Under Construction!

Uh-oh — the page you are looking for didn’t survive the latest round of funding cuts. Try using the search button at the top of the page or searching on Google.

While we wait for the public comment period, why not test your Transportation & Data Science smarts? It’s more fun than hitting the back button. Probably.

Welcome to the Transportation Data Science Challenge! 🚦

Complete these interactive R exercises to test your skills in transportation planning and data analysis. Each exercise builds on real-world scenarios you’ll encounter as a transportation professional.

🧠 Knowledge Test

Test your understanding of transportation planning fundamentals

Question 1: Traffic Signal Timing ⏱️

If a traffic light has a 90-second cycle and the green light lasts 60 seconds, how many cars can pass through if each car takes 3 seconds to clear the intersection?

# Calculate how many cars can pass in one green phase
green_time <- 60  # seconds
time_per_car <- 3  # seconds

# Divide green time by time per car
cars_per_cycle <- green_time / time_per_car

cars_per_cycle

Question 2: Level of Service 🚦

Calculate the volume-to-capacity (V/C) ratio for an intersection with 1,600 vehicles/hour and capacity of 1,800 vehicles/hour. What LOS category does this represent? (A-C: ≤0.8, D: 0.8-0.9, E-F: >0.9)

volume <- 1600
capacity <- 1800

# Calculate V/C ratio
vc_ratio <- volume / capacity

# Determine LOS category based on thresholds
los_category <- if (vc_ratio <= 0.8) {
  "A-C"
} else if (vc_ratio <= 0.9) {
  "D" 
} else {
  "E-F"
}

list(vc_ratio = vc_ratio, los_category = los_category)

Question 3: Modal Split 🚌🚲🚗

In a city where 40% of trips are by car, 25% by transit, 20% by walking, and 15% by cycling, what percentage of non-car trips are made by transit?

car_percent <- 40
transit_percent <- 25
walking_percent <- 20
cycling_percent <- 15

# Calculate percentage of non-car trips that are transit
non_car_total <- 100 - car_percent  # 60%
transit_share_of_non_car <- (transit_percent / non_car_total) * 100

transit_share_of_non_car

Question 4: Trip Generation 🏠

A residential development has 200 housing units. Using ITE trip generation rate of 9.5 trips per dwelling unit per day, calculate total daily trips. If 15% occur during PM peak hour, how many PM peak trips?

housing_units <- 200
trip_rate <- 9.5
pm_peak_percent <- 15

# Calculate total daily trips
total_daily_trips <- housing_units * trip_rate

# Calculate PM peak hour trips
pm_peak_trips <- total_daily_trips * (pm_peak_percent / 100)

list(daily = total_daily_trips, pm_peak = pm_peak_trips)

Question 5: Transit Frequency 🚍

A bus route operates every 12 minutes during peak hours. How many buses pass a given stop in one hour? If each bus has 40-passenger capacity and average load is 75%, what’s the hourly passenger capacity past that stop?

frequency_minutes <- 12
bus_capacity <- 40
load_factor <- 0.75

# Calculate buses per hour
buses_per_hour <- 60 / frequency_minutes

# Calculate hourly passenger capacity
hourly_passenger_capacity <- buses_per_hour * bus_capacity * load_factor

list(buses_per_hour = buses_per_hour, hourly_capacity = hourly_passenger_capacity)

🚀 Skills Test

Put your R and dplyr skills to work with real transportation datasets

Question 1: High-Traffic Location Analysis 📊

Using the traffic_data dataset, filter for locations with AADT > 15,000 and count how many locations meet this criteria.

# Filter for high-traffic locations and count them
high_traffic <- traffic_data |>
  filter(aadt > 15000)

# Count the results
count_high_traffic <- nrow(high_traffic)

count_high_traffic

Question 2: Safety Rate Calculation 🔍

Calculate the accident rate per 1,000 AADT for each location. Create a new column called accident_rate and arrange by highest rate.

# Calculate accident rates and arrange
safety_analysis <- traffic_data |>
  mutate(accident_rate = (accidents_2023 / aadt) * 1000) |>
  arrange(desc(accident_rate))

# Show the results
safety_analysis

Question 3: Transit Efficiency Analysis 🚌

Calculate riders per mile for each transit route and identify the most efficient route (highest riders per mile).

# Calculate efficiency and find the best route
transit_efficiency <- transit_data |>
  mutate(riders_per_mile = daily_riders / route_length_miles) |>
  arrange(desc(riders_per_mile))

# Get the most efficient route name
most_efficient <- transit_efficiency |>
  slice(1) |>
  pull(route)

most_efficient

Question 4: Bike Share Visualization 📈

Create a scatter plot showing the relationship between distance from downtown and daily trips for bike share stations. Add a smooth trend line.

# Create the bike share analysis plot
bike_plot <- ggplot(bike_data, aes(x = distance_downtown_km, y = daily_trips)) +
  geom_point(size = 3, alpha = 0.7, color = "steelblue") +
  geom_smooth(method = "lm", se = FALSE, color = "red") +
  labs(
    title = "Bike Share Usage vs Distance from Downtown",
    x = "Distance from Downtown (km)",
    y = "Daily Trips"
  ) +
  theme_minimal()

bike_plot

Question 5: Comprehensive Performance Summary 🏆

Create a summary table showing for each location: total daily volume, accidents per 1000 vehicles, and congestion level (Low: <3hrs, Medium: 3-5hrs, High: >5hrs).

# Create comprehensive performance summary
performance_summary <- traffic_data |>
  mutate(
    daily_volume = aadt,
    accidents_per_1000 = (accidents_2023 / aadt) * 1000,
    congestion_level = case_when(
      congestion_hours < 3 ~ "Low",
      congestion_hours <= 5 ~ "Medium", 
      TRUE ~ "High"
    )
  ) |>
  select(location, daily_volume, accidents_per_1000, congestion_level)

performance_summary

🎯 Quiz Complete!

Congratulations, You are now a Transportation Data Scientist! 🏆

P.S. - If you got this far, you’re definitely not lost anymore. You’re a certified Transportation Data Scientist! 🎯

Built with Quarto Live and WebR