minor changes in analysis summary

src/main/R/drtAnalysis/readValuesFromRunSummaries.R

@@ -85,7 +85,9 @@ process_folders <- function(main_folder, speed) {
 #############
 
 mainDir <- "D:/Projekte/KelRide/runs/v3.1.1/output-KEXI-2.45-AV--0.0/"
-#mainDir <- "//sshfs.r/[email protected]/net/ils/matsim-kelheim/v3.1.1/output-KEXI-2.45-AV--0.0/"
+
+mainDir <- "E:/matsim-kelheim/v3.1.1/output-KEXI-2.45-AV--0.0/"
+
 speeds <- list(3.3, 5, 8.3)
 
 results <- list()
@@ -114,26 +116,26 @@ write_csv(transposed_result, paste(mainDir, "results.csv", sep=""))
 ######PLOTS####
 
 # Filtern der Daten für die gewünschten Parameter
-plot_data <- results %>%
-  filter(parameter %in% c("Handled Requests"))
-
-# Erstellen des interaktiven dreidimensionalen Plots
-plot <- plot_ly(plot_data, 
-                x = ~speed, 
-                y = ~fleetSize, 
-                z =  ~mean, 
-                color = ~area, 
-                type = "scatter3d", 
-                mode = "markers",
-                marker = list(size = 5)) %>%
-  add_markers() %>%
-  layout(title = "Handled Requests by speed, area and Fleet Size",
-         scene = list(xaxis = list(title = "Speed"),
-                      yaxis = list(title = "Fleet Size"),
-                      zaxis = list(title = "Handled Requests")))
+#plot_data <- results %>%
+#  filter(parameter %in% c("Handled Requests"))
 
-# Plot anzeigen
-plot
+## Erstellen des interaktiven dreidimensionalen Plots
+#plot <- plot_ly(plot_data, 
+#                x = ~speed, 
+#                y = ~fleetSize, 
+#                z =  ~mean, 
+#                color = ~area, 
+#                type = "scatter3d", 
+#                mode = "markers",
+#                marker = list(size = 5)) %>%
+#  add_markers() %>%
+#  layout(title = "Handled Requests by speed, area and Fleet Size",
+#         scene = list(xaxis = list(title = "Speed"),
+#                      yaxis = list(title = "Fleet Size"),
+#                      zaxis = list(title = "Handled Requests")))
+#
+## Plot anzeigen
+#plot
 
 # Erstellen des interaktiven dreidimensionalen Plots mit Mesh
 plot <- plot_ly(plot_data) %>%
@@ -153,38 +155,38 @@ plot <- plot_ly(plot_data) %>%
 # Plot anzeigen
 plot
 
-mesh_data <- plot_data %>%
-  group_by(area) %>%
-  summarise(speed = mean(speed),
-            fleetSize = mean(fleetSize),
-            mean = mean(mean)) %>%
-  arrange(area)
+#mesh_data <- plot_data %>%
+#  group_by(area) %>%
+#  summarise(speed = mean(speed),
+#            fleetSize = mean(fleetSize),
+#            mean = mean(mean)) %>%
+#  arrange(area)
 
-# Erstellen des interaktiven dreidimensionalen Plots mit Meshes für jede Area
-plot <- plot_ly(plot_data) %>%
-  add_trace(x = ~speed, 
-            y = ~fleetSize, 
-            z = ~mean,
-            color = ~area,
-            type = "scatter3d",
-            mode = "markers",
-            marker = list(size = 5)) %>%
-  add_trace(data = mesh_data,
-            x = ~speed,
-            y = ~fleetSize,
-            z = ~mean,
-            color = ~area,
-            type = "mesh3d",
-            opacity = 0.6,  # Opazität der Flächen
-            colorscale = "Viridis",  # Farbskala für die Flächen
-            showscale = TRUE) %>%
-  layout(title = "Handled Requests by speed, fleetSize and area",
-         scene = list(xaxis = list(title = "Speed"),
-                      yaxis = list(title = "Fleet Size"),
-                      zaxis = list(title = "Mean Handled Requests")))
+## Erstellen des interaktiven dreidimensionalen Plots mit Meshes für jede Area
+#plot <- plot_ly(plot_data) %>%
+#  add_trace(x = ~speed, 
+#            y = ~fleetSize, 
+#            z = ~mean,
+#            color = ~area,
+#            type = "scatter3d",
+#            mode = "markers",
+#            marker = list(size = 5)) %>%
+#  add_trace(data = mesh_data,
+#            x = ~speed,
+#            y = ~fleetSize,
+#           z = ~mean,
+#            color = ~area,
+#            type = "mesh3d",
+#            opacity = 0.6,  # Opazität der Flächen
+#            colorscale = "Viridis",  # Farbskala für die Flächen
+#            showscale = TRUE) %>%
+#  layout(title = "Handled Requests by speed, fleetSize and area",
+#         scene = list(xaxis = list(title = "Speed"),
+#                      yaxis = list(title = "Fleet Size"),
+#                      zaxis = list(title = "Mean Handled Requests")))
 
-# Plot anzeigen
-plot
+## Plot anzeigen
+#plot
 
 
 ###########################
@@ -256,7 +258,9 @@ plotByConfiguration("Pax per veh-km")
     geom_point(size = 3
                #,aes(shape = as.factor(intermodal))
                ) +
-    facet_wrap(~ speed, scales = "free") +
+    facet_wrap(~ speed
+               #, scales = "free"
+               ) +
     labs(title = "Handled Requests by Avg. Wait Time, Speed, Area, and All Day",
          x = "Avg. Wait Time",
          y = "Handled Requests",

src/main/R/drtAnalysis/readValuesFromRunSummaries_v3.0.1-fareExperiments.R

@@ -0,0 +1,188 @@
+library(dplyr)
+library(readr)
+library(tidyr)
+library(ggplot2)  # Für das Plotting mit ggplot2
+library(plotly)
+
+# Funktion zum Extrahieren der Parameter aus dem Ordnernamen
+extract_parameters <- function(folder_name) {
+  # Extrahiere 'area'
+  fares <- strsplit(folder_name, '-')[[1]][2]
+
+  return(list(fares = fares))
+}
+
+# Funktion zum Einlesen der CSV-Datei und Extrahieren der "mean"-Werte
+read_stats <- function(folder_path, file_name) {
+  csv_path <- file.path(folder_path, "analysis/drt-drt", file_name)
+
+  if (file.exists(csv_path)) {
+    df <- read_csv(csv_path)
+    mean_values <- df %>% select(parameter, mean)
+    return(mean_values)
+  } else {
+    return(NULL)
+  }
+}
+
+# Hauptfunktion zum Iterieren durch Unterordner
+process_folders <- function(main_folder) {
+  # Liste aller Unterordner im Hauptordner
+  subfolders <- list.dirs(main_folder, recursive = FALSE, full.names = FALSE)
+
+  # Initialisiere eine Liste zum Speichern der Ergebnisse
+  results <- list()
+
+  # Iteriere durch alle Unterordner
+  for (subfolder in subfolders) {
+    parameters <- extract_parameters(subfolder)
+    full_path <- file.path(main_folder, subfolder)
+
+    demand_mean_values <- read_stats(full_path, "avg_demand_stats.csv")
+    supply_mean_values <- read_stats(full_path, "avg_supply_stats.csv")
+
+    if (!is.null(demand_mean_values) || !is.null(supply_mean_values)) {
+      if (!is.null(demand_mean_values)) {
+        demand_mean_values <- demand_mean_values %>% 
+          mutate(type = "demand",
+                 fares = parameters$fares)
+      }
+
+      if (!is.null(supply_mean_values)) {
+        supply_mean_values <- supply_mean_values %>% 
+          mutate(type = "supply",
+                 fares = parameters$fares)
+      }
+
+      combined_values <- bind_rows(demand_mean_values, supply_mean_values)
+      results[[subfolder]] <- combined_values
+    }
+  }
+
+  # Kombiniere alle Ergebnisse in eine Tabelle
+  final_result <- bind_rows(results)
+  return(final_result)
+}
+
+#############
+
+mainDir <- "D:/Projekte/KelRide/runs/v3.0.1-fare-experiments/output-KEXI-kexi"
+
+#speeds <- list(3.3, 5, 8.3)
+#results <- list()
+#for (speed in speeds) {
+#  main_folder <- paste(mainDir, "AV-speed-mps-", speed, "/", sep="")
+#  runResults <- process_folders(mainDir)  
+#  results[[speed]] <- runResults
+#}
+
+#results <- bind_rows(results)
+
+
+results <- process_folders(mainDir)
+
+# Transponiere die Tabelle, um Parameter als Spalten zu setzen
+transposed_result <- results %>%
+  select(fares, parameter, mean) %>%
+  spread(key = parameter, value = mean)
+
+
+# Ergebnisse ausgeben
+print(results)
+print(transposed_result)
+
+write_csv(transposed_result, paste(mainDir, "results.csv", sep=""))
+
+
+
+
+###########################
+plotByConfiguration <- function(parameterStr){
+
+  # Filtern der Daten für die gewünschten Parameter
+  plot_data <- results %>%
+    filter(parameter == parameterStr)
+
+  # Erstellen des Facet-Plots
+  ggplot(plot_data, aes(x = fares, y = mean)) +
+    #geom_line(size = 1.2) +
+    geom_point(size = 3,
+               #aes(shape = as.factor(intermodal))
+    ) +
+    #facet_wrap(~ speed,
+    #           scales = "free"
+    #) +
+    labs(title = paste(parameterStr, "by fare system (conv. KEXI)"),
+         x = "Fare System",
+         y = parameterStr,
+         #color = "Area",
+         #linetype = "All Day"
+         #,shape = "Intermodal"
+    ) +
+    theme_dark() +
+    theme(
+      plot.title = element_text(size = 16, face = "bold"),  # Titelgröße anpassen
+      axis.title.x = element_text(size = 14),  # X-Achsentitelgröße anpassen
+      axis.title.y = element_text(size = 14),  # Y-Achsentitelgröße anpassen
+      axis.text = element_text(size = 12),  # Achsentextgröße anpassen
+      legend.title = element_text(size = 14),  # Legendentitelgröße anpassen
+      legend.text = element_text(size = 12),  # Legendtextgröße anpassen
+      strip.text = element_text(size = 12)  # Facet-Textgröße anpassen
+    )
+
+}
+
+unique(results$parameter)
+plotByConfiguration("Rides")
+plotByConfiguration("Avg. wait time")
+plotByConfiguration("Avg. ride distance [km]")
+plotByConfiguration("Empty ratio")
+plotByConfiguration("Total vehicle mileage [km]")
+plotByConfiguration("Avg. fare [MoneyUnit]" )
+plotByConfiguration("Pax per veh-km")
+
+#####################
+##Zusammenhang wait time und Nachfrage
+
+handled_requests_data <- results %>%
+  filter(parameter == "Rides") %>%
+  select(fares, mean) %>%
+  rename(handled_requests = mean)
+
+avg_wait_time_data <- results %>%
+  filter(parameter == "Avg. wait time") %>%
+  select(fares, mean) %>%
+  rename(avg_wait_time = mean)
+
+# Zusammenführen der Daten
+plot_data <- left_join(handled_requests_data, avg_wait_time_data, by = c("fares"))
+
+# Erstellen des Facet-Plots
+facet_plot <- ggplot(plot_data, aes(x = avg_wait_time, y = handled_requests)) +
+  #geom_line(size = 1.2) +
+  geom_point(size = 3
+             #,aes(shape = as.factor(intermodal))
+  ) +
+  geom_text(aes(label = fares), vjust = -1, hjust = 0.5, size = 3, color = "white") +
+  #facet_wrap(~ speed, scales = "free") +
+  labs(title = "Handled Requests by Avg. Wait Time and Fare System (conv. KEXI)",
+       x = "Avg. Wait Time",
+       y = "Handled Requests",
+       #color = "Area",
+       #linetype = "All Day"
+       #,shape = "Intermodal"
+  ) +
+  theme_dark() +
+  theme(
+    plot.title = element_text(size = 16, face = "bold"),  # Titelgröße anpassen
+    axis.title.x = element_text(size = 14),  # X-Achsentitelgröße anpassen
+    axis.title.y = element_text(size = 14),  # Y-Achsentitelgröße anpassen
+    axis.text = element_text(size = 12),  # Achsentextgröße anpassen
+    legend.title = element_text(size = 14),  # Legendentitelgröße anpassen
+    legend.text = element_text(size = 12),  # Legendtextgröße anpassen
+    strip.text = element_text(size = 12)  # Facet-Textgröße anpassen
+  )
+
+# Plot anzeigen
+print(facet_plot)
+