In luukvdmeer/dockless: Spatio-Temporal Forecasts for Bike Availability in Dockless Bike Sharing Systems

knitr::opts_chunk$set(echo = TRUE)

The dockless package

The dockless package contains the code for my master thesis Spatiotemporal Forecasts for Bike Availability in Dockless Bike Sharing Systems. The package can be installed as follows, given that you have devtools installed in advance.

devtools::install_github('luukvdmeer/dockless')

The analysis can be reproduced with the following code:

require(dockless)
require(sf)

## ----------------------- CLUSTER LOOP --------------------------

# Create grid
gridcells = dockless::create_grid(
  area = systemarea,
  cellsize = c(500, 500)
)

# Calculate grid cell centroids
gridcentroids = gridcells %>%
  dockless::project_sf() %>%
  sf::st_centroid() %>%
  sf::st_transform(crs = 4326)

# Usage intensity per grid cell
gridcells$intensity = dockless::usage_intensity(
  usage = usagedata_train,
  grid = gridcells
)

# Add intensity information to grid cell centroids
gridcentroids$intensity = gridcells$intensity

# Cluster
clusters = dockless::spatial_cluster(
  data = distancedata_centroids,
  grid = gridcells,
  area = systemarea,
  K = c(3:10),
  omega = seq(0, 1, 0.1)
)


# Add cluster information to grid cells and grid cell centroids
gridcells$cluster     = clusters$indices
gridcentroids$cluster = clusters$indices

# Create model points
modelpoints = dockless::create_modelpoints(
  centroids = gridcentroids
)

## ------------------------ MODEL LOOP ---------------------------

# Build models
models = dockless::build_models(
  data = distancedata_modelpoints,
  auto_seasonality = TRUE,
  seasons = list(NULL, 96, 672, c(96, 672))
)

## ---------------------- FORECAST LOOP --------------------------

# Forecast test points with DBAFS and NFS
forecasts_dbafs = dockless::forecast_multiple(
  data = distancedata_testpoints,
  method = 'DBAFS',
  points = testpoints,
  models = models
)

forecasts_nfs = dockless::forecast_multiple(
  data = distancedata_testpoints,
  method = 'NFS',
  points = testpoints
)

# Calculate RMSE's
errors_dbafs = dockless::evaluate(
  forecasts_dbafs,
  type = 'RMSE',
  clusters = testpoints$cluster
)

errors_nfs   = dockless::evaluate(
  forecasts_nfs,
  type = 'RMSE',
  clusters = testpoints$cluster
)

Furthermore, reproducible scripts for all tables and figures in chapter 5 can be found through in the scripts folder

luukvdmeer/dockless documentation built on May 10, 2019, 1:24 p.m.