README.md
In douglascm/trafficr: Traffic Assignment Methods
trafficr
douglascm
1/2/2020
Traffic Assignment Package
trafficr is a package for static traffic assignment R code. Currently,
the program can solve the static traffic assignment problem using user
equilibrium (UE) for the city network in urban traffic assignment. The
solution is achieved using the Frank-Wolfe algorithm. Package is based
on dodgr package for
all-or-nothing assignment, route and time data.
How to run traffic assignment
Following steps explain the setup and how to run the procedures and
extract flows and maps.
1. Install the R package with:
devtools::install_github("douglascm/trafficr")
Package devtools is required for the installation, install with
install.packages('devtools')
2. Data Preparation :
Import into the current environment all three required datasets:
- demand tabular data containing demand data for each origin and
destination:
demand <- fread("path to file")
- Required columns:
- orig: origin id (integer)
- dest: destination id (integer)
head(sioux_demand)
#> orig dest demand
#> 1: 0 1 100
#> 2: 0 2 100
#> 3: 0 3 500
#> 4: 0 4 200
#> 5: 0 5 300
#> 6: 0 6 500
- network sf object data containing road network:
network <- st_read("path to file")
- Required columns:
- id_link: link id (integer)
- ffs_fw: forward free flow speed in km/h (numeric)
- ffs_bw: backward free flow speed in km/h (numeric)
- cap_fw: forward link capacity in veh/h (numeric)
- cap_bw: backward link capacity in veh/h (numeric)
- Optional columns:
- addv_fw: forward additional cost (numeric)
- addv_bw: backward additional cost (numeric)
- wg_fw: forward cost weight (numeric)
- wg_bw: backward cost weight (numeric)
- alpha: alpha BPR function parameter (numeric)
- beta: beta BPR function parameter (numeric)
- cost: actual network cost, used instead of geometric distance and speed (numeric)
head(sioux_network)
#> Simple feature collection with 6 features and 12 fields
#> geometry type: LINESTRING
#> dimension: XY
#> bbox: xmin: 5 ymin: 32 xmax: 32 ymax: 51
#> epsg (SRID): 4326
#> proj4string: +proj=longlat +datum=WGS84 +no_defs
#> id_link ffs_fw ffs_bw cap_fw cap_bw addv_fw addv_bw wg_fw wg_bw alpha
#> 1 1 60 60 25900 25900 0 0 1 1 0.15
#> 2 2 60 60 23403 23403 0 0 1 1 0.15
#> 3 3 60 60 4958 4958 0 0 1 1 0.15
#> 4 4 60 60 17111 17111 0 0 1 1 0.15
#> 5 5 60 60 23403 23403 0 0 1 1 0.15
#> 6 6 60 60 17783 17783 0 0 1 1 0.15
#> beta cost geometry
#> 1 4 6 LINESTRING (5 51, 32 51)
#> 2 4 4 LINESTRING (5 51, 5 44)
#> 3 4 5 LINESTRING (32 51, 32 44)
#> 4 4 4 LINESTRING (5 44, 13 44)
#> 5 4 4 LINESTRING (5 44, 5 32)
#> 6 4 2 LINESTRING (13 44, 22 44)
- zones sf object data containing zones to be added to road network:
zones <- st_read("path to file")
- Required columns:
- id_zone: link id (integer)
- longitude: Longitude in WGS84 (numeric)
- latitude: Latitude in WGS84 (numeric)
head(sioux_zones)
#> Simple feature collection with 6 features and 3 fields
#> geometry type: POINT
#> dimension: XY
#> bbox: xmin: 5 ymin: 44 xmax: 32 ymax: 51
#> epsg (SRID): 4326
#> proj4string: +proj=longlat +datum=WGS84 +no_defs
#> id_zone x y geometry
#> 1 0 5 51 POINT (5 51)
#> 2 1 32 51 POINT (32 51)
#> 3 2 5 44 POINT (5 44)
#> 4 3 13 44 POINT (13 44)
#> 5 4 22 44 POINT (22 44)
#> 6 5 32 44 POINT (32 44)
3. Running the code :
Use the following methods to perform operations:
configure_graph() Tranforms network data from source network and zones
and outputs a list with 2 elemens, 1 being the graph and 2 being the
transformed
zones:
configure_graph(network,zones,turn_penalty,left_side,addvalue_cols,weighted,performance_measures,use_cost_col)
- network: sf object containing network data
- zones: sf object containing zone data
- turn_penalty: Adds turn penalty (see dodgr documentation on turn
penalty from function ‘R/weight-streetnet.R’)
- left_side: Does traffic travel on the left side of the road
(TRUE) or the right side (FALSE)? - only has effect on turn angle
calculations for edge times. (see dodgr documentation on turn
penalty from function ‘R/weight-streetnet.R’)
- addvalue_cols: Does network shapefile has columns ‘addv_fw’ and
‘addv_bw’ for added cost in links? (TRUE) or (FALSE)
- weighted: Does network shapefile has columns ‘wg_fw’ and ‘wg_bw’
for a cost multiplication factor in links? (TRUE) or (FALSE)
- performance_measures: Does network shapefile has columns ‘alpha’
and ‘beta’ for usage on BPR funcion? (TRUE) or (FALSE)
- use_cost_col: Use cost column as cost (opposite to speed and
distance)? (TRUE) or (FALSE)
configure_demand() Tranforms demand data from source origin and
destination to match origin and destination from graph/zone objects:
configure_demand(demand,zones)
- demand: demand data
- zones: zones object created with configure_graph()
solve_ue() Solve the traffic flow assignment model (user equilibrium)
by Frank-Wolfe algorithm (all the necessary data must be properly input
into the model in advance).
solve_ue(graph,demand,col,max_iterations)
- graph: Graph object created with configure_graph() function
- demand: Demand object created with configure_demand() function
- col: Column from demand file selected for assignment
- max_iterations: Maximum number of iterations for Frank-Wolfe
algorithm
Next is an example of the structure to run the entire code, with
additional functions that are documented inside R:
flist <- configure_graph(sioux_network,sioux_zones,use_cost_col=TRUE)
#> zone data.frame created, graph completed without errors...
graph <- flist[[1]]
zones <- flist[[2]]
demand <- configure_demand(sioux_demand,zones)
graph <- solve_ue(graph,demand,col = 'demand')
#> Number of iterations: 39
#> Time elapsed: 1.53s
times <- get_times(graph,demand)
paths <- get_paths(graph,demand)
network <- graph_to_sf(graph,sioux_network)
plot_flowmap(network,show_labels = T)
#> tmap mode set to plotting
#> Linking to GEOS 3.6.1, GDAL 2.2.3, PROJ 4.9.3
Questions
Feel free to send an email to
douglas.capelossi@gmail.com if you have
questions or concerns.
Future releases
Future releases will have an implementation of other traffic assignment
algorithms such as Gradient Projection, Origin-based assignment, and
Algorithm B.
douglascm/trafficr documentation built on March 13, 2020, 11:44 a.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.
trafficr
douglascm 1/2/2020
Traffic Assignment Package
trafficr is a package for static traffic assignment R code. Currently, the program can solve the static traffic assignment problem using user equilibrium (UE) for the city network in urban traffic assignment. The solution is achieved using the Frank-Wolfe algorithm. Package is based on dodgr package for all-or-nothing assignment, route and time data.
How to run traffic assignment
Following steps explain the setup and how to run the procedures and extract flows and maps.
1. Install the R package with:
devtools::install_github("douglascm/trafficr")
Package devtools is required for the installation, install with
install.packages('devtools')
2. Data Preparation :
Import into the current environment all three required datasets:
- demand tabular data containing demand data for each origin and destination:
demand <- fread("path to file")
- Required columns:
- orig: origin id (integer)
- dest: destination id (integer)
head(sioux_demand)
#> orig dest demand
#> 1: 0 1 100
#> 2: 0 2 100
#> 3: 0 3 500
#> 4: 0 4 200
#> 5: 0 5 300
#> 6: 0 6 500
- network sf object data containing road network:
network <- st_read("path to file")
- Required columns:
- id_link: link id (integer)
- ffs_fw: forward free flow speed in km/h (numeric)
- ffs_bw: backward free flow speed in km/h (numeric)
- cap_fw: forward link capacity in veh/h (numeric)
- cap_bw: backward link capacity in veh/h (numeric)
- Optional columns:
- addv_fw: forward additional cost (numeric)
- addv_bw: backward additional cost (numeric)
- wg_fw: forward cost weight (numeric)
- wg_bw: backward cost weight (numeric)
- alpha: alpha BPR function parameter (numeric)
- beta: beta BPR function parameter (numeric)
- cost: actual network cost, used instead of geometric distance and speed (numeric)
head(sioux_network)
#> Simple feature collection with 6 features and 12 fields
#> geometry type: LINESTRING
#> dimension: XY
#> bbox: xmin: 5 ymin: 32 xmax: 32 ymax: 51
#> epsg (SRID): 4326
#> proj4string: +proj=longlat +datum=WGS84 +no_defs
#> id_link ffs_fw ffs_bw cap_fw cap_bw addv_fw addv_bw wg_fw wg_bw alpha
#> 1 1 60 60 25900 25900 0 0 1 1 0.15
#> 2 2 60 60 23403 23403 0 0 1 1 0.15
#> 3 3 60 60 4958 4958 0 0 1 1 0.15
#> 4 4 60 60 17111 17111 0 0 1 1 0.15
#> 5 5 60 60 23403 23403 0 0 1 1 0.15
#> 6 6 60 60 17783 17783 0 0 1 1 0.15
#> beta cost geometry
#> 1 4 6 LINESTRING (5 51, 32 51)
#> 2 4 4 LINESTRING (5 51, 5 44)
#> 3 4 5 LINESTRING (32 51, 32 44)
#> 4 4 4 LINESTRING (5 44, 13 44)
#> 5 4 4 LINESTRING (5 44, 5 32)
#> 6 4 2 LINESTRING (13 44, 22 44)
- zones sf object data containing zones to be added to road network:
zones <- st_read("path to file")
- Required columns:
- id_zone: link id (integer)
- longitude: Longitude in WGS84 (numeric)
- latitude: Latitude in WGS84 (numeric)
head(sioux_zones)
#> Simple feature collection with 6 features and 3 fields
#> geometry type: POINT
#> dimension: XY
#> bbox: xmin: 5 ymin: 44 xmax: 32 ymax: 51
#> epsg (SRID): 4326
#> proj4string: +proj=longlat +datum=WGS84 +no_defs
#> id_zone x y geometry
#> 1 0 5 51 POINT (5 51)
#> 2 1 32 51 POINT (32 51)
#> 3 2 5 44 POINT (5 44)
#> 4 3 13 44 POINT (13 44)
#> 5 4 22 44 POINT (22 44)
#> 6 5 32 44 POINT (32 44)
3. Running the code :
Use the following methods to perform operations:
configure_graph() Tranforms network data from source network and zones
and outputs a list with 2 elemens, 1 being the graph and 2 being the
transformed
zones:
configure_graph(network,zones,turn_penalty,left_side,addvalue_cols,weighted,performance_measures,use_cost_col)
- network: sf object containing network data
- zones: sf object containing zone data
- turn_penalty: Adds turn penalty (see dodgr documentation on turn penalty from function ‘R/weight-streetnet.R’)
- left_side: Does traffic travel on the left side of the road (TRUE) or the right side (FALSE)? - only has effect on turn angle calculations for edge times. (see dodgr documentation on turn penalty from function ‘R/weight-streetnet.R’)
- addvalue_cols: Does network shapefile has columns ‘addv_fw’ and ‘addv_bw’ for added cost in links? (TRUE) or (FALSE)
- weighted: Does network shapefile has columns ‘wg_fw’ and ‘wg_bw’ for a cost multiplication factor in links? (TRUE) or (FALSE)
- performance_measures: Does network shapefile has columns ‘alpha’ and ‘beta’ for usage on BPR funcion? (TRUE) or (FALSE)
- use_cost_col: Use cost column as cost (opposite to speed and distance)? (TRUE) or (FALSE)
configure_demand() Tranforms demand data from source origin and
destination to match origin and destination from graph/zone objects:
configure_demand(demand,zones)
- demand: demand data
- zones: zones object created with configure_graph()
solve_ue() Solve the traffic flow assignment model (user equilibrium)
by Frank-Wolfe algorithm (all the necessary data must be properly input
into the model in advance).
solve_ue(graph,demand,col,max_iterations)
- graph: Graph object created with configure_graph() function
- demand: Demand object created with configure_demand() function
- col: Column from demand file selected for assignment
- max_iterations: Maximum number of iterations for Frank-Wolfe algorithm
Next is an example of the structure to run the entire code, with additional functions that are documented inside R:
flist <- configure_graph(sioux_network,sioux_zones,use_cost_col=TRUE)
#> zone data.frame created, graph completed without errors...
graph <- flist[[1]]
zones <- flist[[2]]
demand <- configure_demand(sioux_demand,zones)
graph <- solve_ue(graph,demand,col = 'demand')
#> Number of iterations: 39
#> Time elapsed: 1.53s
times <- get_times(graph,demand)
paths <- get_paths(graph,demand)
network <- graph_to_sf(graph,sioux_network)
plot_flowmap(network,show_labels = T)
#> tmap mode set to plotting
#> Linking to GEOS 3.6.1, GDAL 2.2.3, PROJ 4.9.3
Questions
Feel free to send an email to douglas.capelossi@gmail.com if you have questions or concerns.
Future releases
Future releases will have an implementation of other traffic assignment algorithms such as Gradient Projection, Origin-based assignment, and Algorithm B.
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.
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