenger Cars saw a slight decrease from 1,117 to 1,047 vehicles.
Top Vehicle Makes (1,673 vehicles)
Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2024-05-01 to 2024-05-31 · 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: 2024-05-01 through 2024-05-31
- Report generated: September 9, 2026
Data Coverage
- Reporting period: 2024-05-01 through 2024-05-31 (31 days)
- Geographic scope: montgomery, MD
- Total crash records analyzed: 957
- Total persons involved: 1,734
- Total vehicles involved: 1,673
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 2024." Published September 9, 2026. Reporting period: 2024-05-01 to 2024-05-31. 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-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
ThatCarHitMe.com · An Injuria.ai Company
ThatCarHitMe.com
An Injuria.ai Company
Crash Data Intelligence
Data: Montgomery County Crash Reporting (ACRS) · Socrata
Period: 2024-05-01 – 2024-05-31
Generated: September 9, 2026 · All rights reserved