From 15274f7d8753cf2c1b7192f41d48f6d85b34b8ad Mon Sep 17 00:00:00 2001 From: Johanna Baensch Date: Fri, 6 Dec 2024 13:56:22 +0100 Subject: [PATCH] Final 06. Dezember --- buildings_pois.csv | 11474 ---------------- pois.csv | 75 - pois_complete.csv | 983 ++ .../thesisJBAnalysis/Accessibility_analysis.R | 404 + src/main/R/thesisJBAnalysis/KEXI_analysis.R | 561 + src/main/R/thesisJBAnalysis/data_prep.R | 183 + src/main/R/thesisJBAnalysis/r5r.R | 93 + src/main/R/thesisJBAnalysis/readme_JB.txt | 5 + .../org/matsim/run/RunKelheimScenario.java | 68 +- supermarkets_LK.csv | 28 - 10 files changed, 2274 insertions(+), 11600 deletions(-) delete mode 100644 buildings_pois.csv delete mode 100644 pois.csv create mode 100644 pois_complete.csv create mode 100644 src/main/R/thesisJBAnalysis/Accessibility_analysis.R create mode 100644 src/main/R/thesisJBAnalysis/KEXI_analysis.R create mode 100644 src/main/R/thesisJBAnalysis/data_prep.R create mode 100644 src/main/R/thesisJBAnalysis/r5r.R create mode 100644 src/main/R/thesisJBAnalysis/readme_JB.txt delete mode 100644 supermarkets_LK.csv diff --git a/buildings_pois.csv b/buildings_pois.csv deleted file mode 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--git a/src/main/R/thesisJBAnalysis/Accessibility_analysis.R b/src/main/R/thesisJBAnalysis/Accessibility_analysis.R new file mode 100644 index 0000000..f7fcca1 --- /dev/null +++ b/src/main/R/thesisJBAnalysis/Accessibility_analysis.R @@ -0,0 +1,404 @@ +library(tidyverse) +library(sf) + +#each section is meant to run separately, wd is set and data loaded within each section. The overall pattern is always the same, but file/column names change. +#there are three shape files needed for the calculations in east Kelheim: + +buildings <- st_read("C:/Users/J/Documents/Thesis/shapefiles/Buildings_LK.shp") %>% + st_transform(crs = 25832) %>% + mutate(built = "yes") +study_area <- st_read("C:/Users/J/Documents/Thesis/shapefiles/LK_Ost.shp") %>% + st_transform(crs=25832) +grid <- st_read("C:/Users/J/Documents/Thesis/shapefiles/ek_grid.shp") +grid <- grid[study_area,] +grid_buildings <-grid[buildings,] + +#### TOWN KELHEIM - PT ALL DAY #### + +#Stadt Kelheim +setwd("~/Thesis/accessibilities/final/stadt_model1") +accessibilities_files <- list.files(pattern = "doctor", full.names = TRUE) + +##Create list of data frame names +names_long <-substr(accessibilities_files, 3, 18) +names_shrt <-substr(accessibilities_files, 15, 18) + +#OstKelheim wait =20 Supermarket +setwd("~/Thesis/accessibilities/final/ek_model1_w20") +accessibilities_files <- list.files(pattern = "supermarkt", full.names = TRUE) + +##Create list of data frame names without the ".csv" part +names_long <-substr(accessibilities_files, 3, 29) +names_shrt <-substr(accessibilities_files, 26, 29) + +###Load all files +for(i in 1:length(names_long)){ + + filepath <- file.path(paste0(names_long[i],".csv")) + assign(paste0("acc",names_shrt[i]), read.csv(filepath)) +} + +day_pt <- select(acc0862, -c("time", "car_accessibility", "estimatedDrt_accessibility")) %>% + left_join(select(acc0943, c("id", "pt_accessibility")), by = join_by(id), suffix = c(".0862", ".0943")) %>% + left_join(.,select(acc1009, c("id", "pt_accessibility")), by = join_by(id) ) %>% + rename(pt_accessibility.1009 = pt_accessibility) %>% + left_join(.,select(acc1189, c("id", "pt_accessibility")), by = join_by(id)) %>% + rename(pt_accessibility.1189 = pt_accessibility) %>% + left_join(.,select(acc1369, c("id", "pt_accessibility")), by = join_by(id)) %>% + rename(pt_accessibility.1369 = pt_accessibility)%>% + left_join(.,select(acc1398, c("id", "pt_accessibility")), by = join_by(id)) %>% + rename(pt_accessibility.1398 = pt_accessibility)%>% + left_join(.,select(acc1426, c("id", "pt_accessibility")), by = join_by(id)) %>% + rename(pt_accessibility.1426 = pt_accessibility) %>% + left_join(.,select(acc1594, c("id", "pt_accessibility")), by = join_by(id)) %>% + rename(pt_accessibility.1594 = pt_accessibility)%>% + left_join(.,select(acc1643, c("id", "pt_accessibility")), by = join_by(id)) %>% + rename(pt_accessibility.1643 = pt_accessibility) + +day_drt <- select(acc0862, -c("time", "car_accessibility", "pt_accessibility")) %>% + left_join(select(acc0943, c("id", "estimatedDrt_accessibility")), by = join_by(id), suffix = c(".0862", ".0943")) %>% + left_join(.,select(acc1009, c("id", "estimatedDrt_accessibility")), by = join_by(id) ) %>% + rename(estimatedDrt_accessibility.1009 = estimatedDrt_accessibility) %>% + left_join(.,select(acc1189, c("id", "estimatedDrt_accessibility")), by = join_by(id)) %>% + rename(estimatedDrt_accessibility.1189 = estimatedDrt_accessibility) %>% + left_join(.,select(acc1369, c("id", "estimatedDrt_accessibility")), by = join_by(id)) %>% + rename(estimatedDrt_accessibility.1369 = estimatedDrt_accessibility)%>% + left_join(.,select(acc1398, c("id", "estimatedDrt_accessibility")), by = join_by(id)) %>% + rename(estimatedDrt_accessibility.1398 = estimatedDrt_accessibility)%>% + left_join(.,select(acc1426, c("id", "estimatedDrt_accessibility")), by = join_by(id)) %>% + rename(estimatedDrt_accessibility.1426 = estimatedDrt_accessibility) %>% + left_join(.,select(acc1594, c("id", "estimatedDrt_accessibility")), by = join_by(id)) %>% + rename(estimatedDrt_accessibility.1594 = estimatedDrt_accessibility)%>% + left_join(.,select(acc1643, c("id", "estimatedDrt_accessibility")), by = join_by(id)) %>% + rename(estimatedDrt_accessibility.1643 = estimatedDrt_accessibility) + +day_car <- select(acc0862, -c("time", "estimatedDrt_accessibility", "pt_accessibility")) %>% + left_join(select(acc0943, c("id", "car_accessibility")), by = join_by(id), suffix = c(".0862", ".0943")) %>% + left_join(.,select(acc1009, c("id", "car_accessibility")), by = join_by(id) ) %>% + rename(car_accessibility.1009 = car_accessibility) %>% + left_join(.,select(acc1189, c("id", "car_accessibility")), by = join_by(id)) %>% + rename(car_accessibility.1189 = car_accessibility) %>% + left_join(.,select(acc1369, c("id", "car_accessibility")), by = join_by(id)) %>% + rename(car_accessibility.1369 = car_accessibility)%>% + left_join(.,select(acc1398, c("id", "car_accessibility")), by = join_by(id)) %>% + rename(car_accessibility.1398 = car_accessibility)%>% + left_join(.,select(acc1426, c("id", "car_accessibility")), by = join_by(id)) %>% + rename(car_accessibility.1426 = car_accessibility) %>% + left_join(.,select(acc1594, c("id", "car_accessibility")), by = join_by(id)) %>% + rename(car_accessibility.1594 = car_accessibility)%>% + left_join(.,select(acc1643, c("id", "car_accessibility")), by = join_by(id)) %>% + rename(car_accessibility.1643 = car_accessibility) + +day_pt <- day_pt %>% + rowwise() %>% + mutate( + mean = mean(c_across(pt_accessibility.0862:pt_accessibility.1643)), + min = min(c_across(pt_accessibility.0862:pt_accessibility.1643)), + max = max(c_across(pt_accessibility.0862:pt_accessibility.1643))) + +day_drt <- day_drt %>% + rowwise() %>% + mutate( + mean_DRT = mean(c_across(estimatedDrt_accessibility.0862:estimatedDrt_accessibility.1643)), + min_DRT = min(c_across(estimatedDrt_accessibility.0862:estimatedDrt_accessibility.1643)), + max_DRT = max(c_across(estimatedDrt_accessibility.0862:estimatedDrt_accessibility.1643))) + +day_car <- day_car %>% + rowwise() %>% + mutate( + mean = mean(c_across(car_accessibility.0862:car_accessibility.1643)), + min = min(c_across(car_accessibility.0862:car_accessibility.1643)), + max = max(c_across(car_accessibility.0862:car_accessibility.1643))) + +write.csv(day_pt, "day_pt.csv") +write.csv(day_drt, "day_drt.csv") +write.csv(day_car, "day_car.csv") + +avg_range_day_pt <- day_pt %>% + pivot_longer(cols = starts_with("pt"), values_to = "acc", names_to = "time") %>% + group_by(time) %>% + summarise(mean= mean(acc), + min = min(acc), + max = max(acc)) %>% + mutate(times_h = c("08:37","09:25","10:05","11:53","13:41","13:58","14:15","15:56","16:25")) %>% + pivot_longer(cols=c("mean", "min", "max"), values_to = "acc", names_to = "type") + +ggplot(avg_range_day_pt)+ + geom_line(aes(x=factor(times_h), y=acc, color = type, group = type)) + +day <- st_as_sf(day, coords = c("xcoord", "ycoord"), crs = 25832 ) +ggplot(day)+ + geom_sf(aes(color=mean))+ + scale_color_viridis_c() + +ggplot(day)+ + geom_sf(aes(color=sd))+ + scale_color_viridis_c() + + + +#### East Kelheim - SPORT #### + +#OstKelheim wait =20 Sport +setwd("~/Thesis/accessibilities/final/ek_model1_w20") +accessibilities_files <- list.files(pattern = "sport", full.names = TRUE) + +##Create list of data frame names without the ".csv" part +names_long <-substr(accessibilities_files, 3, 24) +names_shrt <-substr(accessibilities_files, 21, 24) + +###Load all files +for(i in 1:length(names_long)){ + + filepath <- file.path(paste0(names_long[i],".csv")) + #placeholder <- read.csv(filepath) + assign(paste0("acc",names_shrt[i]), read.csv(filepath)) +} + +day_pt <- select(acc1520, -c("time", "car_accessibility", "estimatedDrt_accessibility")) %>% + left_join(select(acc1585, c("id", "pt_accessibility")), by = join_by(id), suffix = c(".1520", ".1585")) %>% + left_join(.,select(acc1678, c("id", "pt_accessibility")), by = join_by(id) ) %>% + rename(pt_accessibility.1678 = pt_accessibility) %>% + left_join(.,select(acc1807, c("id", "pt_accessibility")), by = join_by(id)) %>% + rename(pt_accessibility.1807 = pt_accessibility) %>% + left_join(.,select(acc1961, c("id", "pt_accessibility")), by = join_by(id)) %>% + rename(pt_accessibility.1961 = pt_accessibility) + +day_drt <- select(acc1520, -c("time", "car_accessibility", "pt_accessibility")) %>% + left_join(select(acc1585, c("id", "estimatedDrt_accessibility")), by = join_by(id), suffix = c(".1520", ".1585")) %>% + left_join(.,select(acc1678, c("id", "estimatedDrt_accessibility")), by = join_by(id) ) %>% + rename(estimatedDrt_accessibility.1678 = estimatedDrt_accessibility) %>% + left_join(.,select(acc1807, c("id", "estimatedDrt_accessibility")), by = join_by(id)) %>% + rename(estimatedDrt_accessibility.1807 = estimatedDrt_accessibility) %>% + left_join(.,select(acc1961, c("id", "estimatedDrt_accessibility")), by = join_by(id)) %>% + rename(estimatedDrt_accessibility.1961 = estimatedDrt_accessibility) + +day_car <- select(acc1520, -c("time", "estimatedDrt_accessibility", "pt_accessibility")) %>% + left_join(select(acc1585, c("id", "car_accessibility")), by = join_by(id), suffix = c(".1520", ".1585")) %>% + left_join(.,select(acc1678, c("id", "car_accessibility")), by = join_by(id) ) %>% + rename(car_accessibility.1678 = car_accessibility) %>% + left_join(.,select(acc1807, c("id", "car_accessibility")), by = join_by(id)) %>% + rename(car_accessibility.1807 = car_accessibility) %>% + left_join(.,select(acc1961, c("id", "car_accessibility")), by = join_by(id)) %>% + rename(car_accessibility.1961 = car_accessibility) + +day_pt <- day_pt %>% + rowwise() %>% + mutate( + mean = mean(c_across(pt_accessibility.1520:pt_accessibility.1961)), + min = min(c_across(pt_accessibility.1520:pt_accessibility.1961)), + max = max(c_across(pt_accessibility.1520:pt_accessibility.1961))) + +day_drt <- day_drt %>% + rowwise() %>% + mutate( + mean_DRT = mean(c_across(estimatedDrt_accessibility.1520:estimatedDrt_accessibility.1961)), + min_DRT = min(c_across(estimatedDrt_accessibility.1520:estimatedDrt_accessibility.1961)), + max_DRT = max(c_across(estimatedDrt_accessibility.1520:estimatedDrt_accessibility.1961))) + +day_car <- day_car %>% + rowwise() %>% + mutate( + mean = mean(c_across(car_accessibility.1520:car_accessibility.1961)), + min = min(c_across(car_accessibility.1520:car_accessibility.1961)), + max = max(c_across(car_accessibility.1520:car_accessibility.1961))) + +write.csv(day_pt, "day_pt.csv") +write.csv(day_drt, "day_drt.csv") +write.csv(day_car, "day_car.csv") + +avg_range_day_pt <- day_pt %>% + pivot_longer(cols = starts_with("pt"), values_to = "acc", names_to = "time") %>% + group_by(time) %>% + summarise(mean= mean(acc), + min = min(acc), + max = max(acc)) %>% + mutate(times_h = c("15:12","15:51","16:46","18:04","19:36")) %>% + pivot_longer(cols=c("mean", "min", "max"), values_to = "acc", names_to = "type") + +ek_sport_drt <- st_as_sf(day_drt, coords = c("xcoord", "ycoord"), crs=25832) +ek_sport_pt <- st_as_sf(day_pt, coords = c("xcoord", "ycoord"), crs=25832) +ek_sport_car <- st_as_sf(day_car, coords = c("xcoord", "ycoord"), crs=25832) + +grid_buildings <-grid[buildings,] + +grid_buildings_drt <-st_join(grid_buildings,ek_sport_drt) +grid_buildings_pt <-st_join(grid_buildings,ek_sport_pt) +grid_buildings_car <-st_join(grid_buildings,ek_sport_car) + +grid_buildings$mean_car <- grid_buildings_car$mean +grid_buildings$mean_pt <- grid_buildings_pt$mean +grid_buildings$mean_drt <- grid_buildings_drt$mean_DRT + +grid_buildings <- st_join(grid_buildings, select(study_area, name)) +grid_buildings <- grid_buildings %>% + pivot_longer(cols = c("mean_car", "mean_pt", "mean_drt"), values_to = "mean", names_to = "mode") + +summ <- grid_buildings %>% + group_by(mode, name) %>% + summarize(min = min(mean), max = max(mean), + mean = mean(mean)) %>% + mutate(min = round(min,2), + max = round(max,2), + mean = round(mean,2)) %>% + st_drop_geometry() + +ggplot(grid_buildings)+ + geom_histogram(aes(x = mean, fill = mode), alpha = 0.7, position = "identity")+ + scale_fill_viridis_d(labels= c("car", "DRT", "PT"))+ + facet_wrap(vars(name))+ + xlab("mean accessibility (utils)") + + +mean(grid_buildings_drt$mean_DRT) +mean(grid_buildings_pt$mean) +mean(grid_buildings_car$mean) + +min(grid_buildings_drt$mean_DRT) +min(grid_buildings_pt$mean) +min(grid_buildings_car$mean) + +max(grid_buildings_drt$mean_DRT) +max(grid_buildings_pt$mean) +max(grid_buildings_car$mean) + + +#### East Kelheim - INDEX #### +#OstKelheim wait =20 +setwd("~/Thesis/accessibilities/final/ek_model1_w20") +accessibilities_files <- list.files(pattern = "1200", full.names = TRUE) + +##Create list of data frame names without the ".csv" part +names_long <-substr(accessibilities_files, 3, 33) +names_shrt <-substr(accessibilities_files, 3, 7) + +###Load all files +for(i in 1:length(names_long)){ + + filepath <- file.path(names_long[i]) + #placeholder <- read.csv(filepath) + assign(paste0("acc_",names_shrt[i]), read.csv(filepath)) +} + + +#glue the columns together to create one dataframe per mode with one column per time/activity and cut to size of grid_buildings +all_pt <- select(acc_ameni, -c("time", "car_accessibility", "estimatedDrt_accessibility")) %>% + left_join(select(acc_docto, c("id", "pt_accessibility")), by = join_by(id), suffix = c(".ameni", ".docto")) %>% + left_join(.,select(acc_pharm, c("id", "pt_accessibility")), by = join_by(id) ) %>% + rename(pt_accessibility.pharm = pt_accessibility) %>% + # left_join(.,select(acc_churc, c("id", "pt_accessibility")), by = join_by(id)) %>% #CHURCH COMMENT OUT IF NOT NEEDED + #rename(pt_accessibility.churc = pt_accessibility) %>% #CHURCH COMMENT OUT IF NOT NEEDED + left_join(.,select(acc_sport, c("id", "pt_accessibility")), by = join_by(id)) %>% + rename(pt_accessibility.sport = pt_accessibility) %>% + left_join(.,select(acc_super, c("id", "pt_accessibility")), by = join_by(id)) %>% + rename(pt_accessibility.super = pt_accessibility) %>% + st_as_sf(coords = c("xcoord", "ycoord"), crs=25832) +all_pt <- all_pt[grid_buildings,] #cut to size + +all_drt <- select(acc_ameni, -c("time", "car_accessibility", "pt_accessibility")) %>% + left_join(select(acc_docto, c("id", "estimatedDrt_accessibility")), by = join_by(id), suffix = c(".ameni", ".docto")) %>% + left_join(.,select(acc_pharm, c("id", "estimatedDrt_accessibility")), by = join_by(id) ) %>% + rename(estimatedDrt_accessibility.pharm = estimatedDrt_accessibility) %>% + # left_join(.,select(acc_churc, c("id", "estimatedDrt_accessibility")), by = join_by(id)) %>% #CHURCH COMMENT OUT IF NOT NEEDED + #rename(estimatedDrt_accessibility.churc = estimatedDrt_accessibility) %>% #CHURCH COMMENT OUT IF NOT NEEDED + left_join(.,select(acc_sport, c("id", "estimatedDrt_accessibility")), by = join_by(id)) %>% + rename(estimatedDrt_accessibility.sport = estimatedDrt_accessibility) %>% + left_join(.,select(acc_super, c("id", "estimatedDrt_accessibility")), by = join_by(id)) %>% + rename(estimatedDrt_accessibility.super = estimatedDrt_accessibility)%>% + st_as_sf(coords = c("xcoord", "ycoord"), crs=25832) +all_drt <- all_drt[grid_buildings,] + +all_car <- select(acc_ameni, -c("time", "estimatedDrt_accessibility", "pt_accessibility")) %>% + left_join(select(acc_docto, c("id", "car_accessibility")), by = join_by(id), suffix = c(".ameni", ".docto")) %>% + left_join(.,select(acc_pharm, c("id", "car_accessibility")), by = join_by(id) ) %>% + rename(car_accessibility.pharm = car_accessibility) %>% + #left_join(.,select(acc_churc, c("id", "car_accessibility")), by = join_by(id)) %>% #CHURCH COMMENT OUT IF NOT NEEDED + #rename(car_accessibility.churc = car_accessibility) %>% #CHURCH COMMENT OUT IF NOT NEEDED + left_join(.,select(acc_sport, c("id", "car_accessibility")), by = join_by(id)) %>% + rename(car_accessibility.sport = car_accessibility) %>% + left_join(.,select(acc_super, c("id", "car_accessibility")), by = join_by(id)) %>% + rename(car_accessibility.super = car_accessibility)%>% + st_as_sf(coords = c("xcoord", "ycoord"), crs=25832) +all_car <- all_car[grid_buildings,] + +#create additional columns with mean/min/max +all_pt <- all_pt %>% + rowwise() %>% + mutate( + mean = mean(c_across(pt_accessibility.ameni:pt_accessibility.super)), + median = median(c_across(pt_accessibility.ameni:pt_accessibility.super)), + min = min(c_across(pt_accessibility.ameni:pt_accessibility.super)), + max = max(c_across(pt_accessibility.ameni:pt_accessibility.super))) + + +all_drt <- all_drt %>% + rowwise() %>% + mutate( + mean_DRT = mean(c_across(estimatedDrt_accessibility.ameni:estimatedDrt_accessibility.super)), + median_DRT = median(c_across(estimatedDrt_accessibility.ameni:estimatedDrt_accessibility.super)), + min_DRT = min(c_across(estimatedDrt_accessibility.ameni:estimatedDrt_accessibility.super)), + max_DRT = max(c_across(estimatedDrt_accessibility.ameni:estimatedDrt_accessibility.super))) + +all_car <- all_car %>% + rowwise() %>% + mutate( + mean = mean(c_across(car_accessibility.ameni:car_accessibility.super)), + median = median(c_across(car_accessibility.ameni:car_accessibility.super)), + min = min(c_across(car_accessibility.ameni:car_accessibility.super)), + max = max(c_across(car_accessibility.ameni:car_accessibility.super))) + +grid_buildings_meanmodes <- grid_buildings %>% + left_join(st_drop_geometry(select(all_drt, c("id", "mean_DRT", "median_DRT"))), by = "id") %>% + left_join(st_drop_geometry(select(all_pt, c("id", "mean", "median"))), by = "id") %>% + left_join(st_drop_geometry(select(all_car, c("id", "mean", "median"))), by = "id", suffix = c("_PT", "_car")) + +grid_buildings_meanmodes <- st_join(grid_buildings_meanmodes, select(study_area, name)) +grid_buildings_meanmodes <- grid_buildings_meanmodes %>% + pivot_longer(cols = c("mean_car", "mean_PT", "mean_DRT"), values_to = "mean", names_to = "mode") + +ggplot(grid_buildings_meanmodes)+ + geom_histogram(aes(x = mean, fill = mode), alpha = 0.7, position = "identity")+ + scale_fill_viridis_d(labels= c("car", "DRT", "PT"))+ + facet_wrap(vars(name))+ + xlim(-12,1)+ + xlab("mean accessibility (utils)") + +grid_buildings_medianmodes <- grid_buildings_meanmodes %>% + pivot_longer(cols = c("median_car", "median_PT", "median_DRT"), values_to = "median", names_to = "mode") + +ggplot(grid_buildings_medianmodes)+ + geom_histogram(aes(x = median, fill = mode), alpha = 0.7, position = "identity")+ + scale_fill_viridis_d(labels= c("car", "DRT", "PT"))+ + facet_wrap(vars(name))+ + xlim(-12,1)+ + xlab("median accessibility (utils)") + +#### East Kelheim - BUILDINGS#### +#OstKelheim wait =20 +setwd("~/Thesis/accessibilities/final/ek_model1_w20") +#load data +data_buildings <- read.csv("buildings_3vh1_w20_1200.csv") %>% + st_as_sf(coords= c("xcoord", "ycoord"), crs=25832) +data_buildings <- data_buildings[grid_buildings,] + +grid_buildings_meanmodes <- st_join(data_buildings, select(study_area, name)) %>% + drop_na(name) +grid_buildings_meanmodes <- grid_buildings_meanmodes %>% + pivot_longer(cols = c("car_accessibility", "pt_accessibility", "estimatedDrt_accessibility"), values_to = "mean", names_to = "mode") + +ggplot(grid_buildings_meanmodes)+ + geom_histogram(aes(x = mean, fill = mode), alpha = 0.7, position = "identity")+ + scale_fill_viridis_d(labels= c("car", "DRT", "PT"))+ + facet_wrap(vars(name))+ + xlim(-12,1)+ + xlab("mean accessibility (utils)") + +grid_buildings_medianmodes <- grid_buildings_meanmodes %>% + pivot_longer(cols = c("median_car", "median_PT", "median_DRT"), values_to = "median", names_to = "mode") + +ggplot(grid_buildings_medianmodes)+ + geom_histogram(aes(x = median, fill = mode), alpha = 0.7, position = "identity")+ + scale_fill_viridis_d(labels= c("car", "DRT", "PT"))+ + facet_wrap(vars(name))+ + xlim(-12,1)+ + xlab("median accessibility (utils)") diff --git a/src/main/R/thesisJBAnalysis/KEXI_analysis.R b/src/main/R/thesisJBAnalysis/KEXI_analysis.R new file mode 100644 index 0000000..165367a --- /dev/null +++ b/src/main/R/thesisJBAnalysis/KEXI_analysis.R @@ -0,0 +1,561 @@ +library(tidyverse) +library(sf) +library(clock) +library(lubridate) +library(scales) +library(xtable) +library(ggpubr) + +#### FILE PATHS #### + +data_path <- "rides_clean.csv" + +#data_dist is created in r5r.R +data_dist_path <- "rides_dist.csv" + +raster_path <- "../shapefiles/grid_4326.shp" + +#### READ DATA #### + +data <- read.csv(data_path)%>% + mutate(av_dr = ifelse(is.na(Grund.für.die.Fahrt),"DR",Grund.für.die.Fahrt)) +data_dist <- read.csv(data_dist_path) + +raster <- st_read(raster_path) + +####GENERAL NUMBERS#### + +n_distinct(data$Fahrgast.ID) + +#trips per user +user_dist <- data %>% + filter(Buchungsmethode != "Agent") %>% + group_by(Fahrgast.ID) %>% + summarise(count = n()) %>% + group_by(count) %>% + summarise(count_count = n()) %>% + mutate(abs = count*count_count) + +ggplot(user_dist)+ + geom_histogram(aes(x=count, y = abs), stat = "identity") + +#number of trips with multiple travelers +data %>% + filter(Anzahl.der.Fahrgäste >1) %>% + nrow() + +#number of trips by Buchungsart +data %>% + group_by(Buchungsart) %>% + summarise(count = n()) + +#number of trips by Planungspräferenz +data %>% + group_by(Planungspräferenz) %>% + summarise(count = n()) + +#number of trips by Buchungsart and Planungspräferenz +data %>% + group_by(Buchungsart, Planungspräferenz) %>% + summarise(count = n()) + +#number of trips by Buchungsmethode +data %>% + group_by(Buchungsmethode) %>% + summarise(count = n()) + +#number of trips by Buchungsart and Buchungsmethode +table <-data %>% + group_by(Buchungsmethode, Buchungsart) %>% + summarise(count = n()) %>% + pivot_wider(names_from = Buchungsart, values_from = count) + +xtable(table) + +#### COUNT ORIGIN/DESTINATION in GRID #### +#create sf from data +origins <- st_as_sf(data, coords = c("Start.Längengrad", "Start.Breitengrad"), crs = 4326 ) +destinations <- st_as_sf(data, coords = c("Zielort.Längengrad", "Zielort.Breitengrad"), crs = 4326 ) + + +#join origins to raster and remove raster cells that don't contain any rides +raster_origins <- raster %>% + mutate(counts = lengths(st_intersects(., origins))) +raster_origins <- subset(raster_origins, raster_origins$counts !=0) + +#join destinations to raster and remove raster cells that don't contain any rides +raster_dest <- raster %>% + mutate(counts = lengths(st_intersects(., destinations)),) +raster_dest <- subset(raster_dest, raster_dest$counts !=0) + +#join both raster sfs to one +all_raster <- full_join(raster_origins, st_drop_geometry(raster_dest), by = "id") %>% + mutate(ratio = (counts.x-counts.y)) + +st_write(all_raster, "../Shapefiles/raster_origins_destinations.shp", append = FALSE) + +#### DEMAND BY MONTH #### + +#COUNT FOR EACH MONTH +count_month <- data %>% + mutate(month = format(as.Date(Erstellungsdatum.der.Fahrtanfrage), "%Y-%m")) %>% + group_by(month) %>% + summarise(count = n()) + +ggplot(count_month)+ + geom_bar(aes(x = month, y = count), stat = "identity", fill = c("#2a788e"))+ + xlab("month")+ + theme(axis.text.x = element_text(angle = 45, vjust = 0.5)) + +#COUNT FOR EACH MONTH AV/DR +count_month_2 <- data %>% + mutate(month = format(as.Date(Erstellungsdatum.der.Fahrtanfrage), "%Y-%m")) %>% + group_by(month, av_dr) %>% + summarise(count = n()) + +ggplot(count_month_2)+ + geom_bar(aes(x = month, y = count, fill = av_dr), stat = "identity")+ + xlab("month")+ + theme(axis.text.x = element_text(angle = 45, vjust = 0.5))+ + labs(fill = "vehicle type") + +#COUNT USERS PER MONTH +count_users <- data %>% + mutate(month = format(as.Date(Erstellungsdatum.der.Fahrtanfrage), "%Y-%m")) %>% + group_by(month) %>% + summarise(users = n_distinct(Fahrgast.ID)) + +ggplot(count_users)+ + geom_bar(aes(x = month, y = users), stat = "identity") + +#COUNT UNIQUE USERS PER MONTH +count_users_2 <- data %>% + mutate(month = format(as.Date(Erstellungsdatum.der.Fahrtanfrage), "%Y-%m")) %>% + mutate(unique_user = !duplicated(Fahrgast.ID)) %>% + group_by(month, unique_user) %>% + summarise(users = n_distinct(Fahrgast.ID)) + +ggplot(count_users_2)+ + geom_bar(aes(x = month, y = users, fill= unique_user), stat = "identity", position = "fill") + +df %>% + mutate(unique_customers = !duplicated(customer_id)) %>% + group_by(month, year) %>% + summarise(unique_customers = sum(unique_customers)) + +#AVERAGE DISTANCE TRAVELED PER MONTH + +data_dist <- read.csv("rides_dist.csv") #direct distance calculated with r5r + +avg_distance_month <- data_dist %>% + mutate(month = format(as.Date(Erstellungsdatum.der.Fahrtanfrage), "%Y-%m")) %>% + drop_na(distance) %>% + group_by(month) %>% + summarise(mean = mean(as.numeric(distance)), + median = median(as.numeric(distance))) %>% + pivot_longer(cols = c("mean", "median"), + names_to = "type", + values_to = "distance") + +ggplot(avg_distance_month)+ + geom_bar(aes(x = month, y = distance, fill = type), + stat = "identity", + position = "dodge") + +#AVERAGE TRIP DURATION PER MONTH +avg_duration_month <- data %>% + mutate(month = format(as.Date(Erstellungsdatum.der.Fahrtanfrage), "%Y-%m")) %>% + drop_na(Fahrtdauer) %>% + group_by(month) %>% + summarise(mean = mean(as.numeric(Fahrtdauer)), + median = median(as.numeric(Fahrtdauer))) %>% + pivot_longer(cols = c("mean", "median"), + names_to = "type", + values_to = "duration") + +ggplot(avg_duration_month)+ + geom_bar(aes(x = month, y = duration, fill = type), + stat = "identity", + position = "dodge") + + +#COUNT BOOKING TYPE PER MONTH +buchungsart_month <- data %>% + mutate(month = format(as.Date(Erstellungsdatum.der.Fahrtanfrage), "%Y-%m")) %>% + group_by(month, Buchungsart) %>% + summarise(count = n()) + +ggplot(buchungsart_month)+ + geom_bar(aes(x = month, y = count, fill = Buchungsart), + stat = "identity", + position = "fill")+ + xlab("month")+ + theme(axis.text.x = element_text(angle = 45, vjust = 0.5))+ + labs(fill = "booking type") + +#COUNT BOOKING METHOD PER MONTH +buchungsmethode_month <- data %>% + mutate(month = format(as.Date(Erstellungsdatum.der.Fahrtanfrage), "%Y-%m")) %>% + group_by(month, Buchungsmethode) %>% + summarise(count = n()) + +ggplot(buchungsmethode_month)+ + geom_bar(aes(x = month, y = count, fill = Buchungsmethode), + stat = "identity", + position = "fill")+ + xlab("month")+ + theme(axis.text.x = element_text(angle = 45, vjust = 0.5))+ + labs(fill = "booking method") + +####DEMAND BY HOUR OF DAY#### +#COUNT TRIPS BY HOUR OF DAY +count_hour <-data %>% + drop_na(Angefragte.Einstiegszeit) %>% + mutate(hour = format(as_datetime(Angefragte.Einstiegszeit), "%H")) %>% + group_by(hour) %>% + summarise(count = n()) + +ggplot(count_hour)+ + geom_bar(aes(x=hour, y = count), stat = "identity", fill = c("#2a788e")) + +#AVG DISTANCE BY HOUR OF DAY +avg_distance_hour <- data %>% + drop_na(Fahrtdistanz) %>% + drop_na(Angefragte.Einstiegszeit) %>% + mutate(hour = format(as_datetime(Angefragte.Einstiegszeit), "%H")) %>% + group_by(hour) %>% + summarise(mean = mean(as.numeric(Fahrtdistanz)), + median = median(as.numeric(Fahrtdistanz))) %>% + pivot_longer(cols = c("mean", "median"), + names_to = "type", + values_to = "distance") + +ggplot(avg_distance_hour)+ + geom_bar(aes(x = hour, y = distance, fill = type), + stat = "identity", + position = "dodge") + +####DEMAND BY WEEKDAY#### +count_weekday <- data %>% + drop_na(Angefragte.Einstiegszeit) %>% + mutate(weekday= wday(as.Date(Angefragte.Einstiegszeit), label = TRUE, locale="en_UK")) %>% + filter(weekday != "Sun") %>% + group_by(weekday) %>% + summarise(count = n()) + +ggplot(count_weekday)+ + geom_bar(aes(x=weekday, y= count), stat = "identity", fill = c("#7ad151")) + +count_weekday_hour <- data %>% + drop_na(Angefragte.Einstiegszeit) %>% + mutate(weekday= wday(as.Date(Angefragte.Einstiegszeit), label = TRUE)) %>% + filter(weekday != "So") %>% + mutate(hour = format(as_datetime(Angefragte.Einstiegszeit), "%H")) %>% + group_by(weekday, hour) %>% + summarise(count = n()) + +ggplot(count_weekday_hour, aes(x=hour, y = count, fill = weekday))+ + geom_bar( stat = "identity")+ + facet_wrap(~weekday) + +#### PREPARATION TIME #### + +#REQUESTED DEPARTURE/ARRIVAL TIME + +data_ein_aus <- data %>% + mutate(ein_aus = if_else(is.na(Angefragte.Einstiegszeit), "aus", "ein")) %>% + mutate(monat = format(as.Date(Erstellungsdatum.der.Fahrtanfrage), "%Y-%m")) %>% + group_by(monat, ein_aus) %>% + summarise(count = n()) + +#pre-booking: REQUEST TIME vs. REQUESTED DEPARTURE TIME +avg_prep <- data %>% + select(-Series.ID, -Session.ID, -Anzahl.der.Fahrgäste,-Wiederholungsart, -Fahrer.ID, -Fahrzeug.ID, + -Letzte.Schicht.ID, -Service, -Geteilte.Fahrt, -Geteilte.Fahrtdauer..Min..,-No.Show.Grund, -No.Show.Zeit, + -Direkte.Fahrtdauer..Min.., -Fahrtbewertung..1.5., -Fahrtfeedback, -Feedback.Labels, -Unternehmens.ID, -Unternehmensname, + -Reason.For.Manual.Status.Update) %>% + drop_na(Angefragte.Einstiegszeit) %>% + mutate(prep = (as.numeric(as_datetime(Angefragte.Einstiegszeit)-as_datetime(Erstellungszeit.der.Fahrtanfrage)))/60) %>% + mutate(monat = format(as.Date(Erstellungsdatum.der.Fahrtanfrage), "%Y-%m")) + +prep_values <- avg_prep %>% + group_by(Buchungsart) %>% + summarise(mean = mean(prep)) + +#development over time (month) +avg_prep %>% + group_by(monat) %>% + summarise(prep = mean(prep), count = n()) %>% + ggplot()+ + geom_histogram(aes(y = prep, x = monat), + bins = 48, stat = "identity", position = "dodge") + +#development short preparation times +avg_prep %>% + filter(prep <5) %>% + group_by(monat) %>% + summarise(prep = mean(prep), count = n()) %>% + ggplot()+ + geom_histogram(aes(y = prep, x = monat, fill = count), + bins = 48, stat = "identity", position = "dodge") + +#development long preparation times +avg_prep %>% + filter(prep >30) %>% + group_by(monat) %>% + summarise(prep = mean(prep), count = n()) %>% + ggplot()+ + geom_histogram(aes(y = prep, x = monat, fill = count), + bins = 48, stat = "identity", position = "dodge") + +#development over time, divded in bins +prep_bins_month <- avg_prep %>% + mutate(prep_bin = case_when( + prep >= -1 & prep < 5 ~ "0-5 mins", + prep >= 5 & prep < 30 ~ "5-30 mins", + prep >= 30 & prep < 60 ~ "30-60 mins", + prep >= 60 & prep < 360 ~ "1-6 hs", + prep >= 360 & prep < 1440 ~ "6-24 hs", + prep >= 1440 & prep < 4320 ~ "1-3 days", + prep >= 4320 & prep <= Inf ~ "more than 3 days", + TRUE ~ NA_character_ + )) %>% + group_by(monat,prep_bin) %>% + summarise(count = n()) + +levels <- c("0-5 mins", + "5-30 mins", + "30-60 mins", + "1-6 hs","6-24 hs", "1-3 days", "more than 3 days") +ggplot(prep_bins_month) + + geom_bar(aes(x=monat, y= count, fill = forcats::fct_rev(factor(prep_bin, levels = levels))), stat = "identity", position = "stack")+ + scale_fill_viridis_d(end = 0.9)+ + xlab("month")+ + theme(axis.text.x = element_text(angle = 45, vjust = 0.5))+ + labs(fill = "Preparation Time") + + +#### WAIT TIME #### +avg_wait <- data %>% + select(-Series.ID, -Session.ID, -Anzahl.der.Fahrgäste,-Wiederholungsart, -Fahrer.ID, -Fahrzeug.ID, + -Letzte.Schicht.ID, -Service, -Geteilte.Fahrt, -Geteilte.Fahrtdauer..Min..,-No.Show.Grund, -No.Show.Zeit, + -Direkte.Fahrtdauer..Min.., -Fahrtbewertung..1.5., -Fahrtfeedback, -Feedback.Labels, -Unternehmens.ID, -Unternehmensname, + -Reason.For.Manual.Status.Update, + -Startzone, -Zielzone, -Fahrtanfragezone, -Anbietername, -Stornierungsgrund, -Stornierungsquelle) %>% + mutate(prep = (as.numeric(as_datetime(Angefragte.Einstiegszeit)-as_datetime(Erstellungszeit.der.Fahrtanfrage)))/60) %>% + mutate(monat = format(as.Date(Erstellungsdatum.der.Fahrtanfrage), "%Y-%m"))%>% + filter(is.na(Stornierungszeit)) %>% + mutate(wait_1 = (as.numeric(as_datetime(Ursprünglich.geplante.Einstiegszeit)-as_datetime(Angefragte.Einstiegszeit)))/60) %>% + mutate(wait_1_5 = as.numeric(as_datetime(Zuletzt.geplante.Einstiegszeit)-as_datetime(Ursprünglich.geplante.Einstiegszeit))/60) %>% + mutate(wait_2 = as.numeric(as_datetime(Fahrzeug.ist.da.SMS.Versandzeit)-as_datetime(Ursprünglich.geplante.Einstiegszeit))/60) %>% + mutate(wait_2_5 = (as.numeric(as_datetime(Fahrzeug.ist.da.SMS.Versandzeit)-as_datetime(ifelse(is.na(Zuletzt.geplante.Einstiegszeit), Ursprünglich.geplante.Einstiegszeit,Zuletzt.geplante.Einstiegszeit))))/60) %>% + mutate(wait_3 = (as.numeric(as_datetime(Tatsächliche.Einstiegszeit)-(as_datetime(Fahrzeug.ist.da.SMS.Versandzeit))))/60) %>% + mutate(wait_4 = (as.numeric(as_datetime(Tatsächliche.Einstiegszeit)-(as_datetime(Ursprünglich.geplante.Einstiegszeit))))/60) + + +#relationship preparation time and difference requested/planned departure time +avg_wait %>% + drop_na(prep, wait_1) %>% + ggplot()+ + geom_point(aes(x = prep, y = wait_1)) + +wait_1_dist <- avg_wait %>% + drop_na(wait_1) %>% + mutate(wait_1_bins = case_when( + wait_1 >= -60 & wait_1 < -10 ~ "-60--10", + wait_1 >= -10 & wait_1 < -5 ~ "-10--5", + wait_1 >= -5 & wait_1 < -2 ~ "-5--2", + wait_1 >= -2 & wait_1 < 2 ~ "-2-2", + wait_1 >= 2 & wait_1 < 5 ~ "2-5", + wait_1 >= 5 & wait_1 < 10 ~ "5-10", + wait_1 >= 10 & wait_1 < 15 ~ "10-15", + wait_1 >= 15 & wait_1 < 30 ~ "15-30", + wait_1 >= 30 & wait_1 <= 60 ~ "30-60", + TRUE ~ NA_character_ + )) %>% + group_by(wait_1_bins) %>% + summarise(count = n()) + +#Ursprüngliche vs. letzte Einstiegszeit + +wait_1_5 <- avg_wait %>% + mutate(Änderung = ifelse(is.na(Änderung), 0, Änderung)) %>% + mutate(Änderung_bins = case_when( + Änderung >= -60 & Änderung < -10 ~ "-60--10", + Änderung >= -10 & Änderung < -5 ~ "-10--5", + Änderung >= -5 & Änderung < -2 ~ "-5--2", + Änderung >= -2 & Änderung < 2 ~ "-2-2", + Änderung >= 2 & Änderung < 5 ~ "2-5", + Änderung >= 5 & Änderung < 10 ~ "5-10", + Änderung >= 10 & Änderung < 15 ~ "10-15", + Änderung >= 15 & Änderung < 30 ~ "15-30", + Änderung >= 30 & Änderung <= 60 ~ "30-60", + TRUE ~ NA_character_ + )) %>% + group_by(Änderung_bins) %>% + summarise(count = n()) + + +#distribution planned departure time/SMS time +wait_2_dist <-avg_wait %>% + drop_na(wait_2) %>% + mutate(Original = case_when( + wait_2 >= -60 & wait_2 < -10 ~ "-60--10", + wait_2 >= -10 & wait_2 < -5 ~ "-10--5", + wait_2 >= -5 & wait_2 < -2 ~ "-5--2", + wait_2 >= -2 & wait_2 < 2 ~ "-2-2", + wait_2 >= 2 & wait_2 < 5 ~ "2-5", + wait_2 >= 5 & wait_2 < 10 ~ "5-10", + wait_2 >= 10 & wait_2 < 15 ~ "10-15", + wait_2 >= 15 & wait_2 < 30 ~ "15-30", + wait_2 >= 30 & wait_2 <= 60 ~ "30-60", + TRUE ~ NA_character_ + )) %>% + mutate(Last = case_when( + wait_2_5 >= -60 & wait_2_5 < -10 ~ "-60--10", + wait_2_5 >= -10 & wait_2_5 < -5 ~ "-10--5", + wait_2_5 >= -5 & wait_2_5 < -2 ~ "-5--2", + wait_2_5 >= -2 & wait_2_5 < 2 ~ "-2-2", + wait_2_5 >= 2 & wait_2_5 < 5 ~ "2-5", + wait_2_5 >= 5 & wait_2_5 < 10 ~ "5-10", + wait_2_5 >= 10 & wait_2_5 < 15 ~ "10-15", + wait_2_5 >= 15 & wait_2_5 < 30 ~ "15-30", + wait_2_5 >= 30 & wait_2_5 <= 60 ~ "30-60", + TRUE ~ NA_character_ + )) %>% + pivot_longer(cols = c("Last", "Original"), + names_to = "wait_type", + values_to = "bins") %>% + group_by(wait_type, bins) %>% + summarise(count = n()) + + +#distribution sms/actual departure +wait_3_dist <-avg_wait %>% + drop_na(wait_3) %>% + mutate(wait_3_bins = case_when( + wait_3 >= 0 & wait_3 < 2 ~ "0-2", + wait_3 >= 2 & wait_3 < 5 ~ "2-5", + wait_3 >= 5 & wait_3 < 10 ~ "5-10", + wait_3 >= 10 & wait_3 < 15 ~ "10-15", + wait_3 >= 15 & wait_3 < 30 ~ "15-30", + wait_3 >= 30 & wait_3 <= 60 ~ "30-60", + TRUE ~ NA_character_ + )) %>% + group_by(wait_3_bins) %>% + summarise(count = n()) + + +#distribution planned departure/actual departure +wait_4_dist <-avg_wait %>% + drop_na(wait_4) %>% + mutate(wait_4_bins = case_when( + wait_4 >= -60 & wait_4 < -10 ~ "-60--10", + wait_4 >= -10 & wait_4 < -5 ~ "-10--5", + wait_4 >= -5 & wait_4 < -2 ~ "-5--2", + wait_4 >= -2 & wait_4 < 2 ~ "-2-2", + wait_4 >= 2 & wait_4 < 5 ~ "2-5", + wait_4 >= 5 & wait_4 < 10 ~ "5-10", + wait_4 >= 10 & wait_4 < 15 ~ "10-15", + wait_4 >= 15 & wait_4 < 30 ~ "15-30", + wait_4 >= 30 & wait_4 <= 60 ~ "30-60", + TRUE ~ NA_character_ + )) %>% + group_by(wait_4_bins) %>% + summarise(count = n()) + + +####PLOTS#### + +ylimits <- c(0, max(c(wait_1_dist$count, wait_2_dist$count, wait_1_5$count))) + +plot_wait_1 <-ggplot(wait_1_dist)+ + geom_bar(aes(x=factor(wait_1_bins, + levels = c("-60--10", "-10--5", "-5--2", + "-2-2","2-5","5-10", + "10-15","15-30","30-60",NA)), y = count),stat = "identity", fill = c("#35b779"))+ + xlab("Original Planned - Requested Departure Time (mins)")+ + ylim(ylimits) + +plot_wait_1_5 <- ggplot(wait_1_5)+ + geom_bar(aes(x=factor(Änderung_bins, + levels = c("-60--10", "-10--5", "-5--2", + "-2-2","2-5","5-10", + "10-15","15-30","30-60",NA)), y = count),stat = "identity", fill = c("#3e4989"))+ + xlab("Last Planned - Original Planned Departure Time (mins)")+ + ylim(ylimits) + +plot_wait_2 <-ggplot(wait_2_dist)+ + geom_bar(aes(x=factor(bins, + levels = c("-60--10", "-10--5", "-5--2", + "-2-2","2-5","5-10", + "10-15","15-30","30-60",NA)), y = count, fill = wait_type),stat = "identity", position = "dodge")+ + scale_fill_manual(values = c("#b5de2b","#26828e"))+ + xlab("Original/Last Planned Departure - Vehicle Arrival Time (mins)")+ + ylim(ylimits)+ + theme(legend.position=c(.75,.75), + legend.background = element_rect(fill = "transparent"), + legend.title = element_blank()) + +plot_wait_3<-ggplot(wait_3_dist)+ + geom_bar(aes(x=factor(wait_3_bins, + levels = c("0-2","2-5","5-10", + "10-15","15-30","30-60",NA)), y = count),stat = "identity", fill = c("#440154"))+ + xlab("Vehicle Arrival - Actual Boarding Time (mins)") + + +plot_wait_4 <-ggplot(wait_4_dist)+ + geom_bar(aes(x=factor(wait_4_bins, + levels = c("-60--10", "-10--5", "-5--2", + "-2-2","2-5","5-10", + "10-15","15-30","30-60",NA)), y = count),stat = "identity", fill = c("#31688e"))+ + xlab("Planned Departure - Actual Boarding Time (mins)")+ + ylim(ylimits) + +ggarrange(plot_wait_1, plot_wait_1_5,plot_wait_2, plot_wait_3, + ncol=2, nrow=2) + + +#wait time for accessibility calculations + +avg_wait <- avg_wait %>% + mutate(Einstiegszeit_kommuniziert = ifelse(is.na(Zuletzt.geplante.Einstiegszeit), Ursprünglich.geplante.Einstiegszeit, Zuletzt.geplante.Einstiegszeit)) %>% + mutate(end_wait = ifelse(is.na(Fahrzeug.ist.da.SMS.Versandzeit),Tatsächliche.Einstiegszeit, + ifelse(Fahrzeug.ist.da.SMS.Versandzeit < Einstiegszeit_kommuniziert, Einstiegszeit_kommuniziert, Fahrzeug.ist.da.SMS.Versandzeit))) %>% + mutate(op_1 = (as.numeric(as_datetime(end_wait)-as_datetime(Angefragte.Einstiegszeit)))) %>% + mutate(op_2 = as.numeric(as_datetime(end_wait)-as_datetime(Ursprünglich.geplante.Einstiegszeit))) %>% + mutate(op_3 = as.numeric(as_datetime(end_wait)-as_datetime(ifelse(is.na(Zuletzt.geplante.Einstiegszeit),Ursprünglich.geplante.Einstiegszeit, Zuletzt.geplante.Einstiegszeit)))) + +test <- avg_wait %>% + filter(prep<=5) %>% + drop_na(op_1) +avg_op_1 = mean(test$op_1) +test <- avg_wait %>% + drop_na(op_2) +avg_op_2 = mean(test$op_2) +test <- avg_wait %>% + drop_na(op_3) %>% + filter(op_3>=-600) +avg_op_3 = mean(test$op_3) + +#WAIT TIME THROUGH THE DAY +avg_wait_hour <- avg_wait %>% + filter(prep <=5) %>% + mutate(hour = format(as_datetime(Ursprünglich.geplante.Einstiegszeit), "%H")) %>% + mutate(on_demand_wait = (as.numeric(as_datetime(Tatsächliche.Einstiegszeit)-as_datetime(Angefragte.Einstiegszeit)))) %>% + group_by(hour) %>% + summarise(mean = mean(on_demand_wait)) + +ggplot(avg_wait_hour)+ + geom_bar(aes(x=hour, y = mean), stat = "identity", fill = c("#2a788e")) + +#WAIT TIME OVER TIME +avg_wait_month <- avg_wait %>% + drop_na(wait_4) %>% + mutate(month = format(as.Date(Erstellungsdatum.der.Fahrtanfrage), "%Y-%m")) %>% + group_by(month) %>% + summarise(mean = mean(wait_4)) + +ggplot(avg_wait_month)+ + geom_bar(aes(x=month, y = mean), stat = "identity", fill = c("#2a788e")) + diff --git a/src/main/R/thesisJBAnalysis/data_prep.R b/src/main/R/thesisJBAnalysis/data_prep.R new file mode 100644 index 0000000..a4edd7b --- /dev/null +++ b/src/main/R/thesisJBAnalysis/data_prep.R @@ -0,0 +1,183 @@ +library(tidyverse) +library(lubridate) +library(xtable) + +#TESTING IDS +testingCustomerIds_extended <- as.character(c(1, + 43, + 649, + 673, + 3432, + 3847, + 3887, + 4589, + 7409, + 7477, + 9808, + 9809, + 8320, + 12777, + 13288, + 13497, + 13498 )) + + +data_raw <- read.csv2("C:/Users/J/Documents/Thesis/thesis/fahrtanfragen.csv", na.strings = "")%>% + filter(!Fahrgast.ID %in% testingCustomerIds_extended) + + +####CLEAN, FILTER & SAVE#### +# delete rows that don't have a boarding time, place or are not marked as completed +data_completed <- data_raw %>% + drop_na(Tatsächliche.Einstiegszeit, + Längengrad..Einstieg.) %>% + filter(Status.der.Fahrtanfrage == "Completed") + +#dataset of AV rides +data_av <- data_completed %>% + filter(Grund.für.die.Fahrt == "AV") + +#dataset of conventional rides +data_dr <- data_completed %>% + filter(Grund.für.die.Fahrt == "DR" | is.na(Grund.für.die.Fahrt)) +#dataset including both AV and conventional, but filtered for time and duration +data_plausible <- data_completed %>% + filter(as.Date(Erstellungsdatum.der.Fahrtanfrage) <"2024-01-01") %>% + filter(as.numeric(Fahrtdauer) > 1 & as.numeric(Fahrtdauer) < 30) + +write.csv(data_plausible, "rides_clean.csv", row.names = FALSE) +write.csv(data_dr, "rides_dr.csv", row.names = FALSE) + +####ANALYSIS RAW DATA#### +#seat unavailable +#overall +unavailable <- data_raw %>% + filter(as.Date(Erstellungsdatum.der.Fahrtanfrage) <"2024-01-01", + as.Date(Erstellungsdatum.der.Fahrtanfrage) >"2021-06-01") %>% + mutate(monat = format(as.Date(Erstellungsdatum.der.Fahrtanfrage), "%Y-%m")) %>% + group_by(monat, Status.der.Fahrtanfrage) %>% + summarise(count = n()) +#on-demand +unavailable <- data_raw %>% + filter(as.Date(Erstellungsdatum.der.Fahrtanfrage) <"2024-01-01", + as.Date(Erstellungsdatum.der.Fahrtanfrage) >"2021-06-01") %>% + filter(Buchungsart =="On Demand") %>% + mutate(monat = format(as.Date(Erstellungsdatum.der.Fahrtanfrage), "%Y-%m")) %>% + group_by(monat, Status.der.Fahrtanfrage) %>% + summarise(count = n()) +#plot +ggplot(unavailable)+ + geom_bar(aes(x = monat, y = count, fill = Status.der.Fahrtanfrage), + stat = "identity", + position = "fill")+ + xlab("month")+ + theme(axis.text.x = element_text(angle = 45, vjust = 0.5))+ + labs(fill = "booking status") + +#Status der Fahrtanfrage + generate table +table <- data_raw %>% + group_by(Status.der.Fahrtanfrage) %>% + count() %>% + mutate(percent = round((n/sum(n))*100,2)) +table <-t(table) +xtable(table) + +#number of users +n_distinct(data_raw$Fahrgast.ID) + +#buchungsart +data_raw %>% + filter(Status.der.Fahrtanfrage=="Completed" | Status.der.Fahrtanfrage=="Unaccepted Proposal") %>% + group_by(Buchungsart, Status.der.Fahrtanfrage) %>% + summarise(count = n()) + +### NACHFRAGE MONATE ### + +#COUNT TRIPS PER MONTH: completed, unaccepted, seat unavailable +count_month <- data_raw %>% + filter(as.Date(Erstellungsdatum.der.Fahrtanfrage) <"2024-01-01", + as.Date(Erstellungsdatum.der.Fahrtanfrage) >"2021-07-01") %>% + filter(Status.der.Fahrtanfrage=="Completed" | Status.der.Fahrtanfrage=="Unaccepted Proposal" |Status.der.Fahrtanfrage=="Seat Unavailable") %>% + mutate(monat = format(as.Date(Erstellungsdatum.der.Fahrtanfrage), "%Y-%m")) %>% + group_by(monat, Status.der.Fahrtanfrage) %>% + summarise(count = n()) + +ggplot(count_month)+ + geom_bar(aes(x = monat, y = count, fill =factor(Status.der.Fahrtanfrage, levels = c("Unaccepted Proposal", "Seat Unavailable", "Completed"))), stat = "identity")+ + theme(axis.text.x = element_text(angle = 45, vjust = 0.5))+ + scale_fill_viridis_d(end = 0.9)+ + labs(fill = "booking status")+ + xlab("month") + +#COUNT REQUESTS AND USERS +count_month <- data_raw %>% + filter(as.Date(Erstellungsdatum.der.Fahrtanfrage) <"2024-01-01", + as.Date(Erstellungsdatum.der.Fahrtanfrage) >"2021-06-01") %>% + mutate(monat = format(as.Date(Erstellungsdatum.der.Fahrtanfrage), "%Y-%m")) %>% + group_by(monat) %>% + summarise(count = n(), ids = length(unique(Fahrgast.ID))) + +ggplot(count_month)+ + geom_bar(aes(x = monat, y = count, fill = ids), stat = "identity")+ + theme(axis.text.x = element_text(angle = 45, vjust = 0.5)) + +###NACHFRAGE WOCHE### +#COUNT TRIPS PER WEEKDAY: completed, unaccepted, seat unavailable +count_weekday <- data_raw %>% + filter(Status.der.Fahrtanfrage=="Completed" | Status.der.Fahrtanfrage=="Unaccepted Proposal" |Status.der.Fahrtanfrage=="Seat Unavailable") %>% + drop_na(Angefragte.Einstiegszeit) %>% + mutate(weekday= wday(as.Date(Angefragte.Einstiegszeit), label = TRUE, locale="en_UK")) %>% + filter(weekday != "Sun") %>% + group_by(weekday, Status.der.Fahrtanfrage) %>% + summarise(count = n()) + +plot_count_wday <- ggplot(count_weekday)+ + geom_bar(aes(x = weekday, y = count, fill =factor(Status.der.Fahrtanfrage, levels = c("Unaccepted Proposal", "Seat Unavailable", "Completed"))), stat = "identity")+ + theme(axis.text.x = element_text(angle = 45, vjust = 0.5))+ + scale_fill_viridis_d(end = 0.9)+ + labs(fill = "booking status")+ + xlab("day of the week") + +###NACHFRAGE TAG### +#COUNT TRIPS PER TIME OF DAY: completed, unaccepted, seat unavailable +count_hour <-data_raw %>% + filter(Status.der.Fahrtanfrage=="Completed" | Status.der.Fahrtanfrage=="Unaccepted Proposal" |Status.der.Fahrtanfrage=="Seat Unavailable") %>% + drop_na(Angefragte.Einstiegszeit) %>% + mutate(hour = format(as_datetime(Angefragte.Einstiegszeit), "%H")) %>% + filter(hour !="00", + hour !="04", + hour !="05") %>% + group_by(hour, Status.der.Fahrtanfrage) %>% + summarise(count = n()) + +plot_count_hour <- ggplot(count_hour)+ + geom_bar(aes(x = hour, y = count, fill =factor(Status.der.Fahrtanfrage, levels = c("Unaccepted Proposal", "Seat Unavailable", "Completed"))), stat = "identity")+ + theme(axis.text.x = element_text(angle = 45, vjust = 0.5))+ + scale_fill_viridis_d(end = 0.9)+ + labs(fill = "booking status")+ + xlab("hour") + + +ggarrange(plot_count_hour, plot_count_wday, ncol=2, common.legend = TRUE, legend="bottom") + +###WAITTIME ### + +#calculate wait time (requested vs planned departure time) and compare completed vs. unaccepted rides + +avg_wait <- data_raw %>% + filter(Status.der.Fahrtanfrage=="Completed" | Status.der.Fahrtanfrage=="Unaccepted Proposal") %>% + mutate(monat = format(as.Date(Erstellungsdatum.der.Fahrtanfrage), "%Y-%m")) %>% + drop_na(Zeit.von.Fahrtanfrage.bis.zum.geplanten.Einstieg..nur.On.Demand.) %>% + mutate(prep = (as.numeric(as_datetime(Angefragte.Einstiegszeit)-as_datetime(Erstellungszeit.der.Fahrtanfrage)))/60) + +avg_wait_status <- avg_wait %>% + filter(prep >0) %>% + group_by(monat, Status.der.Fahrtanfrage) %>% + summarise(mean_wait = mean(as.numeric(Zeit.von.Fahrtanfrage.bis.zum.geplanten.Einstieg..nur.On.Demand.)), + mean_prep = mean(prep), + count = n()) + +ggplot(avg_wait_status) + + geom_bar(aes(x = monat, y = mean_wait, fill = Status.der.Fahrtanfrage), stat = "identity", position = "dodge") + + diff --git a/src/main/R/thesisJBAnalysis/r5r.R b/src/main/R/thesisJBAnalysis/r5r.R new file mode 100644 index 0000000..9e871a2 --- /dev/null +++ b/src/main/R/thesisJBAnalysis/r5r.R @@ -0,0 +1,93 @@ +library(osmextract) +library(sf) +library(r5r) +library(tidyverse) + + +#PATHS +rides_path <- "rides_clean.csv" +network_path <- "C:/Users/J/Documents/Thesis/thesis/osm/" #this needs to be a .pbf file, download from OSM using oe_get() + +#load and filter rides +rides <- read.csv(rides_path) %>% + drop_na(Angefragte.Einstiegszeit) + +# allocate RAM memory to Java +options(java.parameters = "-Xmx3G") +rJava::.jinit() + +#setup r5r +r5r_core <- setup_r5(data_path = network_path, verbose = TRUE) + + +##### NETWORK DISTANCE KEXI TRIPS #### + +rides <- rides %>% + rename(id = Fahrtanfragen.ID) + +origins <- rides %>% + dplyr::select(id, Breitengrad..Einstieg., Längengrad..Einstieg., Fahrtdauer) %>% + st_as_sf(coords = c("Längengrad..Einstieg.","Breitengrad..Einstieg." ), crs = 4326 ) %>% + st_cast("POINT") + +destinations <- rides %>% + dplyr::select(id, Breitengrad..Ausstieg., Längengrad..Ausstieg., Fahrtdauer) %>% + st_as_sf(coords = c("Längengrad..Ausstieg.", "Breitengrad..Ausstieg."), crs = 4326 )%>% + st_cast("POINT") + + +mode <- c("CAR") +max_walk_time <- 600 # minutes +max_trip_duration <- 600 # minutes +departure_datetime <- as.POSIXct("13-03-2024 12:00:00", + format = "%d-%m-%Y %H:%M:%S", tz= "GMT") + + +det <- detailed_itineraries(r5r_core = r5r_core, + origins = origins, + destinations = destinations, + mode = mode, + departure_datetime = departure_datetime, + max_walk_time = max_walk_time, + shortest_path = TRUE) + det <- det %>% + dplyr::select(from_id, distance, total_duration) %>% + mutate(from_id = as.integer(from_id)) + +rides_dist <- left_join(rides, det, join_by("id" == "from_id")) %>% + filter(Anbietername != "AV") %>% + mutate(Fahrtdauer_s = Fahrtdauer*60) +write.csv(rides_dist, "rides_dist.csv") + +#share of trips < 1,7km +(rides_dist %>% filter(distance<1700) %>% nrow())/nrow(rides_dist) + +#share of trips < 3,9km +(rides_dist %>% filter(distance<3900) %>% nrow())/nrow(rides_dist) + +#model all trips +model <- lm(Fahrtdauer_s ~ distance, data = rides_dist) +summary(model) +model$coefficients + +ggplot(data = rides_dist, aes(x = distance, y = Fahrtdauer_s)) + + geom_point() + + geom_abline(slope = 0.06835826, intercept = 281.77568059, color = "#b5de2b", linetype="dashed", linewidth=1.5)+ + xlab("distance (m)")+ + ylab("travel time (s)") + +#model non-shared rides +solo_rides <- rides_dist %>% filter(Geteilte.Fahrt=="No") +model <- lm(Fahrtdauer_s ~ distance, data = solo_rides) +summary(model) +model$coefficients + +ggplot(data = solo_rides, aes(x = distance, y = Fahrtdauer_s)) + + geom_point() + + geom_abline(slope = 0.05595693 , intercept = 206.44990288, color = "#2a788e", linetype="dashed", linewidth=1.5)+ + xlab("distance (m)")+ + ylab("travel time (min)") + +#### stop R5R +r5r::stop_r5(r5r_core) +rJava::.jgc(R.gc = TRUE) diff --git a/src/main/R/thesisJBAnalysis/readme_JB.txt b/src/main/R/thesisJBAnalysis/readme_JB.txt new file mode 100644 index 0000000..f87b55c --- /dev/null +++ b/src/main/R/thesisJBAnalysis/readme_JB.txt @@ -0,0 +1,5 @@ +1. Import .csv file of all ride requests into data_prep.R, save data_plausible as rides_clean.csv +2. Use r5r.R to calculate shortest path distances for KEXI data and create linear regression models, save data frame including distances as rides_dist.csv + -this requires the network in .pbf format +3. KEXI_analysis.R: analyse rides_clean.csv and rides_dist.csv +4. Accessibility_analysis.R: accessibility runs over time can be analysed here. Looking back this is very hard coded, it only works for the specific time slots and adjustments need to be made if file names change. \ No newline at end of file diff --git a/src/main/java/org/matsim/run/RunKelheimScenario.java b/src/main/java/org/matsim/run/RunKelheimScenario.java index 868731b..5ef9370 100644 --- a/src/main/java/org/matsim/run/RunKelheimScenario.java +++ b/src/main/java/org/matsim/run/RunKelheimScenario.java @@ -80,6 +80,10 @@ import java.io.BufferedReader; import java.io.IOException; import java.io.InputStreamReader; +import java.net.HttpURLConnection; +import java.net.MalformedURLException; +import java.net.ProtocolException; +import java.net.URL; import java.nio.file.Files; import java.nio.file.Path; import java.util.List; @@ -238,7 +242,8 @@ protected Config prepareConfig(Config config) { //TODO: temp, allow accessibility computations to occur more than 1.5km away from drt stops. drtConfigGroup.maxWalkDistance = 100000.; - drtConfigGroup.transitStopFile = "https://svn.vsp.tu-berlin.de/repos/public-svn/matsim/scenarios/countries/de/kelheim/kelheim-v3.0/input/kelheim-v3.0-drt-stops.xml"; + drtConfigGroup.transitStopFile = "https://svn.vsp.tu-berlin.de/repos/public-svn/matsim/scenarios/countries/de/kelheim/kelheim-drt-accessibility-JB-master/input/drt-stops-land.xml"; + //transit stop file town Kelheim "https://svn.vsp.tu-berlin.de/repos/public-svn/matsim/scenarios/countries/de/kelheim/kelheim-v3.0/input/kelheim-v3.0-drt-stops.xml" } @@ -252,18 +257,18 @@ protected Config prepareConfig(Config config) { config.routing().setRoutingRandomness(0); // settings for kelheim city - double mapCenterX = 712144.17; - double mapCenterY = 5422153.87; - - double tileSize = 200; - double num_rows = 23; - -// settings for east kelheim: -// double mapCenterX = 721455; -// double mapCenterY = 5410601; +// double mapCenterX = 712144.17; +// double mapCenterY = 5422153.87; // // double tileSize = 200; -// double num_rows = 50; +// double num_rows = 23; + +// settings for east kelheim: + double mapCenterX = 721455; + double mapCenterY = 5410601; + + double tileSize = 200; + double num_rows = 50; // settings for landkreis // double mapCenterX = 711014; @@ -278,7 +283,10 @@ protected Config prepareConfig(Config config) { accConfig.setBoundingBoxBottom(mapCenterY - num_rows*tileSize - tileSize/2); accConfig.setBoundingBoxTop(mapCenterY + num_rows*tileSize + tileSize/2); accConfig.setTileSize_m((int) tileSize); - accConfig.setTimeOfDay(19 * 60 * 60.); + + //random numbers generated for time slots between 8:00 and 17:00: 8.68, 14.26, 10.09, 9.43, 13.69, 15.94, 11.89, 13.98, 16.43 + //random numbers generated for time slots between 15:00 and 20:00: 15.20, 15.85, 16.78, 18.07, 19.61 + accConfig.setTimeOfDay(12 * 60 * 60.); accConfig.setComputingAccessibilityForMode(Modes4Accessibility.freespeed, false); // works accConfig.setComputingAccessibilityForMode(Modes4Accessibility.car, true); // works // accConfig.setComputingAccessibilityForMode(Modes4Accessibility.bike, false); // ?? @@ -366,8 +374,18 @@ protected void prepareScenario(Scenario scenario) { // Use this method if reading facilities from a csv. - Path filePath = Path.of("supermarkets_LK.csv"); - try (CSVParser parser = new CSVParser(new BufferedReader(new InputStreamReader(Files.newInputStream(filePath))), + String filePath = "https://svn.vsp.tu-berlin.de/repos/public-svn/matsim/scenarios/countries/de/kelheim/kelheim-drt-accessibility-JB-master/input/pois_complete.csv"; + HttpURLConnection connection = null; + try { + connection = (HttpURLConnection) new URL(filePath).openConnection(); + connection.setRequestMethod("GET"); + } catch (ProtocolException | MalformedURLException e) { + throw new RuntimeException(e); + } catch (IOException e) { + throw new RuntimeException(e); + } + try (CSVParser parser = new CSVParser(new BufferedReader(new InputStreamReader(connection.getInputStream())), + CSVFormat.DEFAULT.withDelimiter(',').withFirstRecordAsHeader())) { for (CSVRecord record : parser) { @@ -504,9 +522,9 @@ public void handleEvent(PersonDepartureEvent event) { // moduleTrain.setConsideredActivityType("train_station"); // controler.addOverridingModule(moduleTrain); -// final AccessibilityModule moduleHealth = new AccessibilityModule(); -// moduleHealth.setConsideredActivityType("health"); -// controler.addOverridingModule(moduleHealth); +// final AccessibilityModule modulePharmacy = new AccessibilityModule(); +// modulePharmacy.setConsideredActivityType("pharmacy"); +// controler.addOverridingModule(modulePharmacy); // // final AccessibilityModule moduleDoctors = new AccessibilityModule(); // moduleDoctors.setConsideredActivityType("doctor"); @@ -516,13 +534,13 @@ public void handleEvent(PersonDepartureEvent event) { // moduleSport.setConsideredActivityType("sport"); // controler.addOverridingModule(moduleSport); -// final AccessibilityModule moduleGroceries = new AccessibilityModule(); -// moduleGroceries.setConsideredActivityType("groceries"); -// controler.addOverridingModule(moduleGroceries); +// final AccessibilityModule moduleAmenity = new AccessibilityModule(); +// moduleAmenity.setConsideredActivityType("amenity"); +// controler.addOverridingModule(moduleAmenity); - final AccessibilityModule moduleSupermarkets = new AccessibilityModule(); - moduleSupermarkets.setConsideredActivityType("supermarket"); - controler.addOverridingModule(moduleSupermarkets); +// final AccessibilityModule moduleSupermarkets = new AccessibilityModule(); +// moduleSupermarkets.setConsideredActivityType("supermarket"); +// controler.addOverridingModule(moduleSupermarkets); // // final AccessibilityModule moduleAltstadt = new AccessibilityModule(); // moduleAltstadt.setConsideredActivityType("altstadt"); @@ -535,6 +553,10 @@ public void handleEvent(PersonDepartureEvent event) { // final AccessibilityModule moduleSenioren = new AccessibilityModule(); // moduleSenioren.setConsideredActivityType("senioren"); // controler.addOverridingModule(moduleSenioren); + + final AccessibilityModule moduleChurch = new AccessibilityModule(); + moduleChurch.setConsideredActivityType("church"); + controler.addOverridingModule(moduleChurch); } } } diff --git a/supermarkets_LK.csv b/supermarkets_LK.csv deleted file mode 100644 index f72853b..0000000 --- a/supermarkets_LK.csv +++ /dev/null @@ -1,28 +0,0 @@ -"id","x","y","type" -1,723746.2221000000,5424827.6290000000,supermarket -2,709700.0877000000,5410369.0540000000,supermarket -3,709838.2051000000,5410320.1830000000,supermarket -4,709349.9017000000,5410795.4830000000,supermarket -5,706815.8387000000,5398826.7890000000,supermarket -6,715846.2768000000,5421485.5920000000,supermarket -7,702406.3107000000,5410374.2480000000,supermarket -8,702672.7821000000,5410204.0650000000,supermarket -9,711894.2993000000,5422270.8970000000,supermarket -10,710314.4025000000,5421672.4900000000,supermarket -11,710236.7883000000,5421709.2820000000,supermarket -12,711295.6154000000,5422755.3680000000,supermarket -13,711247.3028000000,5422770.3510000000,supermarket -14,705237.2085000000,5391759.1200000000,supermarket -15,705269.6193000000,5391757.3440000000,supermarket -16,705102.4232000000,5392407.5250000000,supermarket -17,718110.2046000000,5406151.6420000000,supermarket -18,723910.4494000000,5412090.3440000000,supermarket -19,710321.2780000000,5412135.4320000000,supermarket -20,713405.2027000000,5406044.9470000000,supermarket -21,711997.3195000000,5421707.3630000000,supermarket -22,712806.9355000000,5420816.0550000000,supermarket -23,720944.9000000000,5420014.2290000000,supermarket -24,696420.1301000000,5426752.8980000000,supermarket -25,711793.1799000000,5422423.8100000000,supermarket -26,724396.7235000000,5412449.7770000000,supermarket -27,725612.7606000000,5408447.0760000000,supermarket