model_pcycle_pct_2020: Model cycling levels as a function of explanatory variables

In pct: Propensity to Cycle Tool

Model cycling levels as a function of explanatory variables

Description

Model cycling levels as a function of explanatory variables

Usage

model_pcycle_pct_2020(pcycle, distance, gradient, weights)

Arguments

pcycle	The proportion of trips by bike, e.g. 0.1, meaning 10%
distance	Vector distance numeric values of routes in km (switches to km if more than 100).
gradient	Vector gradient numeric values of routes.
weights	The weights used in the model, typically the total number of people per OD pair

Examples

# l = get_pct_lines(region = "isle-of-wight")
# l = get_pct_lines(region = "cambridgeshire")
l = wight_lines_pct
pcycle = l$bicycle / l$all
pcycle_dutch = l$dutch_slc / l$all
m1 = model_pcycle_pct_2020(
  pcycle,
  distance = l$rf_dist_km,
  gradient = l$rf_avslope_perc - 0.78,
  weights = l$all
  )
m2 = model_pcycle_pct_2020(
  pcycle_dutch, distance = l$rf_dist_km,
  gradient = l$rf_avslope_perc - 0.78,
  weights = l$all
)
m3 = model_pcycle_pct_2020(
  pcycle_dutch, distance = l$rf_dist_km,
  gradient = l$rf_avslope_perc - 0.78,
  weights = rep(1, nrow(l))
)
m1
plot(l$rf_dist_km, pcycle, cex = l$all / 100, ylim = c(0, 0.5))
points(l$rf_dist_km, m1$fitted.values, col = "red")
points(l$rf_dist_km, m2$fitted.values, col = "blue")
points(l$rf_dist_km, pcycle_dutch, col = "green")
cor(l$dutch_slc, m2$fitted.values * l$all)^2 # 95% captured
# identical means:
mean(l$dutch_slc)
mean(m2$fitted.values * l$all)
pct_coefficients_2020 = c(
  alpha = -4.018 + 2.550,
  d1 = -0.6369 -0.08036,
  d2 = 1.988,
  d3 = 0.008775,
  h1 = -0.2555,
  i1 = 0.02006,
  i2 = -0.1234
)
pct_coefficients_2020
m2$coef
plot(pct_coefficients_2020, m2$coeff)
cor(pct_coefficients_2020, m2$coeff)^2
cor(pct_coefficients_2020, m3$coeff)^2 # explains 95%+ variability in params

pct documentation built on May 31, 2023, 7:55 p.m.