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Added trajectories and alternative GUI

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This commit is contained in:
Patrik M
2026-01-19 17:25:18 +01:00
committed by GitHub
parent 2f88c321d1
commit d8dd920d6b
2 changed files with 930 additions and 24 deletions
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src/main.Rmd
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@@ -43,7 +43,6 @@ flights <- getFlights(icao,Sys.time(), creds)
# TODO map from all available flights to tracks
query <- list(icao24= icao, time=as.numeric(flights[[1]][["departure_time"]]))
# can get tracks for up to 30 days in the past
response <-makeAuthenticatedRequest('tracks/all',query, creds)
track_data <- fromJSON(content(response, as = "text", encoding = "UTF-8"))
if (!is.null(track_data$path) && length(track_data$path) > 0) {
@@ -66,29 +65,383 @@ if (!is.null(track_data$path) && length(track_data$path) > 0) {
}
```
# GUI selection
```{r gui}
icaos <- lapply(departures, function(x) x[["ICAO24"]])
options <- unlist(icaos) # tcltk needs a character vector
# Create a GUI list selection
listSelect <- function(options){
selected_option <- NULL
tryCatch({
selected_option <- select.list(
title = "Select an aircraft",
choices = options,
preselect = NULL,
multiple = FALSE,
graphics = TRUE
)
}, error = function(w) {
message('No GUI available')
}
# Trajectory Characteristics Analysis
```{r trajectory-analysis}
if (exists("route_df") && nrow(route_df) > 1) {
# Convert lat/lon to approximate meters (using simple equirectangular projection)
# Reference point: first coordinate
lat_ref <- route_df$lat[1]
lon_ref <- route_df$lon[1]
# Convert to meters (approximate)
meters_per_deg_lat <- 111320
meters_per_deg_lon <- 111320 * cos(lat_ref * pi / 180)
x_meters <- (route_df$lon - lon_ref) * meters_per_deg_lon
y_meters <- (route_df$lat - lat_ref) * meters_per_deg_lat
time_seconds <- route_df$time - route_df$time[1]
# Create trajr trajectory object
trj <- TrajFromCoords(
data.frame(x = x_meters, y = y_meters, time = time_seconds),
xCol = "x", yCol = "y", timeCol = "time"
)
if (nzchar(selected_option)){
return(selected_option)
}
return(options[1])
# Calculate trajectory characteristics
# 1. Duration of travel (seconds)
duration <- TrajDuration(trj)
# 2. Total path length (meters)
path_length <- TrajLength(trj)
# 3. Diffusion distance (net displacement - straight line from start to end)
diffusion_distance <- TrajDistance(trj)
# 4. Straightness index (ratio of net displacement to path length, 0-1)
straightness <- TrajStraightness(trj)
# 5. Mean travel velocity (meters/second)
mean_velocity <- path_length / duration
# 6. Fractal dimension (using divider method)
# Note: requires sufficient points for accurate estimation
fractal_dim <- tryCatch({
# Calculate appropriate step sizes based on trajectory length
min_step <- TrajLength(trj) / 100
max_step <- TrajLength(trj) / 2
step_sizes <- exp(seq(log(min_step), log(max_step), length.out = 10))
TrajFractalDimension(trj, stepSizes = step_sizes)
}, error = function(e) {
message("Could not calculate fractal dimension: ", e$message)
NA
})
# Create summary data frame
trajectory_characteristics <- data.frame(
Parameter = c(
"Duration of Travel (s)",
"Duration of Travel (min)",
"Path Length (m)",
"Path Length (km)",
"Diffusion Distance (m)",
"Diffusion Distance (km)",
"Straightness Index",
"Mean Travel Velocity (m/s)",
"Mean Travel Velocity (km/h)",
"Fractal Dimension"
),
Value = c(
round(duration, 2),
round(duration / 60, 2),
round(path_length, 2),
round(path_length / 1000, 2),
round(diffusion_distance, 2),
round(diffusion_distance / 1000, 2),
round(straightness, 4),
round(mean_velocity, 2),
round(mean_velocity * 3.6, 2),
round(fractal_dim, 4)
)
)
print(trajectory_characteristics)
# Visualize the trajectory using trajr
plot(trj, main = paste("Trajectory of", icao))
} else {
message("No valid trajectory data available for analysis")
}
```
# Statistical Analysis of Multiple Trajectories
```{r multi-trajectory-analysis}
# Function to calculate trajectory characteristics for a single flight
calculate_trajectory_params <- function(icao24, departure_time, creds) {
tryCatch({
query <- list(icao24 = icao24, time = as.numeric(departure_time))
response <- makeAuthenticatedRequest('tracks/all', query, creds)
# Check for HTTP errors
if (httr::status_code(response) != 200) {
return(NULL)
}
track_data <- fromJSON(content(response, as = "text", encoding = "UTF-8"))
if (is.null(track_data$path) || length(track_data$path) < 2) {
return(NULL)
}
route_df <- as.data.frame(track_data$path)
colnames(route_df) <- c("time", "lat", "lon", "alt", "heading", "on_ground")
if (nrow(route_df) < 3) return(NULL)
# Convert to meters
lat_ref <- route_df$lat[1]
lon_ref <- route_df$lon[1]
meters_per_deg_lat <- 111320
meters_per_deg_lon <- 111320 * cos(lat_ref * pi / 180)
x_meters <- (route_df$lon - lon_ref) * meters_per_deg_lon
y_meters <- (route_df$lat - lat_ref) * meters_per_deg_lat
time_seconds <- route_df$time - route_df$time[1]
trj <- TrajFromCoords(
data.frame(x = x_meters, y = y_meters, time = time_seconds),
xCol = "x", yCol = "y", timeCol = "time"
)
# Calculate parameters
duration <- TrajDuration(trj)
path_length <- TrajLength(trj)
diffusion_dist <- TrajDistance(trj)
straight <- TrajStraightness(trj)
mean_vel <- path_length / duration
# Fractal dimension
fractal <- tryCatch({
min_step <- path_length / 100
max_step <- path_length / 2
if (min_step > 0 && max_step > min_step) {
step_sizes <- exp(seq(log(min_step), log(max_step), length.out = 10))
TrajFractalDimension(trj, stepSizes = step_sizes)
} else {
NA
}
}, error = function(e) NA)
return(data.frame(
icao24 = icao24,
diffusion_distance_km = diffusion_dist / 1000,
straightness = straight,
duration_min = duration / 60,
mean_velocity_kmh = mean_vel * 3.6,
fractal_dimension = fractal
))
}, error = function(e) {
message("Error processing ", icao24, ": ", e$message)
return(NULL)
})
}
# Collect trajectory data from multiple departures
message("Collecting trajectory data from departures...")
all_trajectories <- list()
# Process available departures (limit to avoid API rate limits)
n_departures <- min(length(departures), 20)
for (i in 1:n_departures) {
dep <- departures[[i]]
icao24 <- dep[["ICAO24"]]
dep_time <- dep[["departure_time"]] # Use departure time directly from departures list
# Skip if no departure time available
if (is.null(dep_time)) {
message("Skipping ", icao24, ": no departure time")
next
}
params <- calculate_trajectory_params(icao24, dep_time, creds)
if (!is.null(params)) {
all_trajectories[[length(all_trajectories) + 1]] <- params
message("Successfully processed trajectory for ", icao24)
}
Sys.sleep(0.5) # Rate limiting
}
# Combine all trajectory data
if (length(all_trajectories) > 0) {
trajectory_stats_df <- do.call(rbind, all_trajectories)
message("Successfully collected ", nrow(trajectory_stats_df), " trajectories")
print(trajectory_stats_df)
} else {
message("No trajectory data collected")
}
```
# Basic Statistical Analysis of Trajectory Parameters
```{r statistical-analysis}
if (exists("trajectory_stats_df") && nrow(trajectory_stats_df) >= 2) {
# Parameters to analyze
params_to_analyze <- c("diffusion_distance_km", "straightness", "duration_min",
"mean_velocity_kmh", "fractal_dimension")
param_labels <- c("Diffusion Distance (km)", "Straightness Index",
"Duration (min)", "Mean Velocity (km/h)", "Fractal Dimension")
# Function to calculate comprehensive statistics
calc_stats <- function(x, param_name) {
x <- x[!is.na(x)]
if (length(x) < 2) return(NULL)
data.frame(
Parameter = param_name,
N = length(x),
Mean = round(mean(x), 4),
Variance = round(var(x), 4),
Std_Dev = round(sd(x), 4),
Min = round(min(x), 4),
Q1 = round(quantile(x, 0.25), 4),
Median = round(median(x), 4),
Q3 = round(quantile(x, 0.75), 4),
Max = round(max(x), 4),
IQR = round(IQR(x), 4)
)
}
# Calculate statistics for each parameter
stats_list <- list()
for (i in seq_along(params_to_analyze)) {
param <- params_to_analyze[i]
label <- param_labels[i]
stats_list[[i]] <- calc_stats(trajectory_stats_df[[param]], label)
}
# Combine into summary table
stats_summary <- do.call(rbind, stats_list[!sapply(stats_list, is.null)])
cat("\n========== STATISTICAL SUMMARY ==========\n\n")
print(stats_summary, row.names = FALSE)
# Boxplots for each parameter
par(mfrow = c(2, 3))
for (i in seq_along(params_to_analyze)) {
param <- params_to_analyze[i]
label <- param_labels[i]
data <- trajectory_stats_df[[param]][!is.na(trajectory_stats_df[[param]])]
if (length(data) >= 2) {
boxplot(data,
main = paste("Boxplot:", label),
ylab = label,
col = "lightblue",
border = "darkblue")
# Add mean point
points(1, mean(data), pch = 18, col = "red", cex = 1.5)
}
}
par(mfrow = c(1, 1))
# Density plots for each parameter
par(mfrow = c(2, 3))
for (i in seq_along(params_to_analyze)) {
param <- params_to_analyze[i]
label <- param_labels[i]
data <- trajectory_stats_df[[param]][!is.na(trajectory_stats_df[[param]])]
if (length(data) >= 3) {
dens <- density(data, na.rm = TRUE)
plot(dens,
main = paste("Density:", label),
xlab = label,
ylab = "Density",
col = "darkblue",
lwd = 2)
polygon(dens, col = rgb(0, 0, 1, 0.3), border = "darkblue")
# Add vertical lines for mean and median
abline(v = mean(data), col = "red", lwd = 2, lty = 2)
abline(v = median(data), col = "green", lwd = 2, lty = 3)
legend("topright", legend = c("Mean", "Median"),
col = c("red", "green"), lty = c(2, 3), lwd = 2, cex = 0.7)
}
}
par(mfrow = c(1, 1))
# Histogram with density overlay
par(mfrow = c(2, 3))
for (i in seq_along(params_to_analyze)) {
param <- params_to_analyze[i]
label <- param_labels[i]
data <- trajectory_stats_df[[param]][!is.na(trajectory_stats_df[[param]])]
if (length(data) >= 3) {
hist(data,
probability = TRUE,
main = paste("Histogram:", label),
xlab = label,
col = "lightgray",
border = "darkgray")
# Overlay density curve
lines(density(data), col = "red", lwd = 2)
}
}
par(mfrow = c(1, 1))
} else {
message("Insufficient trajectory data for statistical analysis (need at least 2 trajectories)")
}
```
# Interpretation of Results
```{r interpretation}
if (exists("trajectory_stats_df") && nrow(trajectory_stats_df) >= 2) {
cat("\n========== INTERPRETATION OF TRAJECTORY PARAMETERS ==========\n\n")
# Diffusion Distance
dd <- trajectory_stats_df$diffusion_distance_km[!is.na(trajectory_stats_df$diffusion_distance_km)]
if (length(dd) >= 2) {
cat("1. DIFFUSION DISTANCE (Net Displacement):\n")
cat(" - Mean:", round(mean(dd), 2), "km\n")
cat(" - This represents the straight-line distance from origin to destination.\n")
cat(" - High variance (", round(var(dd), 2), ") indicates diverse flight distances.\n\n")
}
# Straightness
st <- trajectory_stats_df$straightness[!is.na(trajectory_stats_df$straightness)]
if (length(st) >= 2) {
cat("2. STRAIGHTNESS INDEX:\n")
cat(" - Mean:", round(mean(st), 4), "(range 0-1, where 1 = perfectly straight)\n")
cat(" - Values close to 1 indicate efficient, direct flight paths.\n")
cat(" - Lower values suggest deviations due to weather, airspace, or routing.\n\n")
}
# Duration
dur <- trajectory_stats_df$duration_min[!is.na(trajectory_stats_df$duration_min)]
if (length(dur) >= 2) {
cat("3. DURATION OF TRAVEL:\n")
cat(" - Mean:", round(mean(dur), 2), "minutes\n")
cat(" - Range:", round(min(dur), 2), "-", round(max(dur), 2), "minutes\n")
cat(" - IQR:", round(IQR(dur), 2), "minutes (middle 50% of flights)\n\n")
}
# Velocity
vel <- trajectory_stats_df$mean_velocity_kmh[!is.na(trajectory_stats_df$mean_velocity_kmh)]
if (length(vel) >= 2) {
cat("4. MEAN TRAVEL VELOCITY:\n")
cat(" - Mean:", round(mean(vel), 2), "km/h\n")
cat(" - Typical commercial aircraft cruise: 800-900 km/h\n")
cat(" - Lower values may include taxi, takeoff, and landing phases.\n\n")
}
# Fractal Dimension
fd <- trajectory_stats_df$fractal_dimension[!is.na(trajectory_stats_df$fractal_dimension)]
if (length(fd) >= 2) {
cat("5. FRACTAL DIMENSION:\n")
cat(" - Mean:", round(mean(fd), 4), "\n")
cat(" - Value of 1.0 = perfectly straight line\n")
cat(" - Values closer to 2.0 = more complex, space-filling paths\n")
cat(" - Aircraft typically show low fractal dimension (efficient paths).\n\n")
}
cat("========== END OF ANALYSIS ==========\n")
}
```