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syounkin 2024-11-20 09:52:58 -06:00
commit 912e12de2d
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2
Makefile
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@ -34,7 +34,7 @@ brouter-container: ./docker/brouter/docker-compose.yml
brouter-data: brouter-data:
cd ./docker/brouter/; rm -rf ./brouter-bkup/; mv -v ./brouter/ ./brouter-bkup/; git clone https://github.com/abrensch/brouter.git cd ./docker/brouter/; rm -rf ./brouter-bkup/; mv -v ./brouter/ ./brouter-bkup/; git clone https://github.com/abrensch/brouter.git
cd ./docker/brouter/; wget -i segments.csv -P ./brouter/misc/segments4/ cd ./docker/brouter/; wget -i segments.csv -P ./brouter/misc/segments4/
cd ./docker/brouter/; cp safety.brf ./brouter/misc/profiles2/safety.brf cd ./docker/brouter/; wget https://brouter.de/brouter/profiles2/safety.brf -O ./brouter/misc/profiles2/safety.brf
cd ./docker/brouter/; rm -rf ./brouter-web-bkup/; mv -v ./brouter-web/ ./brouter-web-bkup/; git clone https://github.com/nrenner/brouter-web.git cd ./docker/brouter/; rm -rf ./brouter-web-bkup/; mv -v ./brouter-web/ ./brouter-web-bkup/; git clone https://github.com/nrenner/brouter-web.git
cd ./docker/brouter/brouter-web; cp keys.template.js keys.js; cd ./docker/brouter/brouter-web; cp keys.template.js keys.js;
cd ./docker/brouter/brouter-web; cp config.template.js config.js cd ./docker/brouter/brouter-web; cp config.template.js config.js

4
R/functions.R
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@ -1,7 +1,7 @@
getLTSForRoute <- function(i) { getLTSForRoute <- function(i, route_table) {
# Filter the routes for the current student number # Filter the routes for the current student number
current_route <- routes %>% filter(student_number == i) current_route <- route_table %>% filter(student_number == i)
# Find intersecting OBJECTIDs # Find intersecting OBJECTIDs
intersecting_ids <- relevant_buffer$OBJECTID[lengths(st_intersects(relevant_buffer, current_route)) > 0] intersecting_ids <- relevant_buffer$OBJECTID[lengths(st_intersects(relevant_buffer, current_route)) > 0]

122
cycling_route_analysis_brouter.Rmd
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@ -48,7 +48,9 @@ WI_schools <- WI_schools %>% mutate(geom = SHAPE)
```{r addresses, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE} ```{r addresses, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
addresses <- read_csv(file="data/addresses/Addresses_Students_EastHS_2024_GeocodeResults.csv") %>% addresses <- read_csv(file="data/addresses/Addresses_Students_EastHS_2024_GeocodeResults.csv") %>%
filter(lat > 0) %>% filter(lat > 0) %>%
st_as_sf(coords=c("lon","lat"), crs=4326) st_as_sf(coords=c("lon","lat"),
crs=4326,
remove = FALSE)
``` ```
(Remember that x = lon and y = lat.) (Remember that x = lon and y = lat.)
@ -84,7 +86,6 @@ options(osrm.profile = "bike")
```{r brouter, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE} ```{r brouter, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
# Set url and profile of brouter server # Set url and profile of brouter server
brouter_url <- "http://127.0.0.1:17777/brouter" brouter_url <- "http://127.0.0.1:17777/brouter"
brouter_profile <- "safety"
``` ```
## Stadia Maps API Key ## Stadia Maps API Key
@ -137,14 +138,17 @@ square grid, the total number of points will be res*res. Increase res to obtain
## Calculate Routes ## Calculate Routes
```{r routes, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE} ```{r routes, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
routes <- list(NULL)
school_focus_location <- WI_schools %>% filter(NCES_CODE %in% school_focus$NCES_CODE) %>% select(LAT, LON) school_focus_location <- WI_schools %>% filter(NCES_CODE %in% school_focus$NCES_CODE) %>% select(LAT, LON)
#calculate routes with the safety profile
brouter_profile <- "safety"
routes_safety <- list(NULL)
for(i in addresses_near %>% arrange(number) %>% pull(number)) { for(i in addresses_near %>% arrange(number) %>% pull(number)) {
query <- paste0( query <- paste0(
brouter_url, brouter_url,
"?lonlats=", "?lonlats=",
(addresses_near %>% filter(number == i) %>% pull(point) %>% str_split(., ","))[[1]][1], ",", addresses_near %>% filter(number == i) %>% pull(lon), ",",
(addresses_near %>% filter(number == i) %>% pull(point) %>% str_split(., ","))[[1]][2], "|", addresses_near %>% filter(number == i) %>% pull(lat), "|",
school_focus_location$LON, ",", school_focus_location$LAT, school_focus_location$LON, ",", school_focus_location$LAT,
"&profile=", brouter_profile, "&profile=", brouter_profile,
"&alternativeidx=0&format=geojson" "&alternativeidx=0&format=geojson"
@ -152,32 +156,57 @@ for(i in addresses_near %>% arrange(number) %>% pull(number)) {
response <- GET(query) response <- GET(query)
route_run <- st_read(content <- content(response, as = "text"), quiet = TRUE) route_run <- st_read(content <- content(response, as = "text"), quiet = TRUE)
route_run[["student_number"]] <- i route_run[["student_number"]] <- i
routes[[i]] <- route_run routes_safety[[i]] <- route_run
message(paste0("done - ", i, " of ", max(addresses_near$number))) message(paste0("done - ", i, " of ", max(addresses_near$number)))
} }
# combine the list of routes into a data table and make sure its the right crs
routes_safety <- st_transform(bind_rows(routes_safety), crs = 4326)
routes <- st_transform(bind_rows(routes), crs = 4326) # calculate routes with the "shortest" profile
brouter_profile <- "shortest"
routes_shortest <- list(NULL)
for(i in addresses_near %>% arrange(number) %>% pull(number)) {
query <- paste0(
brouter_url,
"?lonlats=",
addresses_near %>% filter(number == i) %>% pull(lon), ",",
addresses_near %>% filter(number == i) %>% pull(lat), "|",
school_focus_location$LON, ",", school_focus_location$LAT,
"&profile=", brouter_profile,
"&alternativeidx=0&format=geojson"
)
response <- GET(query)
route_run <- st_read(content <- content(response, as = "text"), quiet = TRUE)
route_run[["student_number"]] <- i
routes_shortest[[i]] <- route_run
message(paste0("done - ", i, " of ", max(addresses_near$number)))
}
# combine the list of routes into a data table and make sure its the right crs
routes_shortest <- st_transform(bind_rows(routes_shortest), crs = 4326)
routes <- bind_rows(routes_safety, routes_shortest) %>%
mutate(total.time = as.double(total.time),
total.energy = as.double(total.energy),
track.length = as.double(track.length))
``` ```
Notes: Notes:
- this queries the brouter server to get routes - this queries the brouter server to get routes
## Set boundaries and get basemap ## plot efficiency loss
```{r basemap, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE} ```{r plotefficiency, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
ggplot(data = as.data.frame(routes) %>%
bbox <- st_bbox(st_buffer(cycle_boundary_poly, dist = 500)) select(student_number, name, track.length) %>%
bbox <- c(left = as.double(bbox[1]), pivot_wider(names_from = name, values_from = track.length) %>%
bottom = as.double(bbox[2]), mutate(difference_percent = ((brouter_safety_0/brouter_shortest_0) - 1) * 100)) +
right = as.double(bbox[3]), geom_point(aes(x = brouter_shortest_0 / 1609, y = difference_percent)) +
top = as.double(bbox[4])) labs(x = "distance for shortest route",
y = "Percent difference between safest and shortest routes")
#get basemap
basemap <- get_stadiamap(bbox = bbox, zoom = 15, maptype = "stamen_toner_lite")
``` ```
Notes:
- This chunk retrieves the base map from Stadia Maps (API key required)
## Combine routes with Bike LTS ## Combine routes with Bike LTS
@ -187,9 +216,9 @@ Notes:
# The intersections have a buffer of 10 m # The intersections have a buffer of 10 m
bike_lts_buffer <- st_buffer(st_intersection(bike_lts, cycle_boundary_poly), 10) bike_lts_buffer <- st_buffer(st_intersection(bike_lts, cycle_boundary_poly), 10)
bike_lts_buffer["student_use"] <- unlist(lapply(st_intersects(bike_lts_buffer, routes), length)) bike_lts_buffer["student_use"] <- unlist(lapply(st_intersects(bike_lts_buffer, routes_safety), length))
bike_lts <- left_join(bike_lts, as.data.frame(bike_lts_buffer %>% select(OBJECTID, student_use)), by = "OBJECTID") bike_lts_studentuse <- left_join(bike_lts, as.data.frame(bike_lts_buffer) %>% select(OBJECTID, student_use), by = "OBJECTID") %>% filter(student_use > 0)
``` ```
Notes: Notes:
@ -209,6 +238,7 @@ relevant_buffer <- bike_lts_buffer %>% filter(student_use > 0)
routes_lts <- mclapply(addresses_near %>% arrange(number) %>% pull(number), routes_lts <- mclapply(addresses_near %>% arrange(number) %>% pull(number),
getLTSForRoute, getLTSForRoute,
route_table = routes_safety,
mc.cores = detectCores() / 2, mc.cores = detectCores() / 2,
mc.cleanup = TRUE, mc.cleanup = TRUE,
mc.preschedule = TRUE, mc.preschedule = TRUE,
@ -219,24 +249,12 @@ routes_lts <- bind_rows(routes_lts)
Notes: Notes:
- for each student's route, this finds which bike_lts segment it - for each student's route, this finds which bike_lts segment it
intersects with and calculates a max and an average level of traffic intersects with and calculates a max and an average level of traffic
stress (LTS). This takes a while, so a parallelized it. There's stress (LTS). This takes a while, so it runs in parallel. There's
probably a more efficient way to do this calculation. probably a more efficient way to do this calculation.
- see ./R/functions.R for defintion of getLTSForRoute() - see ./R/functions.R for definition of getLTSForRoute()
```{r addresslts, eval = runLTS, echo = FALSE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
# Make Maps # Join the route lts data with the addresses data
## Generate map with LTS data
```{r maplts, eval = runLTS, echo = FALSE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
ggmap(basemap) +
geom_sf(data = routes_lts %>% filter(student_number == 6), inherit.aes = FALSE,
aes(color = route$lts,
geometry = route$geometry),
linewidth = 2) +
scale_color_manual(values = bike_lts_scale$color, name = "Bike Level of Traffic Stress")
# Join the data with the addresses data
addresses_near <- left_join(addresses_near, addresses_near <- left_join(addresses_near,
routes_lts %>% routes_lts %>%
select(c("student_number", "lts_max", "lts_average", "lts_1_dist", "lts_2_dist", "lts_3_dist", "lts_4_dist")), select(c("student_number", "lts_max", "lts_average", "lts_1_dist", "lts_2_dist", "lts_3_dist", "lts_4_dist")),
@ -247,6 +265,23 @@ addresses_near <- left_join(addresses_near,
addresses_near <- addresses_near %>% mutate(lts_34_dist = lts_3_dist + lts_4_dist) addresses_near <- addresses_near %>% mutate(lts_34_dist = lts_3_dist + lts_4_dist)
``` ```
# Make Maps
## Set boundaries and get basemap
```{r basemap, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
bbox <- st_bbox(st_buffer(cycle_boundary_poly, dist = 500))
bbox <- c(left = as.double(bbox[1]),
bottom = as.double(bbox[2]),
right = as.double(bbox[3]),
top = as.double(bbox[4]))
#get basemap
basemap <- get_stadiamap(bbox = bbox, zoom = 15, maptype = "stamen_toner_lite")
```
Notes:
- This chunk retrieves the base map from Stadia Maps (API key required)
## Generate map of addresses ## Generate map of addresses
```{r mapaddresses, eval = TRUE, echo = FALSE, results = "show", warning = FALSE, error = TRUE, message = FALSE} ```{r mapaddresses, eval = TRUE, echo = FALSE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
@ -301,7 +336,7 @@ ggsave(file = paste0("figures/",
ggmap(basemap) + ggmap(basemap) +
labs(title = paste0("Cycling routes for students at ", labs(title = paste0("Cycling routes for students at ",
school_focus %>% pull(name)), school_focus %>% pull(name)),
subtitle = paste0("only showing routes within the ", radius, " mile cycling boundary"), subtitle = paste0("only showing the safest routes for students within the ", radius, " mile cycling boundary"),
x = NULL, x = NULL,
y = NULL, y = NULL,
color = NULL, color = NULL,
@ -316,7 +351,7 @@ ggmap(basemap) +
linewidth = 1) + linewidth = 1) +
scale_color_manual(values = "blue", name = NULL) + scale_color_manual(values = "blue", name = NULL) +
new_scale_color() + new_scale_color() +
geom_sf(data = bike_lts %>% filter(!is.na(student_use), student_use > 3), geom_sf(data = bike_lts_studentuse %>% filter(!is.na(student_use), student_use > 3),
inherit.aes = FALSE, inherit.aes = FALSE,
aes(linewidth = student_use), aes(linewidth = student_use),
color = "mediumvioletred", color = "mediumvioletred",
@ -367,7 +402,7 @@ ggmap(basemap) +
linewidth = 1) + linewidth = 1) +
scale_color_manual(values = "blue", name = NULL) + scale_color_manual(values = "blue", name = NULL) +
new_scale_color() + new_scale_color() +
geom_sf(data = bike_lts %>% filter(!is.na(student_use), student_use > 0), geom_sf(data = bike_lts_studentuse %>% filter(!is.na(student_use), student_use > 0),
inherit.aes = FALSE, inherit.aes = FALSE,
aes(color = lts, aes(color = lts,
linewidth = student_use)) + linewidth = student_use)) +
@ -407,7 +442,8 @@ ggmap(basemap) +
school_focus %>% pull(name)), school_focus %>% pull(name)),
subtitle = "only showing routes within the cycling boundary", subtitle = "only showing routes within the cycling boundary",
x = NULL, x = NULL,
y = NULL) + y = NULL,
linewidth = "Potential student cyclists") +
theme(axis.text=element_blank(), theme(axis.text=element_blank(),
axis.ticks=element_blank(), axis.ticks=element_blank(),
plot.caption = element_text(color = "grey")) + plot.caption = element_text(color = "grey")) +
@ -418,7 +454,7 @@ ggmap(basemap) +
linewidth = 1) + linewidth = 1) +
scale_color_manual(values = "blue", name = NULL) + scale_color_manual(values = "blue", name = NULL) +
new_scale_color() + new_scale_color() +
geom_sf(data = routes_lts %>% filter(route$student_use >= 4), geom_sf(data = routes_lts %>% filter(route$student_use >= 3),
inherit.aes = FALSE, inherit.aes = FALSE,
aes(geometry = route$geometry, aes(geometry = route$geometry,
color = route$lts, color = route$lts,

401
docker/brouter/safety.brf
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@ -1,401 +0,0 @@
# *** The trekking profile is for slow travel
# *** and avoiding car traffic, but still with
# *** a focus on approaching your destination
# *** efficiently.
---context:global # following code refers to global config
# Bike profile
assign validForBikes = true
# Use the following switches to change behaviour
assign allow_steps = true # %allow_steps% | Set false to disallow steps | boolean
assign allow_ferries = true # %allow_ferries% | Set false to disallow ferries | boolean
assign ignore_cycleroutes = false # %ignore_cycleroutes% | Set true for better elevation results | boolean
assign stick_to_cycleroutes = false # %stick_to_cycleroutes% | Set true to just follow cycleroutes | boolean
assign avoid_unsafe = true # %avoid_unsafe% | Set true to avoid standard highways | boolean
assign consider_noise = false # %consider_noise% | Activate to prefer a low-noise route | boolean
assign consider_river = false # %consider_river% | Activate to prefer a route along rivers, lakes, etc. | boolean
assign consider_forest = false # %consider_forest% | Activate to prefer a route in forest or parks | boolean
assign consider_town = false # %consider_town% | Activate to bypass cities / big towns as far as possible | boolean
assign consider_traffic = false # %consider_traffic% | Activate to consider traffic estimates | boolean
# Change elevation parameters
assign consider_elevation = true # %consider_elevation% | Set true to favor a route with few elevation meters | boolean
assign downhillcost = 60 # %downhillcost% | Cost for going downhill | number
assign downhillcutoff = 1.5 # %downhillcutoff% | Gradients below this value in percents are not counted. | number
assign uphillcost = 0 # %uphillcost% | Cost for going uphill | number
assign uphillcutoff = 1.5 # %uphillcutoff% | Gradients below this value in percents are not counted. | number
assign downhillcost = if consider_elevation then downhillcost else 0
assign uphillcost = if consider_elevation then uphillcost else 0
# Kinematic model parameters (travel time computation)
assign totalMass = 90 # %totalMass% | Mass (in kg) of the bike + biker, for travel time computation | number
assign maxSpeed = 45 # %maxSpeed% | Absolute maximum speed (in km/h), for travel time computation | number
assign S_C_x = 0.225 # %S_C_x% | Drag coefficient times the reference area (in m^2), for travel time computation | number
assign C_r = 0.01 # %C_r% | Rolling resistance coefficient (dimensionless), for travel time computation | number
assign bikerPower = 100 # %bikerPower% | Average power (in W) provided by the biker, for travel time computation | number
# Turn instructions settings
assign turnInstructionMode = 1 # %turnInstructionMode% | Mode for the generated turn instructions | [0=none, 1=auto-choose, 2=locus-style, 3=osmand-style, 4=comment-style, 5=gpsies-style, 6=orux-style, 7=locus-old-style]
assign turnInstructionCatchingRange = 40 # %turnInstructionCatchingRange% | Within this distance (in m) several turning instructions are combined into one and the turning angles are better approximated to the general direction | number
assign turnInstructionRoundabouts = true # %turnInstructionRoundabouts% | Set "false" to avoid generating special turning instructions for roundabouts | boolean
assign considerTurnRestrictions = true # %considerTurnRestrictions% | Set true to take turn restrictions into account | boolean
assign processUnusedTags = false # %processUnusedTags% | Set true to output unused tags in data tab | boolean
---context:way # following code refers to way-tags
# classifier constants
assign classifier_none = 1
assign classifier_ferry = 2
#
# pre-calculate some logical expressions
#
assign any_cycleroute =
if route_bicycle_icn=yes then true
else if route_bicycle_ncn=yes then true
else if route_bicycle_rcn=yes then true
else if route_bicycle_lcn=yes then true
else false
assign nodeaccessgranted =
if any_cycleroute then true
else lcn=yes
assign is_ldcr =
if ignore_cycleroutes then false
else any_cycleroute
assign isbike = or bicycle_road=yes or bicycle=yes or or bicycle=permissive bicycle=designated lcn=yes
assign ispaved = surface=paved|asphalt|concrete|paving_stones|sett
assign isunpaved = not or surface= or ispaved surface=fine_gravel|cobblestone
assign probablyGood = or ispaved and ( or isbike highway=footway ) not isunpaved
#
# this is the cost (in Meter) for a 90-degree turn
# The actual cost is calculated as turncost*cos(angle)
# (Suppressing turncost while following longdistance-cycleways
# makes them a little bit more magnetic)
#
assign turncost = if is_ldcr then 0
else if junction=roundabout then 0
else 90
#
# for any change in initialclassifier, initialcost is added once
#
assign initialclassifier =
if route=ferry then classifier_ferry
else classifier_none
#
# calculate the initial cost
# this is added to the total cost each time the costfactor
# changed
#
assign initialcost =
if ( equal initialclassifier classifier_ferry ) then 10000
else 0
#
# implicit access here just from the motorroad tag
# (implicit access rules from highway tag handled elsewhere)
#
assign defaultaccess =
if access= then not motorroad=yes
else if access=private|no then false
else true
#
# calculate logical bike access
#
assign bikeaccess =
if bicycle= then
(
if bicycle_road=yes then true
else if vehicle= then ( if highway=footway then false else defaultaccess )
else not vehicle=private|no
)
else not bicycle=private|no|dismount|use_sidepath
#
# calculate logical foot access
#
assign footaccess =
if bikeaccess then true
else if bicycle=dismount then true
else if foot= then defaultaccess
else not foot=private|no
#
# if not bike-, but foot-acess, just a moderate penalty,
# otherwise access is forbidden
#
assign accesspenalty =
if bikeaccess then 0
else if footaccess then 4
else if any_cycleroute then 15
else 10000
#
# handle one-ways. On primary roads, wrong-oneways should
# be close to forbidden, while on other ways we just add
# 4 to the costfactor (making it at least 5 - you are allowed
# to push your bike)
#
assign badoneway =
if reversedirection=yes then
if oneway:bicycle=yes then true
else if oneway= then junction=roundabout
else oneway=yes|true|1
else oneway=-1
assign onewaypenalty =
if ( badoneway ) then
(
if ( cycleway=opposite|opposite_lane|opposite_track ) then 0
else if ( cycleway:left=opposite|opposite_lane|opposite_track ) then 0
else if ( cycleway:right=opposite|opposite_lane|opposite_track ) then 0
else if ( oneway:bicycle=no ) then 0
else if ( cycleway:left:oneway=no ) then 0
else if ( cycleway:right:oneway=no ) then 0
else if ( junction=roundabout|circular ) then 60
else if ( highway=primary|primary_link ) then 50
else if ( highway=secondary|secondary_link ) then 30
else if ( highway=tertiary|tertiary_link ) then 20
else 4.0
)
else 0.0
# add estimate tags
assign traffic_penalty
switch consider_traffic
switch estimated_traffic_class= 0
switch estimated_traffic_class=1|2 0.2
switch estimated_traffic_class=3 0.4
switch estimated_traffic_class=4 0.6
switch estimated_traffic_class=5 0.8
switch estimated_traffic_class=6|7 1 99 0
assign noise_penalty
switch consider_noise
switch estimated_noise_class= 0
switch estimated_noise_class=1 0.3
switch estimated_noise_class=2 0.5
switch estimated_noise_class=3 0.8
switch estimated_noise_class=4 1.4
switch estimated_noise_class=5 1.7
switch estimated_noise_class=6 2 0 0
assign no_river_penalty
switch consider_river
switch estimated_river_class= 2
switch estimated_river_class=1 1.3
switch estimated_river_class=2 1
switch estimated_river_class=3 0.7
switch estimated_river_class=4 0.4
switch estimated_river_class=5 0.1
switch estimated_river_class=6 0 99 0
assign no_forest_penalty
switch consider_forest
switch estimated_forest_class= 1
switch estimated_forest_class=1 0.5
switch estimated_forest_class=2 0.4
switch estimated_forest_class=3 0.25
switch estimated_forest_class=4 0.15
switch estimated_forest_class=5 0.1
switch estimated_forest_class=6 0 99 0
assign town_penalty
switch consider_town
switch estimated_town_class= 0
switch estimated_town_class=1 0.5
switch estimated_town_class=2 0.9
switch estimated_town_class=3 1.2
switch estimated_town_class=4 1.3
switch estimated_town_class=5 1.4
switch estimated_town_class=6 1.6 99 0
#
# calculate the cost-factor, which is the factor
# by which the distance of a way-segment is multiplied
# to calculate the cost of that segment. The costfactor
# must be >=1 and it's supposed to be close to 1 for
# the type of way the routing profile is searching for
#
assign isresidentialorliving = or highway=residential|living_street living_street=yes
assign costfactor
#
# exclude rivers, rails etc.
#
if ( and highway= not route=ferry ) then 10000
#
# exclude motorways and proposed roads
#
else if ( highway=motorway|motorway_link ) then 10000
else if ( highway=proposed|abandoned ) then 10000
#
# all other exclusions below (access, steps, ferries,..)
# should not be deleted by the decoder, to be available
# in voice-hint-processing
#
else min 9999
add town_penalty
add no_forest_penalty
add no_river_penalty
add noise_penalty
add traffic_penalty
#
# apply oneway-and access-penalties
#
add max onewaypenalty accesspenalty
#
# steps and ferries are special. Note this is handled
# before the cycleroute-switch, to be able
# to really exlude them be setting cost to infinity
#
if ( highway=steps ) then ( if allow_steps then 40 else 10000 )
else if ( route=ferry ) then ( if allow_ferries then 5.67 else 10000 )
#
# handle long-distance cycle-routes.
#
else if ( is_ldcr ) then 1 # always treated as perfect (=1)
else
add ( if stick_to_cycleroutes then 0.5 else 0.05 ) # everything else somewhat up
#
# some other highway types
#
if ( highway=pedestrian ) then 3
else if ( highway=bridleway ) then 5
else if ( highway=cycleway ) then 1
else if ( isresidentialorliving ) then ( if isunpaved then 1.5 else 1.1 )
else if ( highway=service ) then ( if isunpaved then 1.6 else 1.3 )
#
# tracks and track-like ways are rated mainly be tracktype/grade
# But note that if no tracktype is given (mainly for road/path/footway)
# it can be o.k. if there's any other hint for quality
#
else if ( highway=track|road|path|footway ) then
(
if ( tracktype=grade1 ) then ( if probablyGood then 1.0 else 1.3 )
else if ( tracktype=grade2 ) then ( if probablyGood then 1.1 else 2.0 )
else if ( tracktype=grade3 ) then ( if probablyGood then 1.5 else 3.0 )
else if ( tracktype=grade4 ) then ( if probablyGood then 2.0 else 5.0 )
else if ( tracktype=grade5 ) then ( if probablyGood then 3.0 else 5.0 )
else ( if probablyGood then 1.0 else 5.0 )
)
#
# When avoiding unsafe ways, avoid highways without a bike hint
#
else add ( if ( and avoid_unsafe not isbike ) then 2 else 0 )
#
# actuals roads are o.k. if we have a bike hint
#
if ( highway=trunk|trunk_link ) then ( if isbike then 1.5 else 10 )
else if ( highway=primary|primary_link ) then ( if isbike then 1.2 else 3 )
else if ( highway=secondary|secondary_link ) then ( if isbike then 1.1 else 1.6 )
else if ( highway=tertiary|tertiary_link ) then ( if isbike then 1.0 else 1.4 )
else if ( highway=unclassified ) then ( if isbike then 1.0 else 1.3 )
#
# default for any other highway type not handled above
#
else 2.0
# way priorities used for voice hint generation
assign priorityclassifier =
if ( highway=motorway ) then 30
else if ( highway=motorway_link ) then 29
else if ( highway=trunk ) then 28
else if ( highway=trunk_link ) then 27
else if ( highway=primary ) then 26
else if ( highway=primary_link ) then 25
else if ( highway=secondary ) then 24
else if ( highway=secondary_link ) then 23
else if ( highway=tertiary ) then 22
else if ( highway=tertiary_link ) then 21
else if ( highway=unclassified ) then 20
else if ( isresidentialorliving ) then 6
else if ( highway=service ) then 6
else if ( highway=cycleway ) then 6
else if ( or bicycle=designated bicycle_road=yes ) then 6
else if ( highway=track ) then if tracktype=grade1 then 6 else 4
else if ( highway=bridleway|road|path|footway ) then 4
else if ( highway=steps ) then 2
else if ( highway=pedestrian ) then 2
else 0
# some more classifying bits used for voice hint generation...
assign isbadoneway = not equal onewaypenalty 0
assign isgoodoneway = if reversedirection=yes then oneway=-1
else if oneway= then junction=roundabout else oneway=yes|true|1
assign isroundabout = junction=roundabout
assign islinktype = highway=motorway_link|trunk_link|primary_link|secondary_link|tertiary_link
assign isgoodforcars = if greater priorityclassifier 6 then true
else if ( or isresidentialorliving highway=service ) then true
else if ( and highway=track tracktype=grade1 ) then true
else false
# ... encoded into a bitmask
assign classifiermask add isbadoneway
add multiply isgoodoneway 2
add multiply isroundabout 4
add multiply islinktype 8
multiply isgoodforcars 16
# include `smoothness=` tags in the response's WayTags for track analysis
assign dummyUsage = smoothness=
---context:node # following code refers to node tags
assign defaultaccess =
if ( access= ) then true # add default barrier restrictions here!
else if ( access=private|no ) then false
else true
assign bikeaccess =
if nodeaccessgranted=yes then true
else if bicycle= then
(
if vehicle= then defaultaccess
else not vehicle=private|no
)
else not bicycle=private|no|dismount
assign footaccess =
if bicycle=dismount then true
else if foot= then defaultaccess
else not foot=private|no
assign initialcost =
if bikeaccess then 0
else ( if footaccess then 100 else 1000000 )

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