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Strategic cycle network planning tools, evidence and reproducible code

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# biclaR

**biclaR** is a tool for the design and assessment of different
scenarios of the cycling network models in the Lisbon metropolitan area
(LMA). The web application can be found at
[u-shift.github.io/biclarwww](https://u-shift.github.io/biclarwww/).

**biclaR** is an open source project, see the source code on
[github.com/U-Shift/biclar](https://github.com/U-Shift/biclar).

For a description of the methods and research underlying the project,
see the
[paper](https://www.sciencedirect.com/science/article/pii/S0198971524001595)
“Reproducible methods for modeling combined public transport and cycling
trips and associated benefits: Evidence from the biclaR tool” published
in *Computers, Environment and Urban Systems* (Félix, Moura, and
Lovelace 2025), and the methodological report \[PT\] on the [TML
website](https://www.tmlmobilidade.pt/projetos-e-atividades/planos-e-estudos/rede-ciclavel-metropolitana-estudo-modelacao-e-ferramenta-de-apoio-ao-planeamento-e-decisao/).

# Input data

The key datasets are as follows:

- Trips dataset with Origin and Destination, at *Freguesia* level,
  disaggregated by transport mode, from Instituto National de
  Estatística (2018)  
- [CAOP
  2020](https://www.dgterritorio.gov.pt/cartografia/cartografia-tematica/caop?language=en) -
  Official limits of Portuguese areas.
- Road network from
  [OpenStreetMap](https://www.openstreetmap.org/#map=11/38.7053/-9.1585)  
- Main public transport interfaces at Lisbon Metropolitan Area, provided
  by [Transportes Metropolitanos de
  Lisboa](https://www.tmlmobilidade.pt/)

# Cenarios for cycling uptake

## Baseline

The baseline scenario makes use of the 2018 mobility survey data in
LMA.  
We considered all trips between *Freguesias*.

See vignette [baseline scenario](articles/0_baseline_scenario.html) to
see how this was modeled.

## ENMAC targets

The National targets for cycling uptake were set to:

- 4% of all trips should be made by bicycle by 2025
- 10% of all trips should be made by bicycle by 2030

Cycling trips should replace car trips directly.

See vignette [ENMAC scenario](articles/1_emnac_scenario.html) to see how
this was modeled.

## Intermodal trips

See vignette [Intermodal scenario](articles/2_intermodal_scenario.html)
to see how this was modeled.

## E-bikes investment policy

See vignette [E-bike scenario](articles/3_ebikes_scenario.html) to see
how this was modeled.

# Methods

## PCT - Propensity to Cycle Tool

`biclar` uses the methods developed in [PCT.bike](https://pct.bike)
(Lovelace et al. 2017) for cycling uptake estimation and data
visualization.

## Jittering

For the disagregation of OD pairs at *Freguesias* level, we use [OD
Jittering](https://github.com/atumworld/odrust) (Lovelace, Félix, and
Carlino 2022) method, which better suits walking and cycling trips
modelling (shorter distances), instead of relying on centroids that
concentrate all the trips between areas.

The OD datasets, before and after jittering, are shown below.

## Cycling routes

Use of [CyclingStreets.net](https://cyclinstreets.net) ([R
package](https://rpackage.cyclestreets.net/)) for fast and quiet bike
routes for baseline scenario.

For e-bike scenario, we developed a proper algorithm, considering the
topography, and using [`slopes`](https://docs.ropensci.org/slopes)
package.

## Intermodal trips

We made use and developed a [methodology](https://github.com/npct/rail)
that considers replacing long trips by bike + train or ferry trips.

## Estimation of socioeconomic benefits

Health Economic Assessment Tool ([HEAT
v5.0](https://www.heatwalkingcycling.org/#how_heat_works)) for walking
and cycling by WHO.

# Results

## Cycling uptake in LMA and by Municipality

#### ENMAC Scenario up to 5km

See [here](https://u-shift.github.io/biclarwww/aml/mapa_cenario1.html)
for full map.

See [here](https://u-shift.github.io/biclarwww/) for results for each
Municipality.

#### ENMAC Scenario up to 10km (E-bike)

See [here](https://u-shift.github.io/biclarwww/aml/mapa_cenario2.html)
for full map.

#### Intermodality Scenario

See [here](https://u-shift.github.io/biclarwww/aml/mapa_cenario3.html)
for full map.

## Comparision with the cycling network plans by Municipality

Compare the modeled cycling networks (segments overlapping) with
expansion plans, by municipality.

We can view it in an [interactive map
here](https://ushift.tecnico.ulisboa.pt/content/tml/RedeExistentePrevista.html).

# Funding

This project is funded by [TML - Transportes Metropolitanos de
Lisboa](https://www.tmlmobilidade.pt/projetos-e-atividades/planos-e-estudos/rede-ciclavel-metropolitana-estudo-modelacao-e-ferramenta-de-apoio-ao-planeamento-e-decisao/).

# References

Félix, Rosa, Filipe Moura, and Robin Lovelace. 2025. “Reproducible
Methods for Modeling Combined Public Transport and Cycling Trips and
Associated Benefits: Evidence from the
<span class="nocase">biclaR</span> Tool.” *Computers, Environment and
Urban Systems* 117 (April): 102230.
<https://doi.org/10.1016/j.compenvurbsys.2024.102230>.

</div>

Instituto National de Estatística. 2018. “Mobilidade e Funcionalidade Do
Território Nas Áreas Metropolitanas Do Porto e de Lisboa: 2017.” Lisboa.
<https://www.ine.pt/xportal/xmain?xpid=INE&xpgid=ine_publicacoes&PUBLICACOESpub_boui=349495406&PUBLICACOESmodo=2&xlang=pt>.

</div>

Lovelace, Robin, Rosa Félix, and Dustin Carlino. 2022. “Jittering: A
Computationally Efficient Method for Generating Realistic Route Networks
from Origin-Destination Data.” *Findings*.
<https://doi.org/10.32866/001c.33873>.

</div>

Lovelace, Robin, Anna Goodman, Rachel Aldred, Nikolai Berkoff, Ali
Abbas, and James Woodcock. 2017. “The Propensity to Cycle Tool: An Open
Source Online System for Sustainable Transport Planning.” *Journal of
Transport and Land Use* 10 (1). <https://doi.org/gfgzf7>.

</div>

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