abstr (version 0.4.2)

ab_scenario: Generate A/B Street Scenario objects by disaggregating aggregate OD data

Description

This function takes a data frame representing origin-destination trip data in 'long' form, zones and, optionally, buildings from where trips can start and end as inputs.

Usage

ab_scenario(
  od,
  zones,
  zones_d = NULL,
  origin_buildings = NULL,
  destination_buildings = NULL,
  pop_var = 3,
  time_fun = ab_time_normal,
  output = "sf",
  modes = c("Walk", "Bike", "Transit", "Drive"),
  ...
)

Value

An sf object by default representing individual trips between randomly selected points (or buildings when available) between the zones represented in the OD data.

Arguments

od

Origin destination data with the first 2 columns containing zone code of origin and zone code of destination. Subsequent columns should be mode names such as All and Walk, Bike, Transit, Drive, representing the number of trips made by each mode of transport for use in A/B Street.

zones

Zones with IDs that match the desire lines. Class: sf.

zones_d

Optional destination zones with IDs that match the second column of the od data frame (work in progress)

origin_buildings

Polygons where trips will originate (sf object)

destination_buildings

Polygons where trips can end, represented as sf object

pop_var

The variable containing the total population of each desire line.

time_fun

The function used to calculate departure times. ab_time_normal() by default.

output

Which output format? "sf" (default) and "json_list" return R objects. A file name such as "baseline.json" will save the resulting scenario to a file.

modes

Character string containing the names of the modes of travel to include in the outputs. These must match column names in the od input data frame. The default is c("Walk", "Bike", "Drive", "Transit"), matching the mode names allowed in the A/B Street scenario schema.

...

Additional arguments to pass to ab_json()

Examples

od = leeds_od
zones = leeds_zones
od[[1]] = c("E02006876")
ablines = ab_scenario(od, zones = zones)
plot(ablines)
table(ablines$mode)
colSums(od[3:7]) # 0.17 vs 0.05 for ab_scenario
ablines = ab_scenario(od, zones = zones, origin_buildings = leeds_buildings)
plot(leeds_zones$geometry)
plot(leeds_buildings$geometry, add = TRUE)
plot(ablines["mode"], add = TRUE)
ablines_json = ab_json(ablines, scenario_name = "test")
od = leeds_desire_lines
names(od)[4:6] = c("Walk", "Bike", "Drive")
ablines = ab_scenario(
  od = od,
  zones = leeds_site_area,
  zones_d = leeds_zones,
  origin_buildings = leeds_houses,
  destination_buildings = leeds_buildings,
  output = "sf"
)
plot(ablines)
plot(ablines$geometry)
plot(leeds_site_area$geometry, add = TRUE)
plot(leeds_buildings$geometry, add = TRUE)