Monthly Traffic Safety Analysis

692 CRASHES IN
MONTGOMERY, MD
NOVEMBER 2020

In November 2020, Montgomery County recorded 692 traffic crashes, resulting in 7 fatalities and 252 injuries. Analysis of the crash data reveals that same-direction rear-end collisions were the most frequent type of incident, comprising 23.4% of the total. The majority of crashes (68.4%) resulted in no injuries.

692

Total Crash Events

7

Persons Killed

252

Persons Injured

19.9%

Hit-and-Run Rate

Note: "Persons Killed" (7) counts individual fatalities across all crash events. "Fatal" in the severity table below (7) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 6 crashes with unreported severity are not shown in the severity breakdown.

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2020-11-01 to 2020-11-30 · Aggregate counts from crash, person, and vehicle records

138

Hit-and-Run Crashes — November 2020

According to initial officer reports, 138 crashes, representing 19.9% of the total for the month, involved a hit-and-run. This designation is based on the responding officer's determination at the scene that a driver involved in the crash had left without providing required information.

Vulnerable Road User Casualties

During this period, 7 individuals were killed and 252 were injured in traffic crashes. Motorists accounted for the largest number of casualties, with 4 killed and 221 injured. Vulnerable road users were also significantly impacted, with 2 pedestrians killed and 21 injured, and 9 cyclists injured.

2

Pedestrians Killed

0

Cyclists Killed

4

Motorists Killed

1

Other Killed

21

Pedestrians Injured

9

Cyclists Injured

221

Motorists Injured

1

Other Injured

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2020-11-01 to 2020-11-30 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

Crash incidents were most frequent on Mondays, which saw 122 crashes during the month. The single hour with the most crashes was the 5 PM hour, with 66 incidents. Crashes generally increased from the morning commute, peaking during the afternoon and evening hours before declining after 8 PM.

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2020-11-01 to 2020-11-30 · Crash date field aggregated by weekday

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2020-11-01 to 2020-11-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Of the 692 crashes reported, 68.4% (473 incidents) resulted in no injuries and were classified as property-damage-only. The remaining 31.6% of crashes involved some level of injury, including 7 fatal crashes. These 7 crashes resulted in a total of 7 fatalities, as recorded in the person-level data.

Outcome by Severity (Crash Events)

Fatal7fatal crashes1%
Serious Injury16serious injury crashes2.3%
Minor Injury78minor injury crashes11.3%
Possible Injury112possible injury crashes16.2%
No Injury473no injury crashes68.4%

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2020-11-01 to 2020-11-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2020-11-01 to 2020-11-30 · Most severe injury per crash record

Top Contributing Factors

The most commonly cited contributing factor was related to weather and road conditions, with 'RAIN, SNOW, WET' listed in 45 crashes (6.5%). A wet road surface, denoted as 'N/A, WET', was a factor in another 23 incidents (3.3%). The presence of an animal on the roadway was noted as a factor in 10 crashes.

Officer-Reported Primary Contributing Cause

RAIN, SNOW, WET45 (6.5%)
N/A, WET23 (3.3%)
ANIMAL, N/A10 (1.4%)
N/A, VISION OBSTRUCTION (INCL. BLINDED BY SUN)4 (0.6%)
N/A, ROAD UNDER CONSTRUCTION/MAINTENANCE3 (0.4%)
BACKUP DUE TO REGULAR CONGESTION, N/A1 (0.1%)
N/A, SMOG, SMOKE1 (0.1%)
N/A, PHYSICAL OBSTRUCTION(S)1 (0.1%)
SLEET, HAIL, FREEZ. RAIN, WET1 (0.1%)
BACKUP DUE TO NON-RECURRING INCIDENT, ROAD UNDER CONSTRUCTION/MAINTENANCE, VISION OBSTRUCTION (INCL. BLINDED BY SUN)1 (0.1%)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2020-11-01 to 2020-11-30 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The majority of crashes occurred in favorable conditions, with 74% happening in clear weather and 75.3% on dry road surfaces. Regarding lighting, 339 crashes (49%) occurred during daylight hours. Crashes in adverse conditions included 68 in rain and 80 on wet roads.

Weather

Clear512 (80.3%)
Rain68 (10.7%)
Cloudy51 (8.0%)
Severe Crosswinds4 (0.6%)
Fog, Smog, Smoke2 (0.3%)
Other1 (0.2%)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2020-11-01 to 2020-11-30 · Weather condition at time of crash

Lighting

Daylight339 (49.5%)
Dark - Lighted249 (36.4%)
Dark - Not Lighted49 (7.2%)
Dusk22 (3.2%)
Dawn16 (2.3%)
Dark - Unknown Lighting9 (1.3%)
Other1 (0.1%)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2020-11-01 to 2020-11-30 · Lighting condition field

Road Surface

Dry521 (86.4%)
Wet80 (13.3%)
Mud, Dirt, Gravel1 (0.2%)
Other1 (0.2%)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2020-11-01 to 2020-11-30 · Road surface condition field

Vehicles & Demographics

Among the 1,157 vehicles involved in crashes, passenger cars were the most common type, accounting for 816 vehicles. The most frequently involved vehicle makes were Toyota (158), Honda (133), and Ford (129). Data on driver age demographics was not available for this reporting period.

Top Vehicle Makes (1,157 vehicles)

1
TOYOTA158 (13.7%)
2
HONDA133 (11.5%)
3
FORD129 (11.1%)
4
NISSAN57 (4.9%)
5
TOYT51 (4.4%)
6
CHEVROLET37 (3.2%)
7
DODGE36 (3.1%)
8
CHEV30 (2.6%)
9
JEEP29 (2.5%)
10
HOND29 (2.5%)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2020-11-01 to 2020-11-30 · Vehicle unit records

At-Fault Party

In crashes where fault was assigned, a driver was determined to be at fault in 622 cases. This represents the vast majority of incidents where fault was specified. A non-motorist was found to be at fault in 13 crashes.

Intersection Type

When crashes occurred at intersections with a defined geometry, four-way intersections were the most common location, accounting for 212 incidents. T-intersections were the second most common site, with 86 crashes. These two types represent the majority of crashes at specified intersection geometries.

Junction Type

Analysis of crash locations shows that incidents occurred both at and away from junctions. A total of 227 crashes occurred directly within an intersection, with an additional 73 classified as intersection-related. Crashes on non-intersection roadway segments accounted for 183 incidents.

Junction Type

"Other" combines 2 smaller categories (4 records): OTHER (3), OTHER DRIVEWAY (1).

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2020-11-01 to 2020-11-30 · Crash-level records

Roadway Division

Crashes were most frequently reported on two-way roads with a divided, positive median barrier, accounting for 270 incidents. The second most common road type was two-way, undivided roads, where 226 crashes occurred. An additional 81 crashes happened on two-way roads with an unprotected painted median.

Roadway Division

1
TWO-WAY, DIVIDED, POSITIVE MEDIAN BARRIER270 (44.3%)
2
TWO-WAY, NOT DIVIDED226 (37%)
3
TWO-WAY, DIVIDED, UNPROTECTED PAINTED MIN 4 FEET81 (13.3%)
4
ONE-WAY TRAFFICWAY24 (3.9%)
5
OTHER8 (1.3%)
6
TWO-WAY, NOT DIVIDED WITH A CONTINUOUS LEFT TURN1 (0.2%)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2020-11-01 to 2020-11-30 · Crash-level records

Vehicle Damage Extent

Vehicle damage was reported as 'Disabling' for 476 vehicles, making it the most common damage category recorded. This was followed by 'Functional' damage in 267 cases and 'Superficial' damage in 263 cases. A total of 63 vehicles were reported as 'Destroyed'.

Vehicle Damage Extent

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2020-11-01 to 2020-11-30 · Vehicle unit records

Driver Action / Circumstance

The data field for driver action primarily lists environmental and road conditions rather than specific driver behaviors. The most frequent entry was 'RAIN, SNOW, WET', associated with 66 drivers. This was followed by 'N/A, WET' for 41 drivers and 'ANIMAL, N/A' for 10 drivers.

Driver Action / Circumstance

1
RAIN, SNOW, WET66 (42.6%)
2
N/A, WET41 (26.5%)
3
ANIMAL, N/A10 (6.5%)
4
N/A, VISION OBSTRUCTION (INCL. BLINDED BY SUN)8 (5.2%)
5
N/A, ROAD UNDER CONSTRUCTION/MAINTENANCE5 (3.2%)
6
N/A, SEVERE CROSSWINDS4 (2.6%)
7
DEBRIS OR OBSTRUCTION, RAIN, SNOW, WET3 (1.9%)
8
N/A, RAIN, SNOW2 (1.3%)
9
BACKUP DUE TO NON-RECURRING INCIDENT, ROAD UNDER CONSTRUCTION/MAINTENANCE, VISION OBSTRUCTION (INCL. BLINDED BY SUN)2 (1.3%)

Showing top 9 of 20 reported. 11 additional (14 total) not shown: BACKUP DUE TO REGULAR CONGESTION, N/A, N/A, PHYSICAL OBSTRUCTION(S), N/A, RUTS, HOLES, BUMPS, N/A, NON-HIGHWAY WORK, N/A, SMOG, SMOKE, DEBRIS OR OBSTRUCTION, SEVERE CROSSWINDS, V EXHAUST SYSTEM|R OTHER ROAD, N/A, V WIPERS|W OTHER ENVIRONMENTAL, DEBRIS OR OBSTRUCTION, N/A, RAIN, SNOW, SLEET, HAIL, FREEZ. RAIN, WET, ANIMAL, RAIN, SNOW, WET, SLEET, HAIL, FREEZ. RAIN, WET.

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2020-11-01 to 2020-11-30 · Person-level records linked to crash events

Driver Distraction

Among drivers for whom distraction status was recorded, a specific distraction was identified for 232 drivers. The most common distraction noted was 'Looked But Did Not See', reported for 138 drivers. This was followed by 'Inattentive or Lost in Thought' for 40 drivers and 'Other Distraction' for 27 drivers.

Driver Distraction

1
NOT DISTRACTED696 (74.9%)
2
LOOKED BUT DID NOT SEE138 (14.9%)
3
INATTENTIVE OR LOST IN THOUGHT40 (4.3%)
4
OTHER DISTRACTION27 (2.9%)
5
DISTRACTED BY OUTSIDE PERSON OBJECT OR EVENT7 (0.8%)
6
BY OTHER OCCUPANTS6 (0.6%)
7
OTHER CELLULAR PHONE RELATED5 (0.5%)
8
EATING OR DRINKING4 (0.4%)
9
OTHER ELECTRONIC DEVICE (NAVIGATIONAL PALM PILOT)2 (0.2%)

Showing top 9 of 13 reported. 4 additional (4 total) not shown: BY MOVING OBJECT IN VEHICLE, SMOKING RELATED, TALKING OR LISTENING TO CELLULAR PHONE, ADJUSTING AUDIO AND OR CLIMATE CONTROLS.

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2020-11-01 to 2020-11-30 · Person-level records linked to crash events

First Harmful Event

The most common first harmful event in a crash was a collision with another motor vehicle, which occurred in 430 incidents, or 62.1% of all crashes. The second most frequent event was striking a fixed object, which happened in 103 crashes. Collisions with parked vehicles were the third most common event, with 60 occurrences.

First Harmful Event

1
OTHER VEHICLE430 (62.8%)
2
FIXED OBJECT103 (15%)
3
PARKED VEHICLE60 (8.8%)
4
PEDESTRIAN24 (3.5%)
5
ANIMAL21 (3.1%)
6
BICYCLE12 (1.8%)
7
OTHER OBJECT10 (1.5%)
8
OFF ROAD10 (1.5%)
9
OTHER4 (0.6%)

Showing top 9 of 16 reported. 7 additional (11 total) not shown: OTHER NON COLLISION, BACKING, OTHER CONVEYANCE, OVERTURN, FELL JUMPED FROM MOTOR VEHICLE, UNITS SEPARATED, DOWNHILL RUNAWAY.

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2020-11-01 to 2020-11-30 · Crash-level records

Non-Motorist Safety Equipment

For non-motorists involved in crashes, safety equipment usage was noted in a limited number of cases. A helmet was reported as being used in 9 instances, while reflective clothing was noted in 2 cases. The data does not comprehensively report the absence of safety equipment for all non-motorists.

Non-Motorist Safety Equipment

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2020-11-01 to 2020-11-30 · Non-motorist records linked to crash events

Point of Impact

The front of the vehicle, designated as 'Twelve Oclock', was the most common point of impact, involved in 472 instances. This represents 40.8% of all vehicle impacts recorded. The rear of the vehicle ('Six Oclock') was the second most frequent impact point, with 190 occurrences.

Point of Impact

"Other" combines 8 smaller categories (126 records): EIGHT OCLOCK (27), FIVE OCLOCK (26), SEVEN OCLOCK (25), NINE OCLOCK (20), FOUR OCLOCK (14), UNDERSIDE (7), ROOF TOP (5), NON-COLLISION (2).

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2020-11-01 to 2020-11-30 · Vehicle unit records

Pre-Crash Driver Action

Prior to the collision, the most common driver action was 'Moving Constant Speed', reported for 519 vehicles, or 44.9% of all vehicles with a recorded action. The next most frequent actions were 'Slowing or Stopping' (127 vehicles) and 'Making Left Turn' (118 vehicles).

Pre-Crash Driver Action

1
MOVING CONSTANT SPEED519 (45.8%)
2
SLOWING OR STOPPING127 (11.2%)
3
MAKING LEFT TURN118 (10.4%)
4
STOPPED IN TRAFFIC LANE95 (8.4%)
5
ACCELERATING59 (5.2%)
6
BACKING40 (3.5%)
7
MAKING RIGHT TURN39 (3.4%)
8
STARTING FROM LANE39 (3.4%)
9
CHANGING LANES23 (2%)

Showing top 9 of 20 reported. 11 additional (75 total) not shown: PARKED, PARKING, STARTING FROM PARKED, OTHER, MAKING U TURN, PASSING, SKIDDING, ENTERING TRAFFIC LANE, LEAVING TRAFFIC LANE, NEGOTIATING A CURVE, RIGHT TURN ON RED.

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2020-11-01 to 2020-11-30 · Vehicle unit records

Pedestrian/Cyclist Action

For pedestrians involved in crashes, 'No Improper Actions' was the most frequently recorded circumstance, noted in 22 cases. However, improper actions were also cited, including 'In Roadway Improperly' in 6 cases, and 'Dart Dash' and 'Failure to Yield Right of Way' in 2 cases each.

Pedestrian/Cyclist Action

1
NO IMPROPER ACTIONS22 (59.5%)
2
IN ROADWAY IMPROPERLY6 (16.2%)
3
DART DASH2 (5.4%)
4
FAILURE TO YIELD RIGHT OF WAY2 (5.4%)
5
INATTENTIVE2 (5.4%)
6
NOT VISIBLE1 (2.7%)
7
OTHER1 (2.7%)
8
FAILURE TO OBEY TRAFFIC SIGNS SIGNALS OR OFFICER1 (2.7%)

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2020-11-01 to 2020-11-30 · Non-motorist records linked to crash events

Manner of Collision

The most frequent type of crash was a same-direction rear-end collision, which accounted for 162 incidents, or 23.4% of all crashes. Single-vehicle crashes were the second most common type with 150 occurrences (21.7%). Straight movement angle crashes were third, representing 99 incidents (14.3%).

Manner of Collision

"Other" combines 9 smaller categories (62 records): SAME DIRECTION RIGHT TURN (15), OPPOSITE DIRECTION SIDESWIPE (13), SAME DIRECTION LEFT TURN (10), ANGLE MEETS LEFT HEAD ON (8), ANGLE MEETS RIGHT TURN (5), SAME DIR REND RIGHT TURN (4), ANGLE MEETS LEFT TURN (4), SAME DIR BOTH LEFT TURN (2), SAME DIR REND LEFT TURN (1).

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2020-11-01 to 2020-11-30 · Crash-level records

Traffic Control Device

A significant portion of crashes, 304 incidents or 43.9% of the total, occurred at locations with no traffic controls present. Crashes at locations with traffic signals accounted for 210 incidents. An additional 41 crashes occurred at intersections controlled by a stop sign.

Traffic Control Device

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2020-11-01 to 2020-11-30 · Crash-level records

Vehicle Type

Passenger cars were the predominant vehicle type involved in crashes, accounting for 816 of the 1,157 vehicles (70.5%). Sport Utility Vehicles were the second most common type with 98 vehicles involved. Commercial vehicles also contributed, including 27 transit buses and 21 cargo vans.

Vehicle Type

"Other" combines 14 smaller categories (62 records): OTHER LIGHT TRUCKS (10,000LBS (4,536KG) OR LESS) (13), MOTORCYCLE (13), OTHER (8), STATION WAGON (6), POLICE VEHICLE/EMERGENCY (5), MEDIUM/HEAVY TRUCKS 3 AXLES (OVER 10,000LBS (4,536 (5), AMBULANCE/EMERGENCY (4), TRUCK TRACTOR (2), AUTOCYCLE (1), FIRE VEHICLE/NON EMERGENCY (1), FIRE VEHICLE/EMERGENCY (1), ALL TERRAIN VEHICLE (ATV) (1), OTHER BUS (1), AMBULANCE/NON EMERGENCY (1).

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2020-11-01 to 2020-11-30 · Vehicle unit records

Person Injury Severity

Out of 1,199 people involved in crashes, 940 were reported with no injuries. A total of 7 individuals sustained fatal injuries (K) and 19 sustained suspected serious injuries (A). Combined, fatal and serious injuries accounted for 2.2% of all persons involved.

Person Injury Severity

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2020-11-01 to 2020-11-30 · Crash-level records

Vehicles Per Crash

The majority of crashes, 387 incidents (56%), involved two vehicles. Single-vehicle crashes were also common, accounting for 260 incidents (37.6%). Multi-vehicle crashes involving three or more vehicles were less frequent, with 33 crashes involving three vehicles and 6 crashes involving four vehicles.

Vehicles Per Crash

Source: Montgomery County Crash Reporting (ACRS) · Socrata Open Data · 2020-11-01 to 2020-11-30 · Crash-level 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: 2020-11-01 through 2020-11-30
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2020-11-01 through 2020-11-30 (30 days)
  • Geographic scope: montgomery, MD
  • Total crash records analyzed: 692
  • Total persons involved: 1,199
  • Total vehicles involved: 1,157

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: November 2020." Published September 9, 2026. Reporting period: 2020-11-01 to 2020-11-30. 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/november-2020-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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