scripts/test_code/old/eval_benefits3.R
In cyipt/cyipt: Cycling Infrastructure Prioritisation Toolkit
#Evaluate Schemes
#Gets PCT Values for the road segments
############################################
#NOTE: THIS OVERRIGHTS EXISTING FILES RATHER THAN CREATING NEW FILES
#############################################
library(sf)
library(dplyr)
library(parallel)
#Settings now come from master file
#skip <- FALSE #Skip Files that already have PCT values
#ncores <- 4 #number of cores to use in parallel processing
#overwrite <- FALSE #Overwrite or create new file
##############################################
# Benefit Inputs: These are values taken from WebTAG and other sources
#############################################
qual.data <- data.frame(scheme = c("Off-road segregated cycle track", "On-road segregated cycle lane",
"On-road non-segregated cycle lane", "Wider lane", "Shared bus lane"),
value = c(8.07,3.43,3.41,2.08,0.88))
#Gender and age split of the observed main-mode cycle trips in England (reference: NTS 2012-14).
gender_split <- data.frame(Male = c(16,39,13,5,1), Female = c(4,16,5,1,0))
row.names(gender_split) <- c("0-19","20-49","50-64","65-80","80+")
# Observed speed of main mode cycling trips (miles/hour) in England (reference: NTS 2012-2014).
gender_speed <- data.frame(Male = c(6.12,9.12,8.91,7.48,7.99), Female = c(4.45,7.22,7.07,5.99,4.2))
row.names(gender_speed) <- c("0-19","20-49","50-64","65-80","80+")
# Observed speed of main mode walking trips (miles/hour) in England (reference: NTS 2012-2014).
walking_speed <- data.frame(Male = c(2.55,2.74,2.62,2.49,2.11), Female = c(2.53,2.6,2.49,2.35,2.04))
row.names(gender_speed) <- c("0-19","20-49","50-64","65-80","80+")
#Background mortality rates by age and gender (reference: Global Burden of Disease Study 2015 results for England)
gender_mortality <- data.frame(Male = c(4.1949E-04,1.1833E-03,6.2669E-03,2.4591E-02,1.1471E-01), Female = c(3.1919E-04,7.1164E-04,4.1887E-03,1.6686E-02,9.9484E-02))
row.names(gender_mortality) <- c("0-19","20-49","50-64","65-80","80+")
#Discounted, average Years of Life Lost (YLL) loss per death
gender_YLL <- data.frame(Male = c(47.71,34.06,23.73,15.13,5.78), Female = c(48.00,33.55,23.73,14.34,5.78))
row.names(gender_YLL) <- c("0-19","20-49","50-64","65-80","80+")
#Discount rate
years <- 0:20
dis <- (1/1.015) ** years
discount <- data.frame(year = years, discount = dis )
rm(years,dis)
##########################################
#List folders
#regions <- list.dirs(path = "../cyipt-bigdata/osm-raw", full.names = FALSE) # Now get regions from the master file
#regions <- regions[2:length(regions)]
regions <- regions.todo
for(b in 1:length(regions)){
if(file.exists(paste0("../cyipt-bigdata/osm-prep/",regions[b],"/schemes.Rds"))){
#Get in all the data
osm <- readRDS(paste0("../cyipt-bigdata/osm-prep/",regions[b],"/osm-lines.Rds"))
osm$group_id[is.na(osm$group_id)] <- 0
schemes <- readRDS(paste0("../cyipt-bigdata/osm-prep/",regions[b],"/schemes-simplified.Rds"))
osm2pct <- readRDS(paste0("../cyipt-bigdata/osm-prep/",regions[b],"/osm2pct.Rds"))
pct2osm <- readRDS(paste0("../cyipt-bigdata/osm-prep/",regions[b],"/pct2osm.Rds"))
pct <- readRDS(paste0("../cyipt-securedata/pct-regions/",regions[b],".Rds"))
pct <- as.data.frame(pct)
pct <- pct[,c("ID","length","all_16p","pct.census","underground","train","bus","taxi","motorcycle","carorvan","passenger","onfoot","other")]
route.uptake <- readRDS(paste0("../cyipt-bigdata/osm-prep/",regions[b],"/route-uptake.Rds"))
route.uptake <- route.uptake[,c("ID","schemeID","perincrease","uptake")]
route.uptake <- left_join(route.uptake, pct, by = c("ID" = "ID"))
#Check if PCT values exist in the file
if(all(c("absenteeism_benefit","health_deathavoided","health_benefit","ncycle_before",
"ncycle_after","ndrive_before","ndrive_after","jouney_qual_ben","ghg_benefit") %in% names(schemes)) & skip){
message(paste0("Benefits already calcualted for ",regions[b]," so skipping"))
}else{
message(paste0("Cal Benefits Schemes for ",regions[b]," at ",Sys.time() ))
#Calcualte Uptake Values
#Calcualte the percentage of each mode
route.uptake$p_underground <- route.uptake$underground / (route.uptake$all_16p - route.uptake$pct.census)
route.uptake$p_train <- route.uptake$train / (route.uptake$all_16p - route.uptake$pct.census)
route.uptake$p_bus <- route.uptake$bus / (route.uptake$all_16p - route.uptake$pct.census)
route.uptake$p_taxi <- route.uptake$taxi / (route.uptake$all_16p - route.uptake$pct.census)
route.uptake$p_motorcycle <- route.uptake$motorcycle / (route.uptake$all_16p - route.uptake$pct.census)
route.uptake$p_carorvan <- route.uptake$carorvan / (route.uptake$all_16p - route.uptake$pct.census)
route.uptake$p_passenger <- route.uptake$passenger / (route.uptake$all_16p - route.uptake$pct.census)
route.uptake$p_onfoot <- route.uptake$onfoot / (route.uptake$all_16p - route.uptake$pct.census)
route.uptake$p_other <- route.uptake$other / (route.uptake$all_16p - route.uptake$pct.census)
#Calcualte the decrease in each mode
route.uptake$d_underground <- route.uptake$p_underground * route.uptake$uptake
route.uptake$d_train <- route.uptake$p_train * route.uptake$uptake
route.uptake$d_bus <- route.uptake$p_bus * route.uptake$uptake
route.uptake$d_taxi <- route.uptake$p_taxi * route.uptake$uptake
route.uptake$d_motorcycle <- route.uptake$p_motorcycle * route.uptake$uptake
route.uptake$d_carorvan <- route.uptake$p_carorvan * route.uptake$uptake
route.uptake$d_passenger <- route.uptake$p_passenger * route.uptake$uptake
route.uptake$d_onfoot <- route.uptake$p_onfoot * route.uptake$uptake
route.uptake$d_other <- route.uptake$p_other * route.uptake$uptake
#Prep Schemes DF
# Work out where the new cyclists come from
schemes$ncycle_before <- schemes$census
schemes$ncycle_after <- schemes$model.future
#Add Empty Columns
schemes$ndrive_before <- NA
schemes$ndrive_after <- NA
schemes$absenteeism_benefit <- NA
schemes$health_deathavoided <- NA
schemes$health_benefit <- NA
schemes <- st_cast(schemes, "MULTILINESTRING")
schemes$length <- as.numeric(st_length(schemes))
for(e in 1:nrow(schemes)){
message(paste0(Sys.time()," Doing scheme ",e," of ",nrow(schemes)))
scheme_id <- schemes$group_id[e]
osm_ids <- osm$id[osm$group_id == scheme_id]
#pct_ids <- unique(unlist(osm2pct[unique(osm_ids)]))
#pct_codes <- pct$ID[pct_ids]
route.up.sub <- route.uptake[route.uptake$schemeID == scheme_id,]
#route.up.sub <- route.up.sub[route.up.sub$ID %in% pct_codes,]
pct.sub <- pct[pct$ID %in% route.up.sub$ID,]
#route.up.sub <- left_join(route.up.sub,pct.sub, by = c("ID" = "ID"))
drivenow <- sum(route.up.sub$taxi, route.up.sub$motorcycle, route.up.sub$carorvan, na.rm = T)
driveafter <- round(drivenow - sum(route.up.sub$d_taxi, route.up.sub$d_motorcycle, route.up.sub$d_carorvan, na.rm = T),0)
schemes$ndrive_before[e] <- drivenow
schemes$ndrive_after[e] <- driveafter
#caluclate the drivign distance
route.up.sub$drivedistnow <- route.up.sub$taxi + route.up.sub$motorcycle + route.up.sub$carorvan * route.up.sub$length / 1000
route.up.sub$drivedistafter <- (route.up.sub$taxi - route.up.sub$d_taxi) + (route.up.sub$motorcycle - route.up.sub$d_motorcycle) + (route.up.sub$carorvan - route.up.sub$d_carorvan) * route.up.sub$length / 1000
# Multimply Up to a year
schemes$carkm_before[e] <- sum(route.up.sub$drivedistnow, na.rm = T) * 2 * 220
schemes$carkm_after[e] <- sum(route.up.sub$drivedistafter, na.rm = T) * 2 * 220
schemes$carkm[e] <- schemes$carkm_after[e] - schemes$carkm_before[e]
########################################################################################################
# Heath Benefits
########################################################################################################
#Estimate the age a gender mix of new cyclists
#Note: does not use weight as PCT values better represent number of cyclists than number of trips
#Otherwise the weight roughyl halves the number of cyclists
#dist <- schemes$length[e] * 1.9 / 1609.34 # convert to miles * 1.9 (two way weighting factor)
route.up.sub$disthealth <- route.up.sub$length * 1.9 / 1609.34 # convert to miles * 1.9 (two way weighting factor)
route.up.sub$absenteeism_benefit <- NA
route.up.sub$health_deathavoided <- NA
route.up.sub$health_benefit <- NA
#Loop over each route and get the health benefits
for(f in 1:nrow(route.up.sub)){
# create results tables
results <- data.frame(id = f , absenteeism_benefit = NA, health_deathavoided = NA, health_benefit = NA )
# Physical Activity
# Based on http://www.cedar.iph.cam.ac.uk/blog/dft-tag-cedar-010917/
# Estiamte the age and gender spit of the new cyclists and lost walkers
increase_cyclers <- route.up.sub$uptake[f]
decrease_walkers <- route.up.sub$d_onfoot[f]
increase_cyclers <- gender_split / 100 * increase_cyclers
decrease_walkers <- gender_split / 100 * decrease_walkers
#############################
# Health Benefits of Cycling
############################
#convert to hours per week for each gender and age bracket
cycle_hours <- (route.up.sub$disthealth[f] * 4.22) / gender_speed # distance per week = distance * 4.22 (days per week)
#convert to METS
#Average Metabolically Equivalent Tasks (MET) for cycling
#(reference: Compendium of Physical Activities, https://sites.google.com/site/compendiumofphysicalactivities/)
cycle_deathsavoided <- cycle_hours * 6.8
#Convert to Relative Risks
# Relative risks (RRs) for all-cause mortality for cycling (reference: Kelly et al. 2014) 0.9
cycle_deathsavoided <- exp(cycle_deathsavoided * log(0.9)/11.25)
# Max benefits from cycling (benefit cap) 0.55
cycle_deathsavoided[cycle_deathsavoided < 0.55] <- 0.55
#Convert to PAF due to cycling
cycle_deathsavoided <- 1 - cycle_deathsavoided
#Convert to Deaths Avoided
cycle_deathsavoided <- cycle_deathsavoided * gender_mortality * increase_cyclers
#factor out 0-19 years
cycle_deathsavoided[1,] <- c(0,0)
#Calc Years of Life Lost
cycle_yearslost <- cycle_deathsavoided * gender_YLL
cycle_health_benefits <- discount
cycle_health_benefits$benefits <- cycle_health_benefits$discount * sum(cycle_yearslost) * 60000 # Value of a startical life in 2012
##################################
# Health Disbenefits of Less Walking
##################################
#convert to hours per week for each gender and age bracket
walk_hours <- (route.up.sub$disthealth[f] * 4.22) / walking_speed # distance per week = distance * 4.22 (days per week)
#convert to METS
#Average Metabolically Equivalent Tasks (MET) for cycling
#(reference: Compendium of Physical Activities, https://sites.google.com/site/compendiumofphysicalactivities/)
walk_deathsincrease <- walk_hours * 3.3
#Convert to Relative Risks
# Relative risks (RRs) for all-cause mortality for cycling (reference: Kelly et al. 2014) 0.9
walk_deathsincrease <- exp(walk_deathsincrease * log(0.9)/11.25)
# Max benefits from walking (benefit cap) 0.55
walk_deathsincrease[walk_deathsincrease < 0.7] <- 0.7
#Convert to PAF due to cycling
walk_deathsincrease <- 1 - walk_deathsincrease
#Convert to Deaths Avoided
walk_deathsincrease <- walk_deathsincrease * gender_mortality * decrease_walkers * -1
#factor out 0-19 years
walk_deathsincrease[1,] <- c(0,0)
#Calc Years of Life Lost
walk_yearslost <- walk_deathsincrease * gender_YLL
walk_health_benefits <- discount
walk_health_benefits$benefits <- walk_health_benefits$discount * sum(walk_yearslost) * 60000 # Value of a startical life in 2012
route.up.sub$health_deathavoided[f] <- sum(cycle_deathsavoided) + sum(walk_deathsincrease)
route.up.sub$health_benefit[f] <- sum(cycle_health_benefits$benefits + walk_health_benefits$benefits)
###############################################
# Absenteeism
##############################################
# Calcualte the Number of people who are doing an additonal 30 minute of exercies
# Convert from hours per weeks to minutes per days to effective people getting extra 30 min per day
cycle_extraexercies <- (cycle_hours / 4.22 * 60) * increase_cyclers / 30
walk_lessexercies <- (walk_hours / 4.22 * 60) * decrease_walkers / 30
# Zero Out the Retired
cycle_extraexercies[4,] <- c(0,0)
cycle_extraexercies[5,] <- c(0,0)
walk_lessexercies[4,] <- c(0,0)
walk_lessexercies[5,] <- c(0,0)
# Calcualte recution in absenteeism
# 25% reduction on average of 6.8 days per year
# average of £19.27 per hour for 7.48 hours per day
cycle_absenteeism <- sum(cycle_extraexercies * 6.8 * 0.25) * 19.27 * 7.48
walk_absenteeism <- sum(walk_lessexercies * 6.8 * 0.25) * 19.27 * 7.48
route.up.sub$absenteeism_benefit[f] <- cycle_absenteeism - walk_absenteeism
###################################################
# Jounrey Quality
##################################################
# Jouney Qualitiy
# From WebTAG A4.1.6
# Value of jounrey ambience benefit of cycling facilities 2015 prices and values
#For Each Route Get the length of the route that is on the scheme
# Get the osm lines that make up this route
route.up.osms <- unique(unlist(pct2osm[ (1:nrow(pct))[pct$ID == route.up.sub$ID[f] ] ]))
#Get the ones that are also in the scheme
route.up.osms <- route.up.osms[route.up.osms %in% osm_ids]
osm.route.sub <- osm[route.up.osms,]
#Select correct value and convert to £ / s
osm.route.sub$qualval <- NA
for(a in seq_len(nrow(osm.route.sub))){
#Select correct value and convert to £ / s
if(osm.route.sub$Recommended[a] %in% c("Segregated Cycle Track", "Stepped Cycle Tracks", "Cycle Lanes with light segregation")){
osm.route.sub$qualval[a] <- qual.data$value[qual.data$scheme == "On-road segregated cycle lane"] / 100 / 60
}else if(osm.route.sub$Recommended[a] == "Cycle Lanes"){
osm.route.sub$qualval[a] <- qual.data$value[qual.data$scheme == "On-road non-segregated cycle lane"] / 100 / 60
}else if(osm.route.sub$Recommended[a] == "Segregated Cycle Track on Path"){
osm.route.sub$qualval[a] <- qual.data$value[qual.data$scheme == "Off-road segregated cycle track"] / 100 / 60
}else{
osm.route.sub$qualval[a] <- 0
}
}
#Ben = #cyclists X length cycled / 8 mph X value of time cycled X 2 for return journey * 220 days per year * 0.5 (rule of half) + same again for existing without rule of half
osm.route.sub$jouney_qual_ben <- (route.up.sub$uptake[f]) * osm.route.sub$length * 0.5 / 3.576 * osm.route.sub$qualval * 2 * 220 * 0.5 + (route.up.sub$pct.census[f]) * osm.route.sub$length * 0.5 / 3.576 * osm.route.sub$qualval * 2 * 220
route.up.sub$quality_benefit[f] <- sum(osm.route.sub$jouney_qual_ben)
}
schemes$absenteeism_benefit[e] <- sum(route.up.sub$absenteeism_benefit, na.rm = T)
schemes$health_deathavoided[e] <- sum(route.up.sub$health_deathavoided, na.rm = T)
schemes$health_benefit[e] <- sum(route.up.sub$health_benefit, na.rm = T)
schemes$quality_benefit[e] <- sum(route.up.sub$quality_benefit, na.rm = T)
rm(route.up.sub)
}
######################################################################
# Accidents
#####################################################################
# Very simple method based on average marginal cost of 1.7p per car km
schemes$accidents_benefit <- round(-1 * schemes$carkm * 1.7 / 100 ,0)
######################################################################
# GHG emissions
######################################################################
pdiesel <- 0.39 #Proportion of diesel cars https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/608374/vehicle-licensing-statistics-2016.pdf
velo <- 48 # average speed assumed to be 30 mph
schemes$co2saved <- NA
for(g in 1:nrow(schemes)){
#Fuel Consumption for petrol and desiel cars
cons.petrol <- (1.18011 / velo) + 0.04639 + (-0.00009 * velo) + (0.000003 * velo **2) * (- schemes$carkm[g] * (1 - pdiesel))
cons.diesel <- (0.51887 / velo) + 0.06556 + (-0.00062 * velo) + (0.000005 * velo **2) * (- schemes$carkm[g] * pdiesel)
#convert litres to kg co2e
emiss.petrol <- cons.petrol * 2.160
emiss.diesel <- cons.diesel * 2.556
schemes$co2saved[g] <- round(emiss.petrol + emiss.diesel,0)
rm(emiss.diesel, emiss.petrol, cons.petrol, cons.diesel)
}
#Convert to £ benefit based on WebTAG Databook A 3.3.
schemes$ghg_benefit <- round(schemes$co2saved / 1000 * 64.66,0)
########################################################################
#air quality - WebTAG being updated soon
########################################################################
#schemes$airqual_benefit <- 0
#######################################################################
#noise
#######################################################################
#schemes$noise_benefit <- 0
######################################################################
# indirect tax revenue
######################################################################
#schemes$tax_benefit <- 0
#######################################################################
# travel time (decongestion)
#######################################################################
# Cost of congestion from WebTAG Data book A5.4.4
#For yorkshire Mon-Fri Average 15.6 p / km
schemes$congestion_benefit <- round(schemes$carkm * 0.156 * -1, 2)
#################################################################
# Total Benefits
####################################################################
schemes$totalBen <- schemes$congestion_benefit + schemes$health_benefit +
schemes$jouney_qual_ben + schemes$ghg_benefit +
schemes$absenteeism_benefit
#+ schemes$airqual_benefit +
#schemes$noise_benefit + schemes$tax_benefit
schemes$costBenRatio <- round(schemes$totalBen/schemes$costTotal,1)
#foo <- as.data.frame(schemes)
#foo$geometry <- NULL
saveRDS(schemes,paste0("../cyipt-bigdata/osm-prep/",regions[b],"/schemes-simplified.Rds"))
#rm(scheme)
}
}else{
message(paste0("Input File Missing for ",regions[b]," at ",Sys.time()))
}
}
rm(b,regions)
cyipt/cyipt documentation built on Aug. 16, 2020, 10:24 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
#Evaluate Schemes
#Gets PCT Values for the road segments
############################################
#NOTE: THIS OVERRIGHTS EXISTING FILES RATHER THAN CREATING NEW FILES
#############################################
library(sf)
library(dplyr)
library(parallel)
#Settings now come from master file
#skip <- FALSE #Skip Files that already have PCT values
#ncores <- 4 #number of cores to use in parallel processing
#overwrite <- FALSE #Overwrite or create new file
##############################################
# Benefit Inputs: These are values taken from WebTAG and other sources
#############################################
qual.data <- data.frame(scheme = c("Off-road segregated cycle track", "On-road segregated cycle lane",
"On-road non-segregated cycle lane", "Wider lane", "Shared bus lane"),
value = c(8.07,3.43,3.41,2.08,0.88))
#Gender and age split of the observed main-mode cycle trips in England (reference: NTS 2012-14).
gender_split <- data.frame(Male = c(16,39,13,5,1), Female = c(4,16,5,1,0))
row.names(gender_split) <- c("0-19","20-49","50-64","65-80","80+")
# Observed speed of main mode cycling trips (miles/hour) in England (reference: NTS 2012-2014).
gender_speed <- data.frame(Male = c(6.12,9.12,8.91,7.48,7.99), Female = c(4.45,7.22,7.07,5.99,4.2))
row.names(gender_speed) <- c("0-19","20-49","50-64","65-80","80+")
# Observed speed of main mode walking trips (miles/hour) in England (reference: NTS 2012-2014).
walking_speed <- data.frame(Male = c(2.55,2.74,2.62,2.49,2.11), Female = c(2.53,2.6,2.49,2.35,2.04))
row.names(gender_speed) <- c("0-19","20-49","50-64","65-80","80+")
#Background mortality rates by age and gender (reference: Global Burden of Disease Study 2015 results for England)
gender_mortality <- data.frame(Male = c(4.1949E-04,1.1833E-03,6.2669E-03,2.4591E-02,1.1471E-01), Female = c(3.1919E-04,7.1164E-04,4.1887E-03,1.6686E-02,9.9484E-02))
row.names(gender_mortality) <- c("0-19","20-49","50-64","65-80","80+")
#Discounted, average Years of Life Lost (YLL) loss per death
gender_YLL <- data.frame(Male = c(47.71,34.06,23.73,15.13,5.78), Female = c(48.00,33.55,23.73,14.34,5.78))
row.names(gender_YLL) <- c("0-19","20-49","50-64","65-80","80+")
#Discount rate
years <- 0:20
dis <- (1/1.015) ** years
discount <- data.frame(year = years, discount = dis )
rm(years,dis)
##########################################
#List folders
#regions <- list.dirs(path = "../cyipt-bigdata/osm-raw", full.names = FALSE) # Now get regions from the master file
#regions <- regions[2:length(regions)]
regions <- regions.todo
for(b in 1:length(regions)){
if(file.exists(paste0("../cyipt-bigdata/osm-prep/",regions[b],"/schemes.Rds"))){
#Get in all the data
osm <- readRDS(paste0("../cyipt-bigdata/osm-prep/",regions[b],"/osm-lines.Rds"))
osm$group_id[is.na(osm$group_id)] <- 0
schemes <- readRDS(paste0("../cyipt-bigdata/osm-prep/",regions[b],"/schemes-simplified.Rds"))
osm2pct <- readRDS(paste0("../cyipt-bigdata/osm-prep/",regions[b],"/osm2pct.Rds"))
pct2osm <- readRDS(paste0("../cyipt-bigdata/osm-prep/",regions[b],"/pct2osm.Rds"))
pct <- readRDS(paste0("../cyipt-securedata/pct-regions/",regions[b],".Rds"))
pct <- as.data.frame(pct)
pct <- pct[,c("ID","length","all_16p","pct.census","underground","train","bus","taxi","motorcycle","carorvan","passenger","onfoot","other")]
route.uptake <- readRDS(paste0("../cyipt-bigdata/osm-prep/",regions[b],"/route-uptake.Rds"))
route.uptake <- route.uptake[,c("ID","schemeID","perincrease","uptake")]
route.uptake <- left_join(route.uptake, pct, by = c("ID" = "ID"))
#Check if PCT values exist in the file
if(all(c("absenteeism_benefit","health_deathavoided","health_benefit","ncycle_before",
"ncycle_after","ndrive_before","ndrive_after","jouney_qual_ben","ghg_benefit") %in% names(schemes)) & skip){
message(paste0("Benefits already calcualted for ",regions[b]," so skipping"))
}else{
message(paste0("Cal Benefits Schemes for ",regions[b]," at ",Sys.time() ))
#Calcualte Uptake Values
#Calcualte the percentage of each mode
route.uptake$p_underground <- route.uptake$underground / (route.uptake$all_16p - route.uptake$pct.census)
route.uptake$p_train <- route.uptake$train / (route.uptake$all_16p - route.uptake$pct.census)
route.uptake$p_bus <- route.uptake$bus / (route.uptake$all_16p - route.uptake$pct.census)
route.uptake$p_taxi <- route.uptake$taxi / (route.uptake$all_16p - route.uptake$pct.census)
route.uptake$p_motorcycle <- route.uptake$motorcycle / (route.uptake$all_16p - route.uptake$pct.census)
route.uptake$p_carorvan <- route.uptake$carorvan / (route.uptake$all_16p - route.uptake$pct.census)
route.uptake$p_passenger <- route.uptake$passenger / (route.uptake$all_16p - route.uptake$pct.census)
route.uptake$p_onfoot <- route.uptake$onfoot / (route.uptake$all_16p - route.uptake$pct.census)
route.uptake$p_other <- route.uptake$other / (route.uptake$all_16p - route.uptake$pct.census)
#Calcualte the decrease in each mode
route.uptake$d_underground <- route.uptake$p_underground * route.uptake$uptake
route.uptake$d_train <- route.uptake$p_train * route.uptake$uptake
route.uptake$d_bus <- route.uptake$p_bus * route.uptake$uptake
route.uptake$d_taxi <- route.uptake$p_taxi * route.uptake$uptake
route.uptake$d_motorcycle <- route.uptake$p_motorcycle * route.uptake$uptake
route.uptake$d_carorvan <- route.uptake$p_carorvan * route.uptake$uptake
route.uptake$d_passenger <- route.uptake$p_passenger * route.uptake$uptake
route.uptake$d_onfoot <- route.uptake$p_onfoot * route.uptake$uptake
route.uptake$d_other <- route.uptake$p_other * route.uptake$uptake
#Prep Schemes DF
# Work out where the new cyclists come from
schemes$ncycle_before <- schemes$census
schemes$ncycle_after <- schemes$model.future
#Add Empty Columns
schemes$ndrive_before <- NA
schemes$ndrive_after <- NA
schemes$absenteeism_benefit <- NA
schemes$health_deathavoided <- NA
schemes$health_benefit <- NA
schemes <- st_cast(schemes, "MULTILINESTRING")
schemes$length <- as.numeric(st_length(schemes))
for(e in 1:nrow(schemes)){
message(paste0(Sys.time()," Doing scheme ",e," of ",nrow(schemes)))
scheme_id <- schemes$group_id[e]
osm_ids <- osm$id[osm$group_id == scheme_id]
#pct_ids <- unique(unlist(osm2pct[unique(osm_ids)]))
#pct_codes <- pct$ID[pct_ids]
route.up.sub <- route.uptake[route.uptake$schemeID == scheme_id,]
#route.up.sub <- route.up.sub[route.up.sub$ID %in% pct_codes,]
pct.sub <- pct[pct$ID %in% route.up.sub$ID,]
#route.up.sub <- left_join(route.up.sub,pct.sub, by = c("ID" = "ID"))
drivenow <- sum(route.up.sub$taxi, route.up.sub$motorcycle, route.up.sub$carorvan, na.rm = T)
driveafter <- round(drivenow - sum(route.up.sub$d_taxi, route.up.sub$d_motorcycle, route.up.sub$d_carorvan, na.rm = T),0)
schemes$ndrive_before[e] <- drivenow
schemes$ndrive_after[e] <- driveafter
#caluclate the drivign distance
route.up.sub$drivedistnow <- route.up.sub$taxi + route.up.sub$motorcycle + route.up.sub$carorvan * route.up.sub$length / 1000
route.up.sub$drivedistafter <- (route.up.sub$taxi - route.up.sub$d_taxi) + (route.up.sub$motorcycle - route.up.sub$d_motorcycle) + (route.up.sub$carorvan - route.up.sub$d_carorvan) * route.up.sub$length / 1000
# Multimply Up to a year
schemes$carkm_before[e] <- sum(route.up.sub$drivedistnow, na.rm = T) * 2 * 220
schemes$carkm_after[e] <- sum(route.up.sub$drivedistafter, na.rm = T) * 2 * 220
schemes$carkm[e] <- schemes$carkm_after[e] - schemes$carkm_before[e]
########################################################################################################
# Heath Benefits
########################################################################################################
#Estimate the age a gender mix of new cyclists
#Note: does not use weight as PCT values better represent number of cyclists than number of trips
#Otherwise the weight roughyl halves the number of cyclists
#dist <- schemes$length[e] * 1.9 / 1609.34 # convert to miles * 1.9 (two way weighting factor)
route.up.sub$disthealth <- route.up.sub$length * 1.9 / 1609.34 # convert to miles * 1.9 (two way weighting factor)
route.up.sub$absenteeism_benefit <- NA
route.up.sub$health_deathavoided <- NA
route.up.sub$health_benefit <- NA
#Loop over each route and get the health benefits
for(f in 1:nrow(route.up.sub)){
# create results tables
results <- data.frame(id = f , absenteeism_benefit = NA, health_deathavoided = NA, health_benefit = NA )
# Physical Activity
# Based on http://www.cedar.iph.cam.ac.uk/blog/dft-tag-cedar-010917/
# Estiamte the age and gender spit of the new cyclists and lost walkers
increase_cyclers <- route.up.sub$uptake[f]
decrease_walkers <- route.up.sub$d_onfoot[f]
increase_cyclers <- gender_split / 100 * increase_cyclers
decrease_walkers <- gender_split / 100 * decrease_walkers
#############################
# Health Benefits of Cycling
############################
#convert to hours per week for each gender and age bracket
cycle_hours <- (route.up.sub$disthealth[f] * 4.22) / gender_speed # distance per week = distance * 4.22 (days per week)
#convert to METS
#Average Metabolically Equivalent Tasks (MET) for cycling
#(reference: Compendium of Physical Activities, https://sites.google.com/site/compendiumofphysicalactivities/)
cycle_deathsavoided <- cycle_hours * 6.8
#Convert to Relative Risks
# Relative risks (RRs) for all-cause mortality for cycling (reference: Kelly et al. 2014) 0.9
cycle_deathsavoided <- exp(cycle_deathsavoided * log(0.9)/11.25)
# Max benefits from cycling (benefit cap) 0.55
cycle_deathsavoided[cycle_deathsavoided < 0.55] <- 0.55
#Convert to PAF due to cycling
cycle_deathsavoided <- 1 - cycle_deathsavoided
#Convert to Deaths Avoided
cycle_deathsavoided <- cycle_deathsavoided * gender_mortality * increase_cyclers
#factor out 0-19 years
cycle_deathsavoided[1,] <- c(0,0)
#Calc Years of Life Lost
cycle_yearslost <- cycle_deathsavoided * gender_YLL
cycle_health_benefits <- discount
cycle_health_benefits$benefits <- cycle_health_benefits$discount * sum(cycle_yearslost) * 60000 # Value of a startical life in 2012
##################################
# Health Disbenefits of Less Walking
##################################
#convert to hours per week for each gender and age bracket
walk_hours <- (route.up.sub$disthealth[f] * 4.22) / walking_speed # distance per week = distance * 4.22 (days per week)
#convert to METS
#Average Metabolically Equivalent Tasks (MET) for cycling
#(reference: Compendium of Physical Activities, https://sites.google.com/site/compendiumofphysicalactivities/)
walk_deathsincrease <- walk_hours * 3.3
#Convert to Relative Risks
# Relative risks (RRs) for all-cause mortality for cycling (reference: Kelly et al. 2014) 0.9
walk_deathsincrease <- exp(walk_deathsincrease * log(0.9)/11.25)
# Max benefits from walking (benefit cap) 0.55
walk_deathsincrease[walk_deathsincrease < 0.7] <- 0.7
#Convert to PAF due to cycling
walk_deathsincrease <- 1 - walk_deathsincrease
#Convert to Deaths Avoided
walk_deathsincrease <- walk_deathsincrease * gender_mortality * decrease_walkers * -1
#factor out 0-19 years
walk_deathsincrease[1,] <- c(0,0)
#Calc Years of Life Lost
walk_yearslost <- walk_deathsincrease * gender_YLL
walk_health_benefits <- discount
walk_health_benefits$benefits <- walk_health_benefits$discount * sum(walk_yearslost) * 60000 # Value of a startical life in 2012
route.up.sub$health_deathavoided[f] <- sum(cycle_deathsavoided) + sum(walk_deathsincrease)
route.up.sub$health_benefit[f] <- sum(cycle_health_benefits$benefits + walk_health_benefits$benefits)
###############################################
# Absenteeism
##############################################
# Calcualte the Number of people who are doing an additonal 30 minute of exercies
# Convert from hours per weeks to minutes per days to effective people getting extra 30 min per day
cycle_extraexercies <- (cycle_hours / 4.22 * 60) * increase_cyclers / 30
walk_lessexercies <- (walk_hours / 4.22 * 60) * decrease_walkers / 30
# Zero Out the Retired
cycle_extraexercies[4,] <- c(0,0)
cycle_extraexercies[5,] <- c(0,0)
walk_lessexercies[4,] <- c(0,0)
walk_lessexercies[5,] <- c(0,0)
# Calcualte recution in absenteeism
# 25% reduction on average of 6.8 days per year
# average of £19.27 per hour for 7.48 hours per day
cycle_absenteeism <- sum(cycle_extraexercies * 6.8 * 0.25) * 19.27 * 7.48
walk_absenteeism <- sum(walk_lessexercies * 6.8 * 0.25) * 19.27 * 7.48
route.up.sub$absenteeism_benefit[f] <- cycle_absenteeism - walk_absenteeism
###################################################
# Jounrey Quality
##################################################
# Jouney Qualitiy
# From WebTAG A4.1.6
# Value of jounrey ambience benefit of cycling facilities 2015 prices and values
#For Each Route Get the length of the route that is on the scheme
# Get the osm lines that make up this route
route.up.osms <- unique(unlist(pct2osm[ (1:nrow(pct))[pct$ID == route.up.sub$ID[f] ] ]))
#Get the ones that are also in the scheme
route.up.osms <- route.up.osms[route.up.osms %in% osm_ids]
osm.route.sub <- osm[route.up.osms,]
#Select correct value and convert to £ / s
osm.route.sub$qualval <- NA
for(a in seq_len(nrow(osm.route.sub))){
#Select correct value and convert to £ / s
if(osm.route.sub$Recommended[a] %in% c("Segregated Cycle Track", "Stepped Cycle Tracks", "Cycle Lanes with light segregation")){
osm.route.sub$qualval[a] <- qual.data$value[qual.data$scheme == "On-road segregated cycle lane"] / 100 / 60
}else if(osm.route.sub$Recommended[a] == "Cycle Lanes"){
osm.route.sub$qualval[a] <- qual.data$value[qual.data$scheme == "On-road non-segregated cycle lane"] / 100 / 60
}else if(osm.route.sub$Recommended[a] == "Segregated Cycle Track on Path"){
osm.route.sub$qualval[a] <- qual.data$value[qual.data$scheme == "Off-road segregated cycle track"] / 100 / 60
}else{
osm.route.sub$qualval[a] <- 0
}
}
#Ben = #cyclists X length cycled / 8 mph X value of time cycled X 2 for return journey * 220 days per year * 0.5 (rule of half) + same again for existing without rule of half
osm.route.sub$jouney_qual_ben <- (route.up.sub$uptake[f]) * osm.route.sub$length * 0.5 / 3.576 * osm.route.sub$qualval * 2 * 220 * 0.5 + (route.up.sub$pct.census[f]) * osm.route.sub$length * 0.5 / 3.576 * osm.route.sub$qualval * 2 * 220
route.up.sub$quality_benefit[f] <- sum(osm.route.sub$jouney_qual_ben)
}
schemes$absenteeism_benefit[e] <- sum(route.up.sub$absenteeism_benefit, na.rm = T)
schemes$health_deathavoided[e] <- sum(route.up.sub$health_deathavoided, na.rm = T)
schemes$health_benefit[e] <- sum(route.up.sub$health_benefit, na.rm = T)
schemes$quality_benefit[e] <- sum(route.up.sub$quality_benefit, na.rm = T)
rm(route.up.sub)
}
######################################################################
# Accidents
#####################################################################
# Very simple method based on average marginal cost of 1.7p per car km
schemes$accidents_benefit <- round(-1 * schemes$carkm * 1.7 / 100 ,0)
######################################################################
# GHG emissions
######################################################################
pdiesel <- 0.39 #Proportion of diesel cars https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/608374/vehicle-licensing-statistics-2016.pdf
velo <- 48 # average speed assumed to be 30 mph
schemes$co2saved <- NA
for(g in 1:nrow(schemes)){
#Fuel Consumption for petrol and desiel cars
cons.petrol <- (1.18011 / velo) + 0.04639 + (-0.00009 * velo) + (0.000003 * velo **2) * (- schemes$carkm[g] * (1 - pdiesel))
cons.diesel <- (0.51887 / velo) + 0.06556 + (-0.00062 * velo) + (0.000005 * velo **2) * (- schemes$carkm[g] * pdiesel)
#convert litres to kg co2e
emiss.petrol <- cons.petrol * 2.160
emiss.diesel <- cons.diesel * 2.556
schemes$co2saved[g] <- round(emiss.petrol + emiss.diesel,0)
rm(emiss.diesel, emiss.petrol, cons.petrol, cons.diesel)
}
#Convert to £ benefit based on WebTAG Databook A 3.3.
schemes$ghg_benefit <- round(schemes$co2saved / 1000 * 64.66,0)
########################################################################
#air quality - WebTAG being updated soon
########################################################################
#schemes$airqual_benefit <- 0
#######################################################################
#noise
#######################################################################
#schemes$noise_benefit <- 0
######################################################################
# indirect tax revenue
######################################################################
#schemes$tax_benefit <- 0
#######################################################################
# travel time (decongestion)
#######################################################################
# Cost of congestion from WebTAG Data book A5.4.4
#For yorkshire Mon-Fri Average 15.6 p / km
schemes$congestion_benefit <- round(schemes$carkm * 0.156 * -1, 2)
#################################################################
# Total Benefits
####################################################################
schemes$totalBen <- schemes$congestion_benefit + schemes$health_benefit +
schemes$jouney_qual_ben + schemes$ghg_benefit +
schemes$absenteeism_benefit
#+ schemes$airqual_benefit +
#schemes$noise_benefit + schemes$tax_benefit
schemes$costBenRatio <- round(schemes$totalBen/schemes$costTotal,1)
#foo <- as.data.frame(schemes)
#foo$geometry <- NULL
saveRDS(schemes,paste0("../cyipt-bigdata/osm-prep/",regions[b],"/schemes-simplified.Rds"))
#rm(scheme)
}
}else{
message(paste0("Input File Missing for ",regions[b]," at ",Sys.time()))
}
}
rm(b,regions)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.