Focus on cycle paths around central area has a number of negative consequences (see below)
Working hypothesis: these can be tackled by 'Bike &
Ride' systems
Cycling tends not to replace cars in the city centre, which has a number of knock-on impacts:
Emphasis on wealthy centres can lead to political 'bikelashes'
Needs origin-destination (OD) data, available from many places (in roughly descending order of quality):
- Each of these has advantages and disadvantages.
Two main data sources that can be used to model OD-level travel in Seville: official data on population counts.
These can be allocated to the transport network as follows:
routes = stplanr::line2route(l = l, route_fun = route_graphhopper, vehicle = "bike")
The final stage is to aggregate the values of overlapping lines (Lovelace et al., 2017):
rnet = overline(routes, "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")
Next step: invest in cycling to stations (cycle paths, cycle parking, ...)
Image: 3000 space cycle parking facility in Cambridge (£2.5 million)
Thanks for listening
Lovelace, R., Goodman, A., Aldred, R., Berkoff, N., Abbas, A., Woodcock, J., 2017. The Propensity to Cycle Tool: An open source online system for sustainable transport planning. Journal of Transport and Land Use 10. doi:10.5198/jtlu.2016.862
Marques et al (under review). ANALYZING THE POTENTIAL OF ‘BIKE & RIDE’ TO PRIORITISE INVESTMENT IN SUBURBAN CYCLING AND PUBLIC TRANSPORT INFRASTRUCTURE: A CASE STUDY OF SEVILLE
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