For example, sum total travel in both directions.

od_oneway(
  x,
  attrib = names(x[-c(1:2)])[vapply(x[-c(1:2)], is.numeric, TRUE)],
  id1 = names(x)[1],
  id2 = names(x)[2],
  stplanr.key = NULL
)

Arguments

x

A data frame or SpatialLinesDataFrame, representing an OD matrix

attrib

A vector of column numbers or names, representing variables to be aggregated. By default, all numeric variables are selected. aggregate

id1

Optional (it is assumed to be the first column) text string referring to the name of the variable containing the unique id of the origin

id2

Optional (it is assumed to be the second column) text string referring to the name of the variable containing the unique id of the destination

stplanr.key

Optional key of unique OD pairs regardless of the order, e.g., as generated by od_id_max_min() or od_id_szudzik()

Value

oneway outputs a data frame (or sf data frame) with rows containing results for the user-selected attribute values that have been aggregated.

Details

Flow data often contains movement in two directions: from point A to point B and then from B to A. This can be problematic for transport planning, because the magnitude of flow along a route can be masked by flows the other direction. If only the largest flow in either direction is captured in an analysis, for example, the true extent of travel will by heavily under-estimated for OD pairs which have similar amounts of travel in both directions. Flows in both direction are often represented by overlapping lines with identical geometries (see flowlines()) which can be confusing for users and are difficult to plot.

See also

Examples

(od_min <- od_data_sample[c(1, 2, 9), 1:6])
#> # A tibble: 3 × 6 #> geo_code1 geo_code2 all from_home light_rail train #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 E02002361 E02002361 109 0 0 0 #> 2 E02002361 E02002363 38 0 0 1 #> 3 E02002363 E02002361 30 0 0 0
(od_oneway <- od_oneway(od_min))
#> # A tibble: 2 × 6 #> geo_code1 geo_code2 all from_home light_rail train #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 E02002361 E02002361 109 0 0 0 #> 2 E02002361 E02002363 68 0 0 1
# (od_oneway_old = onewayid(od_min, attrib = 3:6)) # old implementation nrow(od_oneway) < nrow(od_min) # result has fewer rows
#> [1] TRUE
sum(od_min$all) == sum(od_oneway$all) # but the same total flow
#> [1] TRUE
od_oneway(od_min, attrib = "all")
#> # A tibble: 2 × 3 #> geo_code1 geo_code2 all #> <chr> <chr> <dbl> #> 1 E02002361 E02002361 109 #> 2 E02002361 E02002363 68
attrib <- which(vapply(flow, is.numeric, TRUE)) flow_oneway <- od_oneway(flow, attrib = attrib) colSums(flow_oneway[attrib]) == colSums(flow[attrib]) # test if the colSums are equal
#> All Work.mainly.at.or.from.home #> TRUE TRUE #> Underground..metro..light.rail..tram Train #> TRUE TRUE #> Bus..minibus.or.coach Taxi #> TRUE TRUE #> Motorcycle..scooter.or.moped Driving.a.car.or.van #> TRUE TRUE #> Passenger.in.a.car.or.van Bicycle #> TRUE TRUE #> On.foot Other.method.of.travel.to.work #> TRUE TRUE
# Demonstrate the results from oneway and onewaygeo are identical flow_oneway_geo <- onewaygeo(flowlines, attrib = attrib) flow_oneway_sf <- od_oneway(flowlines_sf)
#> Joining, by = c("Area.of.residence", "Area.of.workplace")
par(mfrow = c(1, 2)) plot(flow_oneway_geo, lwd = flow_oneway_geo$All / mean(flow_oneway_geo$All)) plot(flow_oneway_sf$geometry, lwd = flow_oneway_sf$All / mean(flow_oneway_sf$All))
par(mfrow = c(1, 1)) od_max_min <- od_oneway(od_min, stplanr.key = od_id_character(od_min[[1]], od_min[[2]])) cor(od_max_min$all, od_oneway$all)
#> [1] 1
# benchmark performance # bench::mark(check = FALSE, iterations = 3, # onewayid(flowlines_sf, attrib), # od_oneway(flowlines_sf) # )