By Dimitrios Karamanis, Jordi Honey-Rosés, Ruth Lamas Borraz
Barcelona has taken a pioneering step in understanding how bicycles move through the city, street by street. In 2025, the first ever Bicycle Ridership Model for the city of Barcelona was developed, using a mix of automatic counters, counting campaigns, citizen science through BiciZen, and advanced machine learning (e.g. XGBoost). This model estimated the Annual Average Daily Bicycle Traffic (AADBT) for over 20,000 street segments and proved the value of community involvement and fine-scale data in shaping a more equitable, sustainable, and evidence-based urban mobility system.
But cities don’t end at their borders. Today, over 3 million people live across the Barcelona Metropolitan Area (AMB). More and more of them cycle between municipalities. While Barcelona’s ridership model is a leap forward, it only tells part of the story.
What happens when a cyclist crosses into L’Hospitalet, El Prat, or Sant Adrià de Besòs?
What do we know about ridership across the Bicivia network, the regional cycling backbone connecting 36 municipalities across the AMB?
Laying the Foundation: What the Barcelona Model Taught Us
The municipal model, developed by ICTA-UAB, UAB, BSC, and BACC, combined:
- High-frequency automatic counter data (381 sensors),
- Crowdsourced demographic and behavioral data (via BiciZen),
- Detailed street typologies (e.g., protected lanes, shared streets),
- And machine learning models to estimate ridership for every street segment.
It demonstrated that:
- Citizen science improves both data quality and coverage,
- Model accuracy improves significantly when volunteer data is included,
- And infrastructure typology strongly correlates with cycling volumes and inclusivity (e.g., fewer women on streets with poor or no infrastructure).
The next logical step? Scale it up. Regionally. Intelligently. Inclusively.
Scaling Up to the Metropolitan Area: Key Elements of Our Proposal
1. Focus on the Bicivia Network
We’ll start with the Basic and Secondary Bicivia corridors, which connect Barcelona with key adjacent municipalities. They have:
- Existing automatic counters,
- Growing daily ridership,
- Inter-municipal commuter relevance.
2. Data Compilation & Gap Identification
We’ll compile and map all available data from:
- AMB and municipal automatic counters using data from the latest campaigns,
- Regional planning documents,
- Open data platforms like IDE.AMB.cat,
- BiciZen observations (where available).
The goal is to spot ridership data gaps, especially for:
- Age and gender distribution,
- Infrastructure not yet covered by counters,
- Intersections and high-risk areas.
What Makes the Metropolitan Context Different?
While Barcelona has a dense, well-documented cycling network, the AMB presents new challenges:
- Greater variation in street types and infrastructure quality and heterogeneity,
- Data fragmentation and availability between municipalities,
- Longer, multi-municipal trips with different behavior and demographic patterns,
- Governance fragmentation, requiring coordination across 36 local governments.
- Unlike motor vehicle models, there are validation difficulties due to lack solid benchmarks for comparing predicted ridership across varied contexts.
But these challenges also offer an opportunity and motivation to build one of the first integrated, metropolitan-level active mobility model in Spain and in Europe.
Why This Matters
For cycling to thrive across the entire metropolitan region, we need better data, better models, and better collaboration. With this regional model, we aim to:
- Make infrastructure investments smarter and more inclusive, ensuring that investments in cycling benefit all residents, not just those in central areas,
- Support safety planning with real exposure data for risk analysis on regional roads,
- Empower AMB with open, interoperable tools, supporting sustainability goals of reducing car dependency and promoting active transport,
- Countering the systemic data bias that still favors motor vehicles in transportation planning,
- Showcase a replicable method for other polycentric metropolitan regions worldwide.
“If we want to build a truly bikeable metropolis, we first need to see how the region rides.”
📍 Next Steps
- Map all known ridership and infrastructure data across the AMB,
- Begin drafting a predictive model structure, based on the knowledge from Barcelona Model, that incorporates both automatic counts and observed demographic indicators.
- Identify variables of interest (e.g. physical segregation, connectivity, width)to calibrate the model.
Soon, we hope to have a prototype metropolitan ridership model. A tool that empowers planners, advocates, and researchers to better understand how people cycle across the entire region.
References
Honey-Rosés, J., L, Liebscht, G. Castilla, A. Arenas, H. Ballart, L. Chaves, M. Miah, S. Nel.lo-Deakin, G. Simón-i-Mas, C.G. Treviño, F. Udina, P. Reyes (2025) La Circulación de Bicicletas en Barcelona, calle por calle. City Lab Barcelona, Institut de Ciència i Tecnologies Ambientals (ICTA-UAB), Barcelona Supercomputing Center (BSC), Bicicleta Club de Catalunya (BACC).
