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Merge branch 'master' into drt-fleet-sizing
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@luchengqi7
luchengqi7 committed Sep 9, 2024
2 parents 631d667 + 71adb9a commit 2c91fa9
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67 changes: 55 additions & 12 deletions src/main/R/drtAnalysis/plotRunSummaries.R
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Original file line number Diff line number Diff line change
Expand Up @@ -4,18 +4,33 @@ library(tidyr)

mainDir <- "E:/matsim-kelheim/v3.1.1/output-KEXI-2.45-AV--0.0/"

transposed_result <- read.csv(paste(mainDir, "results-deutsch.csv", sep=""), check.names = FALSE)
transposed_result <- read.csv(paste(mainDir, "results-deutsch.csv", sep=""), check.names = FALSE, sep =";")
#names(transposed_result) <- make.names(names(transposed_result), unique = TRUE, allow_ = FALSE)

# Betriebszeiten umschreiben
transposed_result$Betriebszeiten <- ifelse(transposed_result$Betriebszeiten == TRUE, "0h - 24h", "9h - 16h")
transposed_result$Betriebszeiten <- factor(ifelse(transposed_result$Betriebszeiten == TRUE, "0h - 24h", "9h - 16h"),
levels = c("9h - 16h", "0h - 24h"))


###
#in Realität haben wir eine avg gruppengr0eße von 1.7 gemessen, diese aber nicht simuliert.
# wir rechnen die jetzt im nachhinein wieder drauf.
## --> machen wir jetzt im skript, dass die daten rausschreibt (readValuesFromRunSammaries.R)

#transposed_result <- transposed_result %>%
# mutate(`Anzahl Passagiere` = `Bediente Anfragen` * 1.7,
# `Gesamt Passagierkilometer [km]` = `Gesamt Passagierkilometer [km]` * 1.7) %>%
# mutate(`Passagiere pro Fahrzeug` = `Anzahl Passagiere` / Fahrzeuge,
# `Passagiere pro Fahrzeugkilometer` = `Anzahl Passagiere` / `Summe Fahrzeugkilometer [km]`,
# `Passagiere pro Fahrzeugstunde` = `Anzahl Passagiere` / `Summe Fzg.-Betriebsstunden`,
# `Besetzungsgrad [pax-km/v-km]` = `Gesamt Passagierkilometer [km]` / `Summe Fahrzeugkilometer [km]`)#


results <- transposed_result %>%
gather(key = "parameter", value = "mean", -Geschwindigkeit, -Bediengebiet, -Flottengroeße, -Intermodal, -Betriebszeiten)


plotByConfiguration <- function(parameterStr){
plotByConfiguration <- function(parameterStr, scales = "free"){

# Filtern der Daten für die gewünschten Parameter
plot_data <- results %>%
Expand All @@ -24,7 +39,7 @@ plotByConfiguration <- function(parameterStr){

# Funktion zum Anpassen der Facet-Labels
label_function <- function(value) {
paste(value, "m/s")
paste(round(as.numeric(value) * 3.6, 0), "km/h")
}

# Erstellen des Facet-Plots
Expand All @@ -35,9 +50,9 @@ plotByConfiguration <- function(parameterStr){
) +
facet_wrap(~ Geschwindigkeit,
labeller = labeller(Geschwindigkeit = label_function)
,scales = "free"
,scales = scales
) +
labs(title = paste(parameterStr, "nach Geschwindigkeit, Flottengröße, Bediengebiet und Betriebszeiten"),
labs(title = paste(parameterStr, "nach Geschwindigkeit, Flottengröße,\nBediengebiet und Betriebszeiten"),
x = "Flottengröße",
y = parameterStr,
color = "Bediengebiet",
Expand All @@ -57,11 +72,39 @@ plotByConfiguration <- function(parameterStr){

}

save <- function(fileName){
ggsave(filename = paste(mainDir, "plots/", fileName, ".png", sep = ""),
dpi = 600, width = 32, height = 18, units = "cm")
}

unique(results$parameter)

###nachfrage
plotByConfiguration("Bediente Anfragen")
plotByConfiguration("Mittl. Wartezeit")
plotByConfiguration("Avg. ride distance [km]")
plotByConfiguration("Empty ratio")
plotByConfiguration("Total vehicle mileage [km]")
plotByConfiguration("Avg. fare [MoneyUnit]" )
plotByConfiguration("Pax per veh-km")
save("bedienteAnfragen")
plotByConfiguration("Anzahl Passagiere", "fixed")
save("passagiere")
plotByConfiguration("Mittl. Wartezeit [s]", "fixed")
save("wartezeit")
plotByConfiguration("Umwegfaktor", "fixed")
plotByConfiguration("Mittl. Reiseweite [km]", "fixed")
save("reiseweite")
plotByConfiguration("Anteil Leerkilometer", "fixed")
save("leerkilometer")


###betrieb
plotByConfiguration("Summe Fahrzeugkilometer [km]")
save("fahrzeugkilometer")
plotByConfiguration("Besetzungsgrad [pax-km/v-km]")
save("besetzungsgrad")
plotByConfiguration("Passagiere pro Fahrzeugkilometer", "fixed")
save("paxPerKM")
plotByConfiguration("Passagiere pro Fahrzeugstunde")
plotByConfiguration("Passagiere pro Fahrzeugstunde", "fixed")
x^xsave("paxPerVehHour")


plotByConfiguration("Gesamt Passagierkilometer [km]")
plotByConfiguration("Gesamt Passagierkilometer [km]", "fixed")

20 changes: 18 additions & 2 deletions src/main/R/drtAnalysis/readValuesFromRunSummaries.R
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Original file line number Diff line number Diff line change
Expand Up @@ -24,6 +24,7 @@ extract_parameters <- function(folder_name, speed) {

# Funktion zum Einlesen der CSV-Datei und Extrahieren der "mean"-Werte
read_stats <- function(folder_path, file_name) {
print(paste("reading", folder_path))
csv_path <- file.path(folder_path, "analysis/drt-drt-av", file_name)

if (file.exists(csv_path)) {
Expand Down Expand Up @@ -99,20 +100,33 @@ for (speed in speeds) {

results <- bind_rows(results)


#####
# Transponiere die Tabelle, um Parameter als Spalten zu setzen
transposed_result <- results %>%
select(speed, area, fleetSize, intermodal, allDay, parameter, mean) %>%
spread(key = parameter, value = mean)

###
#in Realität haben wir eine avg gruppengr0eße von 1.7 gemessen, diese aber nicht simuliert.
# wir rechnen die jetzt im nachhinein wieder drauf.
transposed_result <- transposed_result %>%
mutate(`Passengers (Pax)` = `Handled Requests` * 1.7,
`Total pax distance [km]` = `Total pax distance [km]` * 1.7) %>%
mutate(`Pax per veh` = `Passengers (Pax)` / Vehicles,
`Pax per veh-km` = `Passengers (Pax)` / `Total vehicle mileage [km]`,
`Pax per veh-h` = `Passengers (Pax)` / `Total service hours`,
`Occupancy rate [pax-km/v-km]` = `Total pax distance [km]` / `Total vehicle mileage [km]`)

#transponiere zurück
results <- transposed_result %>%
gather(key = "parameter", value = "mean", -speed, -area, -fleetSize, -intermodal, -allDay)

# Ergebnisse ausgeben
print(results)
print(transposed_result)

write_csv(transposed_result, paste(mainDir, "results.csv", sep=""))


#####################################################################
######PLOTS####

Expand All @@ -138,6 +152,7 @@ plotByConfiguration <- function(parameterStr){
labeller = labeller(speed = label_function)
,scales = "free"
) +
geom_text(aes(label = fleetSize), vjust = -1, hjust = 0.5, size = 3, color = "black") +
labs(title = paste(parameterStr, "by Fleet Size, Speed, Area and Service Hours"),
x = "Fleet Size",
y = parameterStr,
Expand Down Expand Up @@ -213,4 +228,5 @@ plotByConfiguration("Pax per veh-km")

# Plot anzeigen
print(facet_plot)


98 changes: 71 additions & 27 deletions src/main/R/drtAnalysis/readValuesFromRunSummaries_v3.0.1-fareExperiments.R
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Original file line number Diff line number Diff line change
Expand Up @@ -67,6 +67,7 @@ process_folders <- function(main_folder) {
#############

mainDir <- "D:/Projekte/KelRide/runs/v3.0.1-fare-experiments/output-KEXI-kexi"
mainDir <- "E:/matsim-kelheim/v3.0.1-fare-experiments/output-KEXI-kexi/"

#speeds <- list(3.3, 5, 8.3)
#results <- list()
Expand Down Expand Up @@ -95,6 +96,11 @@ write_csv(transposed_result, paste(mainDir, "results.csv", sep=""))



save <- function(fileName){
ggsave(filename = paste(mainDir, "plots/", fileName, ".png", sep = ""),
dpi = 600, width = 32, height = 18, units = "cm")
}


###########################
plotByConfiguration <- function(parameterStr){
Expand All @@ -119,27 +125,26 @@ plotByConfiguration <- function(parameterStr){
#linetype = "All Day"
#,shape = "Intermodal"
) +
theme_dark() +
#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.title = element_text(size = 20, face = "bold"), # Titelgröße anpassen
axis.title.x = element_text(size = 18), # X-Achsentitelgröße anpassen
axis.title.y = element_text(size = 18), # Y-Achsentitelgröße anpassen
axis.text = element_text(size = 14), # Achsentextgröße anpassen
legend.title = element_text(size = 18), # Legendentitelgröße anpassen
legend.text = element_text(size = 14), # Legendtextgröße anpassen
strip.text = element_text(size = 18, face = "bold") # 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")
#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
Expand All @@ -155,34 +160,73 @@ avg_wait_time_data <- results %>%
rename(avg_wait_time = mean)

# Zusammenführen der Daten
plot_data <- left_join(handled_requests_data, avg_wait_time_data, by = c("fares"))
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") +
geom_text(aes(label = fares), vjust = -1, hjust = 0.5, size = 6, color = "black") +
#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",
labs(title = "Anzahl Passagiere nach durchschn. Wartezeit und Preisschema",
x = "Durchschn. Wartezeit [s]",
y = "# Passagiere",
#color = "Area",
#linetype = "All Day"
#,shape = "Intermodal"
) +
theme_dark() +
#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.title = element_text(size = 20, face = "bold"), # Titelgröße anpassen
axis.title.x = element_text(size = 18), # X-Achsentitelgröße anpassen
axis.title.y = element_text(size = 18), # Y-Achsentitelgröße anpassen
axis.text = element_text(size = 14), # Achsentextgröße anpassen
legend.title = element_text(size = 18), # Legendentitelgröße anpassen
legend.text = element_text(size = 14), # Legendtextgröße anpassen
strip.text = element_text(size = 18, face = "bold") # Facet-Textgröße anpassen
)

# Plot anzeigen
print(facet_plot)
save("pax-over-avg-wait-time")

#####################
##Zusammenhang Durchschnittspreis und Nachfrage

# Filter für die beiden relevanten Parameter
rides_data <- results %>%
filter(parameter == "Rides") %>%
select(fares, mean) %>%
rename(rides = mean)

avg_fare_data <- results %>%
filter(parameter == "Avg. fare [MoneyUnit]") %>%
select(fares, mean) %>%
rename(avg_fare = mean)

# Zusammenführen der Daten
plot_data <- left_join(rides_data, avg_fare_data, by = c("fares"))

# Erstellen des Plots
fare_vs_rides_plot <- ggplot(plot_data, aes(x = avg_fare, y = rides)) +
geom_point(size = 3) +
geom_text(aes(label = fares), vjust = -1, hjust = 0.5, size = 6, color = "black") +
labs(title = "Anzahl Passagiere nach Durchschnittspreis",
x = "Durchschnittlicher Preis pro Fahrt [€]",
y = "# Passagiere") +
theme(
plot.title = element_text(size = 20, face = "bold"),
axis.title.x = element_text(size = 18),
axis.title.y = element_text(size = 18),
axis.text = element_text(size = 14),
legend.title = element_text(size = 18),
legend.text = element_text(size = 14),
strip.text = element_text(size = 18, face = "bold")
)

# Plot anzeigen
print(fare_vs_rides_plot)
save("pax-over-avg-fare")

35 changes: 22 additions & 13 deletions src/main/R/drtDemandAnalysis/VIA-data/KEXI-analysis-for-calibration-VIA.R
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Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,7 @@ Sys.setlocale("LC_TIME", "en_US.UTF-8")
### INPUT DEFINITIONS ###

# set working directory
setwd("D:/Module/vsp/shared-svn/")
setwd("D:/shared-svn/projects/KelRide/data/KEXI")
#setwd("C:/Users/Simon/Documents/shared-svn/projects/KelRide/data/KEXI/")

# read data
Expand Down Expand Up @@ -201,17 +201,20 @@ for(dataset in datasets) {


boxplot_TravelTime_s <- ggplot(j, aes(y=travelTime_s)) +
stat_boxplot(geom="errorbar", width=3) +
stat_boxplot(geom="errorbar", width=5) +
geom_boxplot(width=5) +
scale_y_continuous(n.breaks = 10) +
scale_x_discrete() +
stat_summary(fun=mean, geom="errorbar",aes(ymax=..y.., ymin=..y.., x=0),
width=5, colour="red") +
width=5, colour="red", linewidth =1.5) +
labs(x="", y="travel time [s]", title=paste("Boxplot KEXI Travel Time for dataset", names[i])) +
#labs(x="", y="Reisezeit [s]", title=paste("Verteilung der KEXI Reisezeit\n ab 2022")) +
# labs(x="", y="travel time [s]") + #for paper only
theme(plot.title = element_text(hjust=0.5, size=10, face="bold"), axis.text.y = element_text(size=8),
axis.title.y = element_text(size=15, face="bold"))
theme(plot.title = element_text(hjust=0.5, size=28, face="bold"),
axis.text.y = element_text(size=22),
axis.title.y = element_text(size=26, face="bold"))

boxplot_TravelTime_s
plotFile = paste0("plots/",names[i],"/boxplot_KEXI_travel_time_s.png")
paste0("printing plot to ", plotFile)
ggsave(plotFile, limitsize = FALSE)
Expand Down Expand Up @@ -246,12 +249,15 @@ for(dataset in datasets) {
scale_y_continuous(n.breaks = 10) +
scale_x_discrete() +
stat_summary(fun=mean, geom="errorbar",aes(ymax=..y.., ymin=..y.., x=0),
width=5, colour="red") +
labs(x="", y="travel distance [m]", title=paste("Boxplot KEXI Travel Distance for dataset", names[i])) +
width=5, colour="red", linewidth =1.5) +
#labs(x="", y="travel distance [m]", title=paste("Boxplot KEXI Travel Distance for dataset", names[i])) +
labs(x="", y="Haltestellendistanz [m]", title=paste("Verteilung der Haltestellendistanz\n ab 2022")) +
# labs(x="", y="travel distance [m]") + #for paper only
theme(plot.title = element_text(hjust=0.5, size=10, face="bold"), axis.text.y = element_text(size=8),
axis.title.y = element_text(size=15, face="bold"))
theme(plot.title = element_text(hjust=0.5, size=28, face="bold"),
axis.text.y = element_text(size=22),
axis.title.y = element_text(size=26, face="bold"))

boxplot_distance_m
plotFile = paste0("plots/",names[i],"/boxplot_KEXI_travel_distance_m.png")
paste0("printing plot to ", plotFile)
ggsave(plotFile, limitsize = FALSE)
Expand Down Expand Up @@ -304,12 +310,15 @@ for(dataset in datasets) {
scale_y_continuous(n.breaks = 8) +
scale_x_discrete() +
stat_summary(fun=mean, geom="errorbar",aes(ymax=..y.., ymin=..y.., x=0),
width=5, colour="red") +
width=5, colour="red", linewidth =1.5) +
labs(x="", y="bookings", title=paste("Boxplot KEXI bookings per day for dataset", names[i])) +
#labs(x="", y="# Buchungen", title=paste("Verteilung der Anzahl beförderter\n Buchungen im konv. KEXI ab 2022")) +
# labs(x="", y="travel distance [m]") + #for paper only
theme(plot.title = element_text(hjust=0.5, size=10, face="bold"), axis.text.y = element_text(size=8),
axis.title.y = element_text(size=15, face="bold"))

theme(plot.title = element_text(hjust=1, size=28, face="bold"),
axis.text.y = element_text(size=22),
axis.title.y = element_text(size=26, face="bold"))

boxplot_daily_bookings
plotFile = paste0("plots/",names[i],"/boxplot_KEXI_daily_bookings.png")
paste0("printing plot to ", plotFile)
ggsave(plotFile, limitsize = FALSE)
Expand Down
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