Context


Current approach


These include:


PT/cycling integration in context


What is 'bike & ride'?


Access to stations in Seville


Bike & Ride catchment areas

Data & Methods


Data input

Needs origin-destination (OD) data, available from many places (in roughly descending order of quality):

  • Each of these has advantages and disadvantages.

Local input data

Two main data sources that can be used to model OD-level travel in Seville: official data on population counts.


Creation of 'flowlines'


Route allocation

These can be allocated to the transport network as follows:

routes = stplanr::line2route(l = l, route_fun = route_graphhopper, vehicle = "bike")


Route network generation

The final stage is to aggregate the values of overlapping lines (Lovelace et al., 2017):

rnet = overline(routes, "potential")


Estimates of Bike & Ride potential

library(tidyverse)
readr::read_csv("data/results.csv") %>% 
  slice(c(1:3, 9)) %>% 
  rename(`Trips to center (all modes)` = `Trips to the city center` ) %>% 
  knitr::kable(caption = "Bike & Ride potential for selected stations (trips/day): long, medium and short term.", format = "html")

Discussion

Next step: invest in cycling to stations (cycle paths, cycle parking, ...)

Image: 3000 space cycle parking facility in Cambridge (£2.5 million)


References and acknowledgements

Thanks for listening

References



Robinlovelace/pctSeville documentation built on May 9, 2019, 10:30 a.m.