Final Project Introduction

Team Member (1)

Tan Nguyen

Introduction : Unveiling Patterns in Montgomery County’s Crashes: A Look at Driver Data

The “Crash Report Drivers Data of Montgomery County, Maryland” dataset offers a comprehensive view of motor vehicle operators involved in traffic collisions across the county’s roadways. Compiled through the Automated Crash Reporting System (ACRS) managed by the Maryland State Police and reported by various law enforcement agencies including the Montgomery County Police, Gaithersburg Police, Rockville Police, and the Maryland-National Capital Park Police, this dataset encapsulates detailed information pertaining to each recorded collision and the drivers implicated. The dataset can be downloaded at https://catalog.data.gov/dataset/crash-reporting-drivers-data.
Important features:
- ACRS.Report.Type : Fatal Crash, Injury Crash, Property Damage Crash
- Crash.Date.Time & Light : date time and light condition
- Route.Type, Cross.Street.Type, Traffic Control : specific location and traffic control
- Weather & Surface Condition
- Collision Type
- Driver Substance Abuse : Alcohol, Medication, Illegal Drug
- Injury Severity :Fatal Injury, No Injury, Possible Injury, Suspected Minor Injury, Suspected Serious Injury
- Circumstance (140746/172105 fields has N/A data)
- Driver Distracted By : phone activity or other distract (many categories, have to grouping or aggregating categories, or using Top-N Analysis)
- Driver License State
What’s interesting in that data set and some challenges:
- This data set has a lot of informations, maybe by delving into this data set, we can uncover the trends and relationships between various factors that contribute to collisions. The exploration can provide crucial insights for improving road safety within Montgomery County
- Challenges:
  - Some features have a lot of categories, some features have a lot of N/A information, also These collision reports are based on preliminary information, so information may include verified and unverified collision data, information may include mechanical or human error.
  - Use some techniques to deal with a lot of categories such as: Grouping, Feature Engineering (derive to new feature)

data = read.csv("Crash_Reporting.csv")
glimpse(data)

## Rows: 172,105
## Columns: 43
## $ Report.Number                  <chr> "MCP3170003V", "MCP3254003K", "EJ788700…
## $ Local.Case.Number              <chr> "240000438", "230072050", "230074270", …
## $ Agency.Name                    <chr> "Montgomery County Police", "Montgomery…
## $ ACRS.Report.Type               <chr> "Property Damage Crash", "Injury Crash"…
## $ Crash.Date.Time                <chr> "01/03/2024 02:55:00 PM", "12/16/2023 1…
## $ Route.Type                     <chr> "", "Maryland (State)", "Maryland (Stat…
## $ Road.Name                      <chr> "", "GERMANTOWN RD", "GREAT SENECA HWY"…
## $ Cross.Street.Type              <chr> "", "County", "Municipality", "County",…
## $ Cross.Street.Name              <chr> "", "MIDDLEBROOK RD", "KENTLANDS BLVD",…
## $ Off.Road.Description           <chr> "IN FRONT OF 18900 BIRDSEYE DR", "", ""…
## $ Municipality                   <chr> "", "N/A", "GAITHERSBURG", "N/A", "N/A"…
## $ Related.Non.Motorist           <chr> "", "BICYCLIST", "", "", "", "", "PEDES…
## $ Collision.Type                 <chr> "OPPOSITE DIRECTION SIDESWIPE", "STRAIG…
## $ Weather                        <chr> "CLOUDY", "CLEAR", "CLEAR", "CLEAR", "R…
## $ Surface.Condition              <chr> "", "DRY", "DRY", "DRY", "WET", "DRY", …
## $ Light                          <chr> "DAYLIGHT", "DAYLIGHT", "DAYLIGHT", "DA…
## $ Traffic.Control                <chr> "NO CONTROLS", "TRAFFIC SIGNAL", "TRAFF…
## $ Driver.Substance.Abuse         <chr> "NONE DETECTED", "NONE DETECTED", "NONE…
## $ Non.Motorist.Substance.Abuse   <chr> "", "NONE DETECTED", "", "", "", "", "N…
## $ Person.ID                      <chr> "ACC015E9-08A4-4856-866E-0004005F986C",…
## $ Driver.At.Fault                <chr> "Yes", "No", "No", "No", "Yes", "Yes", …
## $ Injury.Severity                <chr> "NO APPARENT INJURY", "NO APPARENT INJU…
## $ Circumstance                   <chr> "N/A", "N/A", "N/A", "ANIMAL, N/A", "RA…
## $ Driver.Distracted.By           <chr> "LOOKED BUT DID NOT SEE", "NOT DISTRACT…
## $ Drivers.License.State          <chr> "MD", "MD", "MD", "MD", "MD", "MD", "MD…
## $ Vehicle.ID                     <chr> "4E492574-893B-4EB1-ADCA-53FDD633D6C4",…
## $ Vehicle.Damage.Extent          <chr> "FUNCTIONAL", "FUNCTIONAL", "FUNCTIONAL…
## $ Vehicle.First.Impact.Location  <chr> "SEVEN OCLOCK", "ELEVEN OCLOCK", "SIX O…
## $ Vehicle.Second.Impact.Location <chr> "SEVEN OCLOCK", "ELEVEN OCLOCK", "SIX O…
## $ Vehicle.Body.Type              <chr> "PASSENGER CAR", "PASSENGER CAR", "(SPO…
## $ Vehicle.Movement               <chr> "MOVING CONSTANT SPEED", "MOVING CONSTA…
## $ Vehicle.Continuing.Dir         <chr> "South", "North", "South", "South", "No…
## $ Vehicle.Going.Dir              <chr> "South", "West", "South", "South", "Nor…
## $ Speed.Limit                    <int> 0, 35, 35, 40, 20, 35, 35, 10, 35, 0, 2…
## $ Driverless.Vehicle             <chr> "No", "No", "No", "No", "No", "No", "No…
## $ Parked.Vehicle                 <chr> "No", "No", "No", "No", "No", "No", "No…
## $ Vehicle.Year                   <int> 2017, 2010, 2021, 2019, 2014, 1991, 201…
## $ Vehicle.Make                   <chr> "LEXUS", "TOYT", "SUBARU", "DODGE", "NI…
## $ Vehicle.Model                  <chr> "SUV", "PRIUS", "FORRESTER", "CHARGER",…
## $ Equipment.Problems             <chr> "NO MISUSE", "NO MISUSE", "NO MISUSE", …
## $ Latitude                       <dbl> 39.16500, 39.17878, 39.12357, 39.21174,…
## $ Longitude                      <dbl> -77.24931, -77.26719, -77.23177, -77.17…
## $ Location                       <chr> "(39.16500483, -77.24931)", "(39.178775…

Question to answer

Identify Trends:

Seasonal Variations: Look for patterns related to the date. Are there more crashes during specific times of the year (e.g., winter, night times)?
Weather Conditions: Correlate weather data with crashes. Does rain, snow, or fog increase the frequency of accidents?
Collision Type and Location: Analyze the distribution of collision types (rear-end, head-on, etc.) across different locations. Are certain intersections or road types more prone to specific types of crashes?
Traffic Control and Violations: See if disregarding traffic signals or signage plays a role in crashes.
Vehicle Movement and Speed: Investigate if speeding or unsafe maneuvers (like higher speed limit) are contributing factors.
Surface Condition: Identify if poor road conditions (wet, icy, potholes) are linked to crashes.

Driver Behavior:

Distraction Analysis: Understand how different types of distractions (phone use, eating, etc.) affect crash risk.
Impaired Driving: See if the data suggests a correlation between crashes and factors like driving under the influence of alcohol or drugs.

Data Visualization:

Create charts and graphs to effectively represent the relationships between different factors and crash occurrences.
Use heat map to visualize high-risk locations or specific times of day with a higher crash frequency.

Outcome: Focus on Safety Improvements:

Use the analysis to identify areas for improvement in infrastructure, traffic management, and driver education programs.
Target interventions (e.g., public awareness campaigns) towards specific types of driver distractions or risky behaviors.

Future work

Based on the identified trends, you can potentially build a model to predict high-risk situations based on factors like weather, time of day, and location. After finish class DATA 4319 (maybe)
External Data Integration: Consider incorporating external data sources like traffic volume or road closure information to enrich the analysis.
Data Quality: Ensure the data in the crash reports is accurate and complete for reliable analysis.

# Convert "Crash Date/Time" column to Date type
data$Crash_Date <- as.POSIXct(data$Crash.Date.Time, format = "%m/%d/%Y %I:%M:%S %p")

# Extract year from "Crash Date/Time" column
data$Year <- lubridate::year(data$Crash_Date)

# distinct_years <- data %>%
#   distinct(Year)
# 
# distinct_years <- distinct_years$Year %>% sort()
# 
# print(distinct_years)

# Group by year and count the number of injuries
injury_counts <- data %>%
  group_by(Year, Injury.Severity) %>%
  summarise(Count = n()) %>%
  filter(Year != 2024)

## `summarise()` has grouped output by 'Year'. You can override using the
## `.groups` argument.

# Plot the bar graph
ggplot(injury_counts, aes(x = factor(Year), y = Count)) +
  geom_bar(stat = "identity") +
  labs(x = "Year", y = "Total Number of Injuries", title = "Total Injuries by Year")

# # Plot the bar graph with a trend line
# ggplot(injury_counts, aes(x = Year, y = Count)) +
#   geom_bar(stat = "identity") +
#   geom_smooth(method = "loess", se = FALSE, color = "red") +  # Add a linear trend line
#   labs(x = "Year", y = "Total Number of Injuries", title = "Total Injuries by Year with Trend Line") +
#   theme_minimal() +
#   theme(axis.text.x = element_text(angle = 45, hjust = 1))

# Plot by geom_line
ggplot(injury_counts, aes(x = Year, y = Count, color = Injury.Severity)) +
  geom_line() +
  labs(x = "Year", y = "Number of Injuries", title = "Injuries by Year and Injury Severity") +
  scale_x_continuous(breaks = seq(min(injury_counts$Year), max(injury_counts$Year), by = 1))

library(ggplot2)
library(ggmap)

## Warning: package 'ggmap' was built under R version 4.3.3

## ℹ Google's Terms of Service: <https://mapsplatform.google.com>
##   Stadia Maps' Terms of Service: <https://stadiamaps.com/terms-of-service/>
##   OpenStreetMap's Tile Usage Policy: <https://operations.osmfoundation.org/policies/tiles/>
## ℹ Please cite ggmap if you use it! Use `citation("ggmap")` for details.

api_key <- "AIzaSyD_HHmd6lL3rQOXBwFWwtL0gI_EfpOe5Bc"


poi <- c(lon = mean(data$Longitude), lat = mean(data$Latitude))
gap <- 0
center <- poi
zoom <- 13
register_google(key = api_key)

map <- get_googlemap(center = center, zoom = zoom, maptype = "terrain", color = "bw")

## ℹ <https://maps.googleapis.com/maps/api/staticmap?center=39.083119,-77.112343&zoom=13&size=640x640&scale=2&maptype=terrain&key=xxx>

# Check for missing values in Longitude and Latitude columns
missing_values <- data[is.na(data$Longitude) | is.na(data$Latitude), ]
print(missing_values)

##  [1] Report.Number                  Local.Case.Number             
##  [3] Agency.Name                    ACRS.Report.Type              
##  [5] Crash.Date.Time                Route.Type                    
##  [7] Road.Name                      Cross.Street.Type             
##  [9] Cross.Street.Name              Off.Road.Description          
## [11] Municipality                   Related.Non.Motorist          
## [13] Collision.Type                 Weather                       
## [15] Surface.Condition              Light                         
## [17] Traffic.Control                Driver.Substance.Abuse        
## [19] Non.Motorist.Substance.Abuse   Person.ID                     
## [21] Driver.At.Fault                Injury.Severity               
## [23] Circumstance                   Driver.Distracted.By          
## [25] Drivers.License.State          Vehicle.ID                    
## [27] Vehicle.Damage.Extent          Vehicle.First.Impact.Location 
## [29] Vehicle.Second.Impact.Location Vehicle.Body.Type             
## [31] Vehicle.Movement               Vehicle.Continuing.Dir        
## [33] Vehicle.Going.Dir              Speed.Limit                   
## [35] Driverless.Vehicle             Parked.Vehicle                
## [37] Vehicle.Year                   Vehicle.Make                  
## [39] Vehicle.Model                  Equipment.Problems            
## [41] Latitude                       Longitude                     
## [43] Location                       Crash_Date                    
## [45] Year                          
## <0 rows> (or 0-length row.names)

# Plot the map with points colored by Injury Severity
ggmap(map) +
  geom_point(data = data, aes(x = Longitude, y = Latitude, color = Injury.Severity), alpha = 0.6, size = 0.1) +
  facet_wrap(~ Injury.Severity) +
  labs(title = "Injury Severity by Location") +
  theme(legend.position = "bottom")

## Warning: Removed 139034 rows containing missing values (`geom_point()`).

library(leaflet)

## Warning: package 'leaflet' was built under R version 4.3.3

fatal <- data %>%
  filter(Injury.Severity =="FATAL INJURY" | Injury.Severity =="SUSPECTED SERIOUS INJURY")
# Create a leaflet map
m <- leaflet(fatal) %>%
  addTiles() %>%
  setView(lng = mean(fatal$Longitude), lat = mean(fatal$Latitude), zoom = 10) # Set initial view to center around the data

# Add markers for each data point, colored by Injury Severity
m <- m %>%
  addCircleMarkers(~Longitude, ~Latitude, color = "red",
                   fillColor = ~Injury.Severity, fillOpacity = 0.6, radius = 1)

# Display the map
m

Final Project Introduction

TAN NGUYEN

2024-04-11

Team Member (1)

Introduction : Unveiling Patterns in Montgomery County’s Crashes: A Look at Driver Data

Question to answer

Identify Trends:

Driver Behavior:

Data Visualization:

Outcome: Focus on Safety Improvements:

Future work