diff --git a/pom.xml b/pom.xml
index ac713775..28b6112f 100644
--- a/pom.xml
+++ b/pom.xml
@@ -6,7 +6,7 @@
matsim-all
- 2025.0-PR3402
+ 2025.0-PR3501
@@ -15,7 +15,7 @@
4.0.0
com.github.matsim-scenarios
matsim-kelheim
- 3.x-SNAPSHOT
+ 2024.2-SNAPSHOT
MATSim Kelheim Model
A transport model of the district of Kelheim and it's surroundings
@@ -212,7 +212,7 @@
org.apache.avro
avro
- 1.11.3
+ 1.12.0
diff --git a/src/main/R/drtAnalysis/avConfiguration_modalShiftAnalysis.R b/src/main/R/drtAnalysis/avConfiguration_modalShiftAnalysis.R
new file mode 100644
index 00000000..eaebee4b
--- /dev/null
+++ b/src/main/R/drtAnalysis/avConfiguration_modalShiftAnalysis.R
@@ -0,0 +1,325 @@
+library(sf)
+library(matsim)
+library(tidyverse)
+library(plotly)
+
+
+######################################################################################
+####################### functions #######################################
+
+#copied from matsim-r and modified
+sankey <- function (trips_table1, trips_table2, show_onlychanges = FALSE,
+ unite_modes = character(0), united_name = "united")
+{
+ trips_table1 <- process_rename_mainmodes(trips_table = trips_table1,
+ unite_modes = unite_modes, united_name = united_name)
+ trips_table2 <- process_rename_mainmodes(trips_table = trips_table2,
+ unite_modes = unite_modes, united_name = united_name)
+ joined <- as_tibble(inner_join(trips_table1, trips_table2 %>%
+ select(trip_id, main_mode), by = "trip_id") %>% dplyr::rename(base_mode = main_mode.x,
+ policy_mode = main_mode.y))
+ if (show_onlychanges == TRUE) {
+ joined <- joined %>% filter(base_mode != policy_mode)
+ }
+ joined <- joined %>% group_by(base_mode, policy_mode) %>%
+ count()
+ modes = sort(unique(c(joined$base_mode, joined$policy_mode)))
+ num_modes <- length(modes)
+ joined$base_mode <- as.numeric(factor(joined$base_mode,
+ levels = modes,
+ ordered = TRUE))
+ joined$policy_mode <- as.numeric(factor(joined$policy_mode,
+ levels = modes,
+ ordered = TRUE))
+ palette <- colorRampPalette(c("blue", "yellow", "red"))(num_modes)
+ fig <- plot_ly(type = "sankey",
+ orientation = "h",
+ node = list(label = c(modes,modes),
+ color = c(palette, palette),
+ pad = 15, thickness = 20,
+ line = list(color = "black", width = 0.5)),
+ link = list(source = joined$base_mode - 1,
+ target = joined$policy_mode + num_modes - 1,
+ value = joined$n)) %>%
+ layout(title = list(text = "Basic Sankey Diagram",
+ font = list(size = 24, weight = "bold"), # Fettschrift und große Schriftgröße
+ x = 0.5, # Zentriert den Titel horizontal
+ y = 0.95, # Positioniert den Titel vertikal etwas weiter unten (0.95 = 95% der Höhe)
+ xref = "paper",
+ yref = "container"),
+ margin = list(t = 90, r = 50, l = 50), # Vergrößert den oberen Rand, um Platz für den Titel zu schaffen
+ font = list(size = 18, weight = "bold"))
+ fig
+ return(fig)
+}
+
+
+######################################################################################
+####################### INPUT #######################################
+
+drtArea <- st_read("D:/public-svn/matsim/scenarios/countries/de/kelheim/shp/prepare-network/av-and-drt-area.shp")
+
+no_kexi_trips <- read_output_trips("E:/matsim-kelheim/v3.0-release/output-base/25pct")
+
+## Zielwert der KEXI Kalibrierung waren 159 Passagiere, erreichter Mittelwert über 5 Seeds 157.4 Passagiere.
+## Seed 4 hat 155 rides und ist damit sehr repräsentativ für den Case "nur konv. KEXI" bzw. am nächsten dran am Durchschnitt aller 5 seeds.
+## ein anderer Kandidat waere seed-3 mit 151 rides
+nur_konv_trips <- read_output_trips("E:/matsim-kelheim/v3.0-release/output-KEXI/seed-4-kexi") %>%
+ mutate(main_mode = recode(main_mode,
+ "av" = "AV KEXI",
+ "drt" = "Konv. KEXI",
+ "pt_w_drt_used" = "KEXI + pt"))
+
+## der case SAR2023 AV2 3.3mps ist die Kalibrierungsgrundlage für den AV im Jahr 2024
+## mit dem Zielwert von 2,7 Buchungen pro Tag und 2,6 simulierten Buchungen über 5 seeds.
+## im Schnitt haben die 5 seeds konventionelle KEXI Passagiere (JAR-Wechsel) :/ ..
+## seed-1 hat 2 AV-Buchungen, 151
+## seed-2 hat 3 AV-Buchungen, 144 konv. Pax
+## seed-3 hat 3 AV-Buchungen, 154 konv. Pax
+## seed-4 hat 3 AV-Buchungen, 145 konv. Pax
+## seed-5 hat 2 AV-Buchungen, 170 konv. Pax
+
+## repraesentativ ist also vor allem seed-3
+av_base_trips <- read_output_trips("E:/matsim-kelheim/v3.1.1/output-KEXI-2.45-AV--0.0/AV-speed-mps-3.3/SAR2023-AV2/seed-3-SAR2023") %>%
+ mutate(main_mode = recode(main_mode,
+ "av" = "AV KEXI",
+ "drt" = "Konv. KEXI",
+ "pt_w_drt_used" = "KEXI + pt"))
+
+## weil es hier um Policy Cases bzgl der !AV!-Auslegung geht und weil der AV-Base-Case oben recht präzise noch die Zahlen des konv. KEXI trifft,
+## ist das unser Bezugsfall
+
+########################################################################################
+###### PROGNOSEFÄLLE
+
+# SAR-AV2-mps3.3-allDay hat im Schnitt 11.2 AV-Buchungen und 138.4 KEXI-Passagiere
+# seed-1 hat 10 / 143
+# seed-2 hat 10 / 140
+# seed-3 hat 9 / 125
+# seed-4 hat 14 / 129
+# seed-5 hat 13 / 155
+av2_3.3mps_allDay_trips <-
+ read_output_trips("E:/matsim-kelheim/v3.1.1/output-KEXI-2.45-AV--0.0/AV-speed-mps-3.3/SAR2023-AV2-allDay/seed-1-SAR2023-allDay") %>%
+ mutate(main_mode = recode(main_mode,
+ "av" = "AV KEXI",
+ "drt" = "Konv. KEXI",
+ "pt_w_drt_used" = "KEXI + pt"))
+
+# wIEKEXI-AV2-mps3.3 hat im Schnitt 3.4 AV-Buchungen und 142 KEXI-Passagiere
+# seed-1 hat 3 / 141
+# seed-2 hat 1 / 132
+# seed-3 hat 6 / 145
+# seed-4 hat 3 / 150
+# seed-5 hat 4 / 142
+av2_3.3mps_largeArea_trips <-
+ read_output_trips("E:/matsim-kelheim/v3.1.1/output-KEXI-2.45-AV--0.0/AV-speed-mps-3.3/WIEKEXI-AV2-intermodal/seed-5-WIEKEXI") %>%
+ mutate(main_mode = recode(main_mode,
+ "av" = "AV KEXI",
+ "drt" = "Konv. KEXI",
+ "pt_w_drt_used" = "KEXI + pt"))
+
+# av2_8.3mps_trips hat im Schnitt 40 AV-Buchungen und 135 KEXI-Passagiere
+# seed-1 hat 43 / 128
+# seed-2 hat 35 / 135
+# seed-3 hat 36 / 126
+# seed-4 hat 35 / 149
+# seed-5 hat 51 / 137
+av2_8.3mps_trips <-
+ read_output_trips("E:/matsim-kelheim/v3.1.1/output-KEXI-2.45-AV--0.0/AV-speed-mps-8.3//SAR2023-AV2/seed-1-SAR2023") %>%
+ mutate(main_mode = recode(main_mode,
+ "av" = "AV KEXI",
+ "drt" = "Konv. KEXI",
+ "pt_w_drt_used" = "KEXI + pt"))
+
+
+# der groeßte AV case mit aktueller geschwindigkeit. Im schnitt haben wir 247.4 simulierte AV-Buchungen und 125 konv. KEXI-Passagiere.
+# seed-5 hat 245 / 124
+av100_3.3mps_allDay_largeArea_trips <-
+ read_output_trips("E:/matsim-kelheim/v3.1.1/output-KEXI-2.45-AV--0.0/AV-speed-mps-3.3/WIEKEXI-AV100-intermodal-allDay/seed-5-WIEKEXI-allDay") %>%
+ mutate(main_mode = recode(main_mode,
+ "av" = "AV KEXI",
+ "drt" = "Konv. KEXI",
+ "pt_w_drt_used" = "KEXI + pt"))
+
+
+# der groeßte AV case überhaupt. Im schnitt haben wir 1105.4 simulierte AV-Buchungen und 136.2 konv. KEXI-Passagiere.
+# seed-5 hat 1082 / 138
+# seed-4 hat 1114 / 146
+# seed-4 hat 1144 / 138
+# seed-2 hat 1100 / 126
+# seed-1 hat 1087 / 133
+av100_8.3mps_allDay_largeArea_trips <-
+ read_output_trips("E:/matsim-kelheim/v3.1.1/output-KEXI-2.45-AV--0.0/AV-speed-mps-8.3/WIEKEXI-AV100-intermodal-allDay/seed-2-WIEKEXI-allDay") %>%
+ mutate(main_mode = recode(main_mode,
+ "av" = "AV KEXI",
+ "drt" = "Konv. KEXI",
+ "pt_w_drt_used" = "KEXI + pt"))
+
+# SAR-AV50-mps3.3 hat im Schnitt 64.8 AV-Buchungen und 138 KEXI-Passagiere
+# seed-1 hat 65 / 148
+# seed-2 hat 65 / 138
+# seed-3 hat 66 / 128
+# seed-4 hat 61 / 134
+# seed-5 hat 67 / 142
+av50_3.3mps_trips <-
+ read_output_trips("E:/matsim-kelheim/v3.1.1/output-KEXI-2.45-AV--0.0/AV-speed-mps-3.3/SAR2023-AV50/seed-2-SAR2023") %>%
+ mutate(main_mode = recode(main_mode,
+ "av" = "AV KEXI",
+ "drt" = "Konv. KEXI",
+ "pt_w_drt_used" = "KEXI + pt"))
+
+# wIEKEXI-AV50-mps8.3-allDay hat im Schnitt 1093.4 AV-Buchungen und 141.6 KEXI-Passagiere
+# seed-1 hat 1081 / 148
+# seed-2 hat 1096 / 153
+# seed-3 hat 1099 / 143
+# seed-4 hat 1099 / 130
+# seed-5 hat 1092 / 134
+av50_8.3mps_largeArea_allDay_trips <-
+ read_output_trips("E:/matsim-kelheim/v3.1.1/output-KEXI-2.45-AV--0.0/AV-speed-mps-8.3/WIEKEXI-AV50-intermodal-allDay/seed-3-WIEKEXI-allDay") %>%
+ mutate(main_mode = recode(main_mode,
+ "av" = "AV KEXI",
+ "drt" = "Konv. KEXI",
+ "pt_w_drt_used" = "KEXI + pt"))
+
+######################## FILTERN ######################
+########################################################
+
+
+
+######################## VARIANTE 1: FILTERE DRT TRIPS AUS DEN POLICY CASES. DANN PLOTTE MODAL SHIFT
+######################## (WO KOMMEN DIE DRT TRIPS HER ??)
+########################################################
+
+drt_modes <- c ("drt", "av", "pt_w_drt_used", "AV KEXI", "KEXI + pt", "Konv. KEXI")
+
+av_base_trips_drt <- av_base_trips %>%
+ filter(main_mode %in% drt_modes)
+
+av2_3.3mps_allDay_trips_drt <- av2_3.3mps_allDay_trips %>%
+ filter(main_mode %in% drt_modes)
+
+av2_3.3mps_largeArea_trips_drt <- av2_3.3mps_largeArea_trips %>%
+ filter(main_mode %in% drt_modes)
+
+av2_8.3mps_trips_drt <- av2_8.3mps_trips %>%
+ filter(main_mode %in% drt_modes)
+
+av50_3.3mps_trips_drt <- av50_3.3mps_trips %>%
+ filter(main_mode %in% drt_modes)
+
+av100_3.3mps_allDay_largeArea_trips_drt <- av100_3.3mps_allDay_largeArea_trips %>%
+ filter(main_mode %in% drt_modes)
+
+av100_8.3mps_allDay_largeArea_trips_drt <- av100_8.3mps_allDay_largeArea_trips %>%
+ filter(main_mode %in% drt_modes)
+
+av50_8.3mps_largeArea_allDay_trips_drt <- av50_8.3mps_largeArea_allDay_trips %>%
+ filter(main_mode %in% drt_modes)
+
+
+###################################
+####PLOTS
+sankey(no_kexi_trips, av_base_trips_drt) %>%
+ layout(title = "Basisfall (ohne KEXI)\n vs. Status Quo Mai 2024")
+
+sankey(no_kexi_trips, av2_3.3mps_largeArea_trips_drt) %>%
+ layout(title = "Basisfall (ohne KEXI)\n vs. Vergrößerung Bediengebiet")
+
+#sankey(av_base_trips, av2_3.3mps_largeArea_trips_drt) %>%
+# layout(title = "Status Quo (Mai 2024)\n vs. Vergrößerung Bediengebiet")
+
+sankey(no_kexi_trips, av2_8.3mps_trips_drt) %>%
+ layout(title = "Basisfall (ohne KEXI)\n vs. Beschleunigung auf 30 km/h")
+
+#sankey(av_base_trips, av2_8.3mps_trips_drt) %>%
+# layout(title = "Status Quo (Mai 2024)\n vs. Beschleunigung auf 30 km/h")
+
+sankey(no_kexi_trips, av2_3.3mps_allDay_trips_drt) %>%
+ layout(title = "Basisfall (ohne KEXI)\n vs. Ganztägiger Betrieb")
+
+#sankey(av_base_trips, av2_3.3mps_allDay_trips_drt) %>%
+# layout(title = "Status Quo (Mai 2024)\n vs. Ganztägiger Betrieb")
+
+sankey(no_kexi_trips, av50_3.3mps_trips_drt) %>%
+ layout(title = "Basisfall (ohne KEXI)\n vs. Große Flotte (50 AV)")
+
+#sankey(av_base_trips, av50_3.3mps_trips_drt) %>%
+# layout(title = "Status Quo (Mai 2024)\n vs. Große Flotte (50 AV)")
+
+sankey(no_kexi_trips, av50_8.3mps_largeArea_allDay_trips_drt) %>%
+ layout(title = "Basisfall (ohne KEXI)\n vs. Alle Maßnahmen")
+
+sankey(av_base_trips, av50_8.3mps_largeArea_allDay_trips_drt) %>%
+ layout(title = "Status Quo (Mai 2024)\n vs. Alle Maßnahmen")
+
+
+
+
+
+sankey(av_base_trips, av100_3.3mps_allDay_largeArea_trips_drt)
+sankey(no_kexi_trips, av100_3.3mps_allDay_largeArea_trips_drt)
+
+
+######################## VARIANTE 2: Räumliches filtern nach Eimnzugsgebiet
+########################################################
+#filter trips auf einzugsgebiet des konventionellen KEXI = Stadtgebiet.
+no_kexi_trips_spatial <- no_kexi_trips %>%
+ process_filter_by_shape(shape_table = drtArea, crs = 25832)
+
+nur_konv_trips_spatial <- nur_konv_trips %>%
+ process_filter_by_shape(shape_table = drtArea, crs = 25832)
+
+av_base_trips_spatial <- av_base_trips %>%
+ process_filter_by_shape(shape_table = drtArea, crs = 25832)
+
+av100_3.3mps_allDay_largeArea_trips_spatial <- av100_3.3mps_allDay_largeArea_trips %>%
+ process_filter_by_shape(shape_table = drtArea, crs = 25832)
+
+av100_8.3mps_allDay_largeArea_trips_spatial <- av100_8.3mps_allDay_largeArea_trips %>%
+ process_filter_by_shape(shape_table = drtArea, crs = 25832)
+
+
+
+
+##################################################
+###PLOTS
+
+#kein KEXI
+p <- plot_mainmode_piechart(no_kexi_trips)
+p <- p %>% layout(title = "Kein KEXI")
+p
+
+#AV-Base
+p <- plot_mainmode_barchart(av_base_trips)
+p <- p %>% layout(title = "AV Base Case")
+p
+
+#av100_3.3mps_allDay_largeArea
+p <- plot_mainmode_piechart(av100_3.3mps_allDay_largeArea_trips)
+p <- p %>% layout(title = "av100_3.3mps_allDay_largeArea")
+p
+
+#av100_8.3mps_allDay_largeArea
+p <- plot_mainmode_piechart(av100_8.3mps_allDay_largeArea_trips)
+p <- p %>% layout(title = "av100_8.3mps_allDay_largeArea")
+p
+
+
+####TODO:
+#filter for trips that are drt or AV in the policy and then display sankey for those only.
+
+#av100_3.3mps_allDay_largeArea VERSUS av_base
+plot_compare_mainmode_barchart(av_base_trips, av100_3.3mps_allDay_largeArea_trips)
+plot_compare_mainmode_sankey(av_base_trips, av100_3.3mps_allDay_largeArea_trips, show_onlychanges = TRUE)
+
+#av100_8.3mps_allDay_largeArea VERSUS av_base
+plot_compare_mainmode_barchart(av_base_trips, av100_8.3mps_allDay_largeArea_trips)
+plot_compare_mainmode_sankey(av_base_trips, av100_8.3mps_allDay_largeArea_trips, show_onlychanges = TRUE)
+
+
+#base vs large AV
+plot_compare_mainmode_barchart(base_filtered, largeAV_filtered)
+plot_compare_mainmode_sankey(base_filtered, largeAV_filtered, show_onlychanges = TRUE)
+plot_mainmode_piechart(largeAV_filtered)
+
+matsim::plot_map_trips(kexi_filtered, crs = 25832)
diff --git a/src/main/R/drtAnalysis/av_modalShiftAnalysis.R b/src/main/R/drtAnalysis/badWeather_av_modalShiftAnalysis.R
similarity index 100%
rename from src/main/R/drtAnalysis/av_modalShiftAnalysis.R
rename to src/main/R/drtAnalysis/badWeather_av_modalShiftAnalysis.R
diff --git a/src/main/R/drtAnalysis/plotRunSummaries.R b/src/main/R/drtAnalysis/plotRunSummaries.R
index cf2f5021..c3f8e300 100644
--- a/src/main/R/drtAnalysis/plotRunSummaries.R
+++ b/src/main/R/drtAnalysis/plotRunSummaries.R
@@ -4,27 +4,32 @@ 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, sep =",")
-#names(transposed_result) <- make.names(names(transposed_result), unique = TRUE, allow_ = FALSE)
+#set to true for AV and FALSE for conv. KEXI
+stats_for_AV = TRUE
-# Betriebszeiten umschreiben
-transposed_result$Betriebszeiten <- factor(ifelse(transposed_result$Betriebszeiten == TRUE, "0h - 24h", "9h - 16h"),
- levels = c("9h - 16h", "0h - 24h"))
+################################################################################################
+################################################################################################
-###
-#in Realität haben wir eine avg gruppengr0eße von 1.7 gemessen, diese aber nicht simuliert.
-# wir rechnen die jetzt im nachhinein wieder drauf.
-## --> machen wir jetzt im skript, dass die daten rausschreibt (readValuesFromRunSammaries.R)
+if (stats_for_AV){
+ input_file <- paste(mainDir, "results-av-deutsch.csv", sep="")
+} else {
+ input_file <- paste(mainDir, "results-konvKEXI-deutsch.csv", sep="")
+}
+
+
+transposed_result <- read.csv(input_file, check.names = FALSE, sep =",")
-#transposed_result <- transposed_result %>%
-# mutate(`Anzahl Passagiere` = `Bediente Anfragen` * 1.7,
-# `Gesamt Passagierkilometer [km]` = `Gesamt Passagierkilometer [km]` * 1.7) %>%
-# mutate(`Passagiere pro Fahrzeug` = `Anzahl Passagiere` / Fahrzeuge,
-# `Passagiere pro Fahrzeugkilometer` = `Anzahl Passagiere` / `Summe Fahrzeugkilometer [km]`,
-# `Passagiere pro Fahrzeugstunde` = `Anzahl Passagiere` / `Summe Fzg.-Betriebsstunden`,
-# `Besetzungsgrad [pax-km/v-km]` = `Gesamt Passagierkilometer [km]` / `Summe Fahrzeugkilometer [km]`)#
+#Bediengebiete umkodieren
+transposed_result <- transposed_result %>%
+ mutate(Bediengebiet = recode(Bediengebiet,
+ "ALLCITY" = "konv. KEXI\n schlechte Wartepunkte",
+ "SAR2023" = "AV 2024",
+ "WIEKEXI" = "konv. KEXI"))
+# Betriebszeiten umschreiben und faktorisieren
+transposed_result$Betriebszeiten <- factor(ifelse(transposed_result$Betriebszeiten == TRUE, "0h - 24h", "9h - 16h"),
+ levels = c("9h - 16h", "0h - 24h"))
results <- transposed_result %>%
gather(key = "parameter", value = "mean", -Geschwindigkeit, -Bediengebiet, -Flottengroeße, -Intermodal, -Betriebszeiten)
@@ -42,6 +47,16 @@ plotByConfiguration <- function(parameterStr, scales = "free"){
paste(round(as.numeric(value) * 3.6, 0), "km/h")
}
+ if (stats_for_AV){
+ plot_title <- paste("AV KEXI:",
+ parameterStr,
+ "nach AV-Konfiguration\n (Geschwindigkeit, Flottengröße, Bediengebiet und Betriebszeiten)")
+ } else {
+ plot_title <- paste("KONV. KEXI:",
+ parameterStr,
+ "nach AV-Konfiguration\n (Geschwindigkeit, Flottengröße, Bediengebiet und Betriebszeiten)")
+ }
+
# Erstellen des Facet-Plots
ggplot(plot_data, aes(x = Flottengroeße, y = mean, color = Bediengebiet, linetype = as.factor(Betriebszeiten), group = interaction(Bediengebiet, Betriebszeiten))) +
geom_line(size = 1.2) +
@@ -52,13 +67,14 @@ plotByConfiguration <- function(parameterStr, scales = "free"){
labeller = labeller(Geschwindigkeit = label_function)
,scales = scales
) +
- labs(title = paste(parameterStr, "nach Geschwindigkeit, Flottengröße,\nBediengebiet und Betriebszeiten"),
+ labs(title = plot_title,
x = "Flottengröße",
y = parameterStr,
color = "Bediengebiet",
linetype = "Betriebszeiten"
#,shape = "Intermodal"
) +
+ #geom_text(aes(label = Flottengroeße), vjust = -1, hjust = 0.5, size = 3, color = "black") +
#theme_dark() +
theme(
plot.title = element_text(size = 20, face = "bold"), # Titelgröße anpassen
@@ -73,12 +89,22 @@ plotByConfiguration <- function(parameterStr, scales = "free"){
}
save <- function(fileName){
- ggsave(filename = paste(mainDir, "plots/", fileName, ".png", sep = ""),
+ if (stats_for_AV){
+ output_file <- paste(mainDir, "plots/AV/", fileName, "-AV.png", sep = "")
+ } else {
+ output_file <- paste(mainDir, "plots/konvKEXI/", fileName, "-konvKEXI.png", sep = "")
+ }
+ ggsave(filename = output_file,
dpi = 600, width = 32, height = 18, units = "cm")
}
unique(results$parameter)
+results <- results %>%
+ filter(Bediengebiet != "konv. KEXI\n schlechte Wartepunkte",
+ Flottengroeße < 150)
+
+
###nachfrage
plotByConfiguration("Bediente Anfragen")
save("bedienteAnfragen")
@@ -86,6 +112,9 @@ plotByConfiguration("Anzahl Passagiere", "fixed")
save("passagiere")
plotByConfiguration("Mittl. Wartezeit [s]", "fixed")
save("wartezeit")
+plotByConfiguration("Mittl. Gesamtreisezeit [s]", "fixed")
+
+
plotByConfiguration("Umwegfaktor", "fixed")
plotByConfiguration("Mittl. Reiseweite [km]", "fixed")
save("reiseweite")
@@ -94,15 +123,15 @@ save("leerkilometer")
###betrieb
-plotByConfiguration("Summe Fahrzeugkilometer [km]")
+plotByConfiguration("Summe Fahrzeugkilometer [km]", "fixed")
save("fahrzeugkilometer")
-plotByConfiguration("Besetzungsgrad [pax-km/v-km]")
+plotByConfiguration("Besetzungsgrad [pax-km/v-km]", "fixed")
save("besetzungsgrad")
plotByConfiguration("Passagiere pro Fahrzeugkilometer", "fixed")
save("paxPerKM")
plotByConfiguration("Passagiere pro Fahrzeugstunde")
plotByConfiguration("Passagiere pro Fahrzeugstunde", "fixed")
-x^xsave("paxPerVehHour")
+save("paxPerVehHour")
plotByConfiguration("Gesamt Passagierkilometer [km]")
diff --git a/src/main/R/drtAnalysis/readValuesFromRunSummaries.R b/src/main/R/drtAnalysis/readValuesFromRunSummaries.R
index eb278b6d..d1fa8b84 100644
--- a/src/main/R/drtAnalysis/readValuesFromRunSummaries.R
+++ b/src/main/R/drtAnalysis/readValuesFromRunSummaries.R
@@ -23,21 +23,28 @@ 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)
+read_stats <- function(folder_path, file_name, stats_for_AV) {
+ if (stats_for_AV){
+ folder_name <- "analysis/drt-drt-av"
+ } else {
+ folder_name <- "analysis/drt-drt"
+ }
+
+ csv_path <- file.path(folder_path, folder_name, file_name)
if (file.exists(csv_path)) {
+ print(paste(Sys.time(), ": ", "reading", csv_path))
df <- read_csv(csv_path, show_col_types = FALSE)
mean_values <- df %>% select(parameter, mean)
return(mean_values)
} else {
+ print(paste(Sys.time(), ": ", "could not find ", csv_path))
return(NULL)
}
}
# Hauptfunktion zum Iterieren durch Unterordner
-process_folders <- function(main_folder, speed) {
+process_folders <- function(main_folder, speed, stats_for_AV) {
# Liste aller Unterordner im Hauptordner
subfolders <- list.dirs(main_folder, recursive = FALSE, full.names = FALSE)
@@ -49,8 +56,8 @@ process_folders <- function(main_folder, speed) {
parameters <- extract_parameters(subfolder, speed)
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")
+ demand_mean_values <- read_stats(full_path, "avg_demand_stats.csv", stats_for_AV)
+ supply_mean_values <- read_stats(full_path, "avg_supply_stats.csv", stats_for_AV)
if (!is.null(demand_mean_values) || !is.null(supply_mean_values)) {
if (!is.null(demand_mean_values)) {
@@ -91,10 +98,12 @@ mainDir <- "E:/matsim-kelheim/v3.1.1/output-KEXI-2.45-AV--0.0/"
speeds <- list(3.3, 5, 8.3)
+stats_for_AV = TRUE #set to true for AV and FALSE for conv. KEXI
+
results <- list()
for (speed in speeds) {
main_folder <- paste(mainDir, "AV-speed-mps-", speed, "/", sep="")
- runResults <- process_folders(main_folder, speed)
+ runResults <- process_folders(main_folder, speed, stats_for_AV)
results[[speed]] <- runResults
}
@@ -106,16 +115,18 @@ 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]`)
+if (stats_for_AV){
+ ###
+ #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 %>%
@@ -125,7 +136,12 @@ results <- transposed_result %>%
print(results)
print(transposed_result)
-write_csv(transposed_result, paste(mainDir, "results.csv", sep=""))
+if (stats_for_AV){
+ output_file <- "results-av.csv"
+} else {
+ output_file <- "results-konvKEXI.csv"
+}
+write_csv(transposed_result, paste(mainDir, output_file, sep=""))
#####################################################################
######PLOTS####
@@ -144,13 +160,13 @@ plotByConfiguration <- function(parameterStr){
# Erstellen des Facet-Plots
ggplot(plot_data, aes(x = fleetSize, y = mean, color = area, linetype = as.factor(allDay), group = interaction(area, allDay))) +
- geom_line(size = 1.2) +
+ geom_line(linewidth = 1.2) +
geom_point(size = 3,
#aes(shape = as.factor(intermodal))
) +
facet_wrap(~ speed,
labeller = labeller(speed = label_function)
- ,scales = "free"
+ ,scales = "fixed"
) +
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"),
@@ -201,7 +217,7 @@ plotByConfiguration("Pax per veh-km")
# Erstellen des Facet-Plots
facet_plot <- ggplot(plot_data, aes(x = avg_wait_time, y = handled_requests, color = area, linetype = as.factor(allDay), group = interaction(area, allDay))) +
- geom_line(size = 1.2) +
+ geom_line(linewidth = 1.2) +
geom_point(size = 3
#,aes(shape = as.factor(intermodal))
) +
diff --git a/src/main/R/modeAnalysis-kexiArea.R b/src/main/R/modeAnalysis-kexiArea.R
deleted file mode 100644
index cde5523d..00000000
--- a/src/main/R/modeAnalysis-kexiArea.R
+++ /dev/null
@@ -1,38 +0,0 @@
-library(sf)
-library(matsim)
-library(tidyverse)
-library(plotly)
-
-drtArea <- st_read("D:/public-svn/matsim/scenarios/countries/de/kelheim/shp/prepare-network/av-and-drt-area.shp")
-
-baseTrips <- read_output_trips("E:/matsim-kelheim/v3.0-release/output-base/25pct")
-kexiTrips <- read_output_trips("E:/matsim-kelheim/v3.0-release/output-KEXI/seed-3-kexi")
-largeAVTrips <- read_output_trips("E:/matsim-kelheim/v3.1.1/output-KEXI-2.45-AV--0.0/AV-speed-mps-8.3/ALLCITY-AV100-intermodal/seed-1-ALLCITY")
-
-
-
-base_filtered <- baseTrips %>%
- process_filter_by_shape(shape_table = drtArea, crs = 25832)
-kexi_filtered <- kexiTrips %>%
- process_filter_by_shape(shape_table = drtArea, crs = 25832)
-largeAV_filtered <- largeAVTrips %>%
- process_filter_by_shape(shape_table = drtArea, crs = 25832)
-
-
-#base
-p <- plot_mainmode_piechart(base_filtered)
-p2 <- p %>% layout(title = "Base Case")
-p2
-
-#base vs KEXI
-plot_compare_mainmode_barchart(base_filtered, kexi_filtered)
-plot_compare_mainmode_sankey(base_filtered, kexi_filtered, show_onlychanges = TRUE)
-plot_mainmode_piechart(kexi_filtered)
-
-
-#base vs large AV
-plot_compare_mainmode_barchart(base_filtered, largeAV_filtered)
-plot_compare_mainmode_sankey(base_filtered, largeAV_filtered, show_onlychanges = TRUE)
-plot_mainmode_piechart(largeAV_filtered)
-
-matsim::plot_map_trips(kexi_filtered, crs = 25832)
diff --git a/src/main/java/org/matsim/analysis/postAnalysis/NoiseAverageAnalysis.java b/src/main/java/org/matsim/analysis/postAnalysis/NoiseAverageAnalysis.java
index 2749a67a..e4526db4 100644
--- a/src/main/java/org/matsim/analysis/postAnalysis/NoiseAverageAnalysis.java
+++ b/src/main/java/org/matsim/analysis/postAnalysis/NoiseAverageAnalysis.java
@@ -4,9 +4,9 @@
import org.apache.avro.file.DataFileReader;
import org.apache.avro.file.DataFileWriter;
import org.apache.avro.file.FileReader;
-import org.apache.avro.generic.GenericData;
import org.apache.avro.generic.GenericDatumReader;
import org.apache.avro.generic.GenericRecord;
+import org.apache.avro.generic.PrimitivesArrays;
import org.apache.avro.io.DatumWriter;
import org.apache.avro.specific.SpecificDatumWriter;
import org.apache.avro.util.Utf8;
@@ -215,10 +215,8 @@ private XYTData calcAvroMeans(List recordList, String dataFieldNa
getCoordData(object1, xCoords);
getCoordData(object2, yCoords);
-
-// TODO: for the example data even for the hourly data there was only one time stamp. This might be different with real data. this needs to be checked
- if (object3 instanceof GenericData.Array) {
- List ints = new ArrayList<>((GenericData.Array) object3);
+ if (object3 instanceof PrimitivesArrays.IntArray intArray) {
+ List ints = new ArrayList<>(intArray);
if (!timeStamps.equals(ints)) {
if (timeStamps.isEmpty()) {
@@ -235,8 +233,8 @@ private XYTData calcAvroMeans(List recordList, String dataFieldNa
List values = new ArrayList<>();
for (Map.Entry entry : ((HashMap) object4).entrySet()) {
- if (entry.getKey() instanceof Utf8 && entry.getKey().toString().equals(dataFieldName) && entry.getValue() instanceof GenericData.Array) {
- values.addAll((GenericData.Array) entry.getValue());
+ if (entry.getKey() instanceof Utf8 && entry.getKey().toString().equals(dataFieldName) && entry.getValue() instanceof PrimitivesArrays.FloatArray floatArray) {
+ values.addAll(floatArray);
String entryString = ((Utf8) entry.getKey()).toString();
@@ -265,8 +263,8 @@ private XYTData calcAvroMeans(List recordList, String dataFieldNa
}
private void getCoordData(Object object, List target) {
- if (object instanceof GenericData.Array) {
- List floats = new ArrayList<>((GenericData.Array) object);
+ if (object instanceof PrimitivesArrays.FloatArray floatArray) {
+ List floats = new ArrayList<>(floatArray);
if (!target.equals(floats)) {
if (target.isEmpty()) {
diff --git a/src/main/java/org/matsim/analysis/postAnalysis/emissions/KelheimOfflineAirPollutionAnalysisByEngineInformation.java b/src/main/java/org/matsim/analysis/postAnalysis/emissions/KelheimOfflineAirPollutionAnalysisByEngineInformation.java
index 2c5ab6c9..8bcbacc5 100644
--- a/src/main/java/org/matsim/analysis/postAnalysis/emissions/KelheimOfflineAirPollutionAnalysisByEngineInformation.java
+++ b/src/main/java/org/matsim/analysis/postAnalysis/emissions/KelheimOfflineAirPollutionAnalysisByEngineInformation.java
@@ -109,10 +109,17 @@ public class KelheimOfflineAirPollutionAnalysisByEngineInformation implements MA
@CommandLine.Option(names = "--grid-size", description = "Grid size in meter", defaultValue = "250")
private double gridSize;
+ private final NumberFormat numberFormat;
//dump out all pollutants. to include only a subset of pollutants, adjust!
static List pollutants2Output = Arrays.asList(Pollutant.values());
+ public KelheimOfflineAirPollutionAnalysisByEngineInformation(){
+ numberFormat = NumberFormat.getInstance(Locale.US);
+ numberFormat.setMaximumFractionDigits(4);
+ numberFormat.setGroupingUsed(false);
+ }
+
@Override
public Integer call() throws Exception {
@@ -140,7 +147,6 @@ private void process(Config config, Scenario scenario) throws IOException {
// the following is copied from the example and supplemented...
//------------------------------------------------------------------------------
-
NetworkUtils.writeNetwork(scenario.getNetwork(), output.getPath( "emissionNetwork.xml.gz").toString());
final String eventsFile = input.getEventsPath();
@@ -301,10 +307,6 @@ private void writeLinkOutput(String linkEmissionAnalysisFile, String linkEmissio
log.info("Writing output...");
- NumberFormat nf = NumberFormat.getInstance(Locale.US);
- nf.setMaximumFractionDigits(4);
- nf.setGroupingUsed(false);
-
{
//dump link-based output files
File absolutFile = new File(linkEmissionAnalysisFile);
@@ -340,14 +342,14 @@ private void writeLinkOutput(String linkEmissionAnalysisFile, String linkEmissio
if (link2pollutants.get(linkId).get(pollutant) != null) {
emissionValue = link2pollutants.get(linkId).get(pollutant);
}
- absolutWriter.write(";" + nf.format(emissionValue));
+ absolutWriter.write(";" + numberFormat.format(emissionValue));
double emissionPerM = Double.NaN;
Link link = network.getLinks().get(linkId);
if (link != null) {
emissionPerM = emissionValue / link.getLength();
}
- perMeterWriter.write(";" + nf.format(emissionPerM));
+ perMeterWriter.write(";" + numberFormat.format(emissionPerM));
}
absolutWriter.newLine();
@@ -435,18 +437,7 @@ private void writeRaster(Network fullNetwork, Network filteredNetwork, Config co
try (CSVPrinter printer = new CSVPrinter(Files.newBufferedWriter(output.getPath("emissions_grid_per_day.xyt.csv")),
CSVFormat.DEFAULT.builder().setCommentMarker('#').build())) {
- NumberFormat nf = NumberFormat.getInstance(Locale.US);
- nf.setMaximumFractionDigits(4);
- nf.setGroupingUsed(false);
-
- String crs = ProjectionUtils.getCRS(fullNetwork);
- if (crs == null)
- crs = config.network().getInputCRS();
- if (crs == null)
- crs = config.global().getCoordinateSystem();
-
- // print coordinate system
-// printer.printComment(crs);
+// printCrsComment(fullNetwork, config, printer);
// print header
printer.print("time");
@@ -480,7 +471,7 @@ private void writeRaster(Network fullNetwork, Network filteredNetwork, Config co
printer.print(coord.getY());
double value = rasterMap.get(Pollutant.CO2_TOTAL).getValueByIndex(xi, yi);
- printer.print(nf.format(value));
+ printer.print(numberFormat.format(value));
printer.println();
}
@@ -517,18 +508,7 @@ private void writeTimeDependentRaster(Network fullNetwork, Network filteredNetwo
try (CSVPrinter printer = new CSVPrinter(IOUtils.getBufferedWriter(output.getPath("emissions_grid_per_hour.csv").toString()),
CSVFormat.DEFAULT.builder().setCommentMarker('#').build())) {
- NumberFormat nf = NumberFormat.getInstance(Locale.US);
- nf.setMaximumFractionDigits(4);
- nf.setGroupingUsed(false);
-
- String crs = ProjectionUtils.getCRS(fullNetwork);
- if (crs == null)
- crs = config.network().getInputCRS();
- if (crs == null)
- crs = config.global().getCoordinateSystem();
-
-// print coordinate system
-// printer.printComment(crs);
+// printCrsComment(fullNetwork, config, printer);
// print header
printer.print("time");
@@ -566,7 +546,7 @@ private void writeTimeDependentRaster(Network fullNetwork, Network filteredNetwo
printer.print(coord.getX());
printer.print(coord.getY());
- printer.print(nf.format(value));
+ printer.print(numberFormat.format(value));
printer.println();
}
@@ -579,4 +559,15 @@ private void writeTimeDependentRaster(Network fullNetwork, Network filteredNetwo
}
+ private static void printCrsComment(Network fullNetwork, Config config, CSVPrinter printer) throws IOException {
+ String crs = ProjectionUtils.getCRS(fullNetwork);
+ if (crs == null)
+ crs = config.network().getInputCRS();
+ if (crs == null)
+ crs = config.global().getCoordinateSystem();
+
+ //print coordinate system
+ printer.printComment(crs);
+ }
+
}
diff --git a/src/main/java/org/matsim/dashboard/AverageKelheimEmissionsDashboard.java b/src/main/java/org/matsim/dashboard/AverageKelheimEmissionsDashboard.java
index fa369af5..b5263be8 100644
--- a/src/main/java/org/matsim/dashboard/AverageKelheimEmissionsDashboard.java
+++ b/src/main/java/org/matsim/dashboard/AverageKelheimEmissionsDashboard.java
@@ -21,17 +21,12 @@
package org.matsim.dashboard;
import org.matsim.analysis.postAnalysis.emissions.EmissionsPostProcessingAverageAnalysis;
-import org.matsim.simwrapper.Dashboard;
-import org.matsim.simwrapper.Data;
-import org.matsim.simwrapper.Header;
-import org.matsim.simwrapper.Layout;
+import org.matsim.simwrapper.*;
import org.matsim.simwrapper.viz.*;
import java.util.ArrayList;
import java.util.List;
-import static org.matsim.dashboard.AverageKelheimNoiseDashboard.*;
-
/**
* Average emissions dashboard for several runs with the same config but a different random seed.
*/
@@ -130,21 +125,16 @@ public void configure(Header header, Layout layout) {
layout.row("second").el(GridMap.class, (viz, data) -> {
viz.title = "CO₂ Emissions";
viz.description = "per day. Be aware that CO2 values are provided in the simulation sample size!";
- setGridMapStandards(viz);
+ DashboardUtils.setGridMapStandards(viz, data, "EPSG:25832");
viz.file = postProcess(data, "mean_emissions_grid_per_day.xyt.csv");
});
layout.row("third")
.el(GridMap.class, (viz, data) -> {
viz.title = "CO₂ Emissions";
viz.description = "per hour. Be aware that CO2 values are provided in the simulation sample size!";
- setGridMapStandards(viz);
+ DashboardUtils.setGridMapStandards(viz, data, "EPSG:25832");
viz.file = postProcess(data, "mean_emissions_grid_per_hour.csv");
});
}
- private static void setGridMapStandards(GridMap viz) {
- viz.projection = "EPSG:25832";
- viz.setColorRamp(new double[]{30, 40, 50, 60, 70}, new String[]{DARK_BLUE, LIGHT_BLUE, YELLOW, SAND, ORANGE, RED});
- viz.height = 12.0;
- }
}
diff --git a/src/main/java/org/matsim/dashboard/AverageKelheimNoiseDashboard.java b/src/main/java/org/matsim/dashboard/AverageKelheimNoiseDashboard.java
index b4715dd9..d3d175e3 100644
--- a/src/main/java/org/matsim/dashboard/AverageKelheimNoiseDashboard.java
+++ b/src/main/java/org/matsim/dashboard/AverageKelheimNoiseDashboard.java
@@ -1,10 +1,7 @@
package org.matsim.dashboard;
import org.matsim.analysis.postAnalysis.NoiseAverageAnalysis;
-import org.matsim.simwrapper.Dashboard;
-import org.matsim.simwrapper.Data;
-import org.matsim.simwrapper.Header;
-import org.matsim.simwrapper.Layout;
+import org.matsim.simwrapper.*;
import org.matsim.simwrapper.viz.ColorScheme;
import org.matsim.simwrapper.viz.GridMap;
import org.matsim.simwrapper.viz.MapPlot;
@@ -23,12 +20,7 @@ public class AverageKelheimNoiseDashboard implements Dashboard {
private final List dirs;
private final Integer noRuns;
private static final String NOISE = "noise";
- static final String DARK_BLUE = "#1175b3";
- static final String LIGHT_BLUE = "#95c7df";
- static final String ORANGE = "#f4a986";
- static final String RED = "#cc0c27";
- static final String SAND = "#dfb095";
- static final String YELLOW = "#dfdb95";
+
public AverageKelheimNoiseDashboard(List dirs, Integer noRuns) {
this.dirs = dirs;
@@ -78,38 +70,28 @@ public void configure(Header header, Layout layout) {
.el(GridMap.class, (viz, data) -> {
viz.title = "Noise Immissions (Grid)";
viz.description = "Total Noise Immissions per day";
- setGridMapStandards(viz, data);
+ DashboardUtils.setGridMapStandards(viz, data, "EPSG:25832");
viz.file = postProcess(data, "mean_immission_per_day.avro");
})
.el(GridMap.class, (viz, data) -> {
viz.title = "Hourly Noise Immissions (Grid)";
viz.description = "Noise Immissions per hour";
- setGridMapStandards(viz, data);
+ DashboardUtils.setGridMapStandards(viz, data, "EPSG:25832");
viz.file = postProcess(data, "mean_immission_per_hour.avro");
});
layout.row("damages")
.el(GridMap.class, (viz, data) -> {
viz.title = "Daily Noise Damages (Grid)";
viz.description = "Total Noise Damages per day [€]";
- setGridMapStandards(viz, data);
+ DashboardUtils.setGridMapStandards(viz, data, "EPSG:25832");
viz.file = postProcess(data, "mean_damages_receiverPoint_per_day.avro");
})
.el(GridMap.class, (viz, data) -> {
viz.title = "Hourly Noise Damages (Grid)";
viz.description = "Noise Damages per hour [€]";
- setGridMapStandards(viz, data);
+ DashboardUtils.setGridMapStandards(viz, data, "EPSG:25832");
viz.file = postProcess(data, "mean_damages_receiverPoint_per_hour.avro");
});
}
- private static void setGridMapStandards(GridMap viz, Data data) {
- viz.height = 12.0;
- viz.cellSize = 250;
- viz.opacity = 0.1;
- viz.maxHeight = 40;
- viz.projection = "EPSG:25832";
- viz.center = data.context().getCenter();
- viz.zoom = data.context().mapZoomLevel;
- viz.setColorRamp(new double[]{30, 40, 50, 60, 70}, new String[]{DARK_BLUE, LIGHT_BLUE, YELLOW, SAND, ORANGE, RED});
- }
}
diff --git a/src/main/java/org/matsim/dashboard/KelheimEmissionsDashboard.java b/src/main/java/org/matsim/dashboard/KelheimEmissionsDashboard.java
index 8be29839..f59d55e0 100644
--- a/src/main/java/org/matsim/dashboard/KelheimEmissionsDashboard.java
+++ b/src/main/java/org/matsim/dashboard/KelheimEmissionsDashboard.java
@@ -23,7 +23,7 @@
import org.matsim.analysis.postAnalysis.emissions.KelheimOfflineAirPollutionAnalysisByEngineInformation;
import org.matsim.application.prepare.network.CreateGeoJsonNetwork;
import org.matsim.simwrapper.Dashboard;
-import org.matsim.simwrapper.Data;
+import org.matsim.simwrapper.DashboardUtils;
import org.matsim.simwrapper.Header;
import org.matsim.simwrapper.Layout;
import org.matsim.simwrapper.viz.GridMap;
@@ -95,21 +95,16 @@ public void configure(Header header, Layout layout) {
layout.row("second").el(GridMap.class, (viz, data) -> {
viz.title = "CO₂ Emissions";
viz.description = "per day. Be aware that CO2 values are provided in the simulation sample size!";
- setGridMapStandards(viz, 12.0, data.compute(KelheimOfflineAirPollutionAnalysisByEngineInformation.class, "emissions_grid_per_day.xyt.csv", new String[0]));
-
+ DashboardUtils.setGridMapStandards(viz, data, "EPSG:25832");
+ viz.file = data.compute(KelheimOfflineAirPollutionAnalysisByEngineInformation.class, "emissions_grid_per_day.xyt.csv", new String[0]);
});
layout.row("third")
.el(GridMap.class, (viz, data) -> {
viz.title = "CO₂ Emissions";
viz.description = "per hour. Be aware that CO2 values are provided in the simulation sample size!";
- setGridMapStandards(viz, 12., data.compute(KelheimOfflineAirPollutionAnalysisByEngineInformation.class, "emissions_grid_per_hour.csv"));
+ DashboardUtils.setGridMapStandards(viz, data, "EPSG:25832");
+ viz.file = data.compute(KelheimOfflineAirPollutionAnalysisByEngineInformation.class, "emissions_grid_per_hour.csv");
});
}
- private static void setGridMapStandards(GridMap viz, double height, String data) {
- viz.height = height;
- viz.projection = "EPSG:25832";
- viz.file = data;
- viz.setColorRamp(new double[]{30, 40, 50, 60, 70}, new String[]{DARK_BLUE, LIGHT_BLUE, YELLOW, SAND, ORANGE, RED});
- }
}
diff --git a/src/main/java/org/matsim/dashboard/KelheimSimWrapperRunner.java b/src/main/java/org/matsim/dashboard/KelheimSimWrapperRunner.java
index 01b3c3a6..7baec11c 100644
--- a/src/main/java/org/matsim/dashboard/KelheimSimWrapperRunner.java
+++ b/src/main/java/org/matsim/dashboard/KelheimSimWrapperRunner.java
@@ -106,7 +106,7 @@ public Integer call() throws Exception {
new DrtDashboardProvider().getDashboards(config, sw).forEach(sw::addDashboard);
}
if (noise){
- sw.addDashboard(new NoiseDashboard());
+ sw.addDashboard(new NoiseDashboard(config.global().getCoordinateSystem()));
}
try {
diff --git a/src/main/java/org/matsim/run/KelheimPtFareModule.java b/src/main/java/org/matsim/run/KelheimPtFareModule.java
deleted file mode 100644
index 2f823747..00000000
--- a/src/main/java/org/matsim/run/KelheimPtFareModule.java
+++ /dev/null
@@ -1,34 +0,0 @@
-package org.matsim.run;
-
-import org.matsim.api.core.v01.TransportMode;
-import org.matsim.core.config.ConfigUtils;
-import org.matsim.core.controler.AbstractModule;
-import playground.vsp.pt.fare.DistanceBasedPtFareHandler;
-import playground.vsp.pt.fare.DistanceBasedPtFareParams;
-import playground.vsp.pt.fare.PtFareConfigGroup;
-import playground.vsp.pt.fare.PtFareUpperBoundHandler;
-
-/**
- * Module to install classes for pt fare.
- */
-public class KelheimPtFareModule extends AbstractModule {
- @Override
- public void install() {
- // Set the money related thing in the config (planCalcScore) file to 0.
- getConfig().scoring().getModes().get(TransportMode.pt).setDailyMonetaryConstant(0);
- getConfig().scoring().getModes().get(TransportMode.pt).setMarginalUtilityOfDistance(0);
-
- // Initialize config group (and also write in the output config)
- PtFareConfigGroup ptFareConfigGroup = ConfigUtils.addOrGetModule(this.getConfig(), PtFareConfigGroup.class);
- DistanceBasedPtFareParams distanceBasedPtFareParams = ConfigUtils.addOrGetModule(this.getConfig(), DistanceBasedPtFareParams.class);
-
-
- // Add bindings
- addEventHandlerBinding().toInstance(new DistanceBasedPtFareHandler(distanceBasedPtFareParams));
- if (ptFareConfigGroup.getApplyUpperBound()) {
- PtFareUpperBoundHandler ptFareUpperBoundHandler = new PtFareUpperBoundHandler(ptFareConfigGroup.getUpperBoundFactor());
- addEventHandlerBinding().toInstance(ptFareUpperBoundHandler);
- addControlerListenerBinding().toInstance(ptFareUpperBoundHandler);
- }
- }
-}
diff --git a/src/main/java/org/matsim/run/RunKelheimScenario.java b/src/main/java/org/matsim/run/RunKelheimScenario.java
index d146f381..0a243b6e 100644
--- a/src/main/java/org/matsim/run/RunKelheimScenario.java
+++ b/src/main/java/org/matsim/run/RunKelheimScenario.java
@@ -43,6 +43,7 @@
import org.matsim.contrib.dvrp.run.DvrpModule;
import org.matsim.contrib.dvrp.run.DvrpQSimComponents;
import org.matsim.contrib.dvrp.trafficmonitoring.DvrpModeLimitedMaxSpeedTravelTimeModule;
+import org.matsim.contrib.vsp.pt.fare.PtFareModule;
import org.matsim.contrib.vsp.scenario.SnzActivities;
import org.matsim.core.api.experimental.events.EventsManager;
import org.matsim.core.config.Config;
@@ -62,8 +63,8 @@
import org.matsim.simwrapper.SimWrapperModule;
import org.matsim.vehicles.VehicleType;
import picocli.CommandLine;
-import playground.vsp.pt.fare.DistanceBasedPtFareParams;
-import playground.vsp.pt.fare.PtFareConfigGroup;
+import org.matsim.contrib.vsp.pt.fare.DistanceBasedPtFareParams;
+import org.matsim.contrib.vsp.pt.fare.PtFareConfigGroup;
import playground.vsp.scoring.IncomeDependentUtilityOfMoneyPersonScoringParameters;
import javax.annotation.Nullable;
@@ -221,16 +222,18 @@ protected Config prepareConfig(Config config) {
// Minimum fare (e.g. short trip or 1 zone ticket)
distanceBasedPtFareParams.setMinFare(2.0);
- // Division between long trip and short trip (unit: m)
- distanceBasedPtFareParams.setLongDistanceTripThreshold(50000);
- // y = ax + b --> a value, for short trips
- distanceBasedPtFareParams.setNormalTripSlope(0.00017);
- // y = ax + b --> b value, for short trips
- distanceBasedPtFareParams.setNormalTripIntercept(1.6);
- // y = ax + b --> a value, for long trips
- distanceBasedPtFareParams.setLongDistanceTripSlope(0.00025);
- // y = ax + b --> b value, for long trips
- distanceBasedPtFareParams.setLongDistanceTripIntercept(30);
+
+ distanceBasedPtFareParams.setTransactionPartner("pt-operator");
+ DistanceBasedPtFareParams.DistanceClassLinearFareFunctionParams shortDistance = distanceBasedPtFareParams.getOrCreateDistanceClassFareParams(50000);
+ shortDistance.setFareIntercept(1.6);
+ shortDistance.setFareSlope(0.00017);
+
+ DistanceBasedPtFareParams.DistanceClassLinearFareFunctionParams longDistance = distanceBasedPtFareParams.getOrCreateDistanceClassFareParams(Double.POSITIVE_INFINITY);
+ longDistance.setFareIntercept(30);
+ longDistance.setFareSlope(0.00025);
+ distanceBasedPtFareParams.setOrder(1);
+
+ ptFareConfigGroup.addParameterSet(distanceBasedPtFareParams);
//enable plan inheritance analysis
config.planInheritance().setEnabled(true);
@@ -291,7 +294,7 @@ protected void prepareControler(Controler controler) {
controler.addOverridingModule(new AbstractModule() {
@Override
public void install() {
- install(new KelheimPtFareModule());
+ install(new PtFareModule());
install(new SwissRailRaptorModule());
install(new PersonMoneyEventsAnalysisModule());
install(new SimWrapperModule());
@@ -377,17 +380,6 @@ public void install() {
// TODO: when to include AV?
//estimatorConfig.addParameterSet(new DrtEstimatorConfigGroup("av"));
-
-// if (intermodal){
-// controler.addOverridingModule(new IntermodalTripFareCompensatorsModule());
-// controler.addOverridingModule(new PtIntermodalRoutingModesModule());
-// controler.addOverridingModule(new AbstractModule() {
-// @Override
-// public void install() {
-// bind(RaptorIntermodalAccessEgress.class).to(EnhancedRaptorIntermodalAccessEgress.class);
-// }
-// });
-// }
}
}
}