In the quest to make cities greener and more livable, urban planners are increasingly turning to bicycling as a sustainable transportation solution. However, expanding cycling networks is a complex puzzle, balancing ridership, safety, and efficiency. A new study, led by J. Diogo Pinto of NOVA Information Management School at Universidade NOVA de Lisboa, Portugal, offers a comprehensive review of methods to prioritize cycling infrastructure investments, shedding light on how cities can optimize their networks for maximum impact.
Published in the journal *Transportation Research Interdisciplinary Perspectives* (which translates to *Transportation Research: Interdisciplinary Perspectives* in English), the study systematically reviews 60 papers to identify the most effective strategies for selecting and prioritizing cycling infrastructure investments. Pinto and his team focused on optimal Origin–Destination (O–D) pair identification and investment prioritization, highlighting the need to balance diverse objectives and stakeholder interests.
“Expanding urban cycling networks is a multifaceted challenge,” Pinto explains. “It’s not just about building more bike lanes; it’s about strategically placing them to maximize ridership, minimize travel effort, and ensure safety.” The study reveals that achieving this balance involves a range of methodological strategies, from Geographic Information Systems (GIS)-based prioritization to advanced optimization and heuristic techniques that explore large sets of candidate solutions.
One of the key findings is the potential of machine learning techniques, such as Genetic Algorithms and Reinforcement Learning, to manage the complexity of these problems. These techniques help decision-makers navigate the vast number of possible solutions, but the study also points to untapped potential in applying Graph Representation Learning and clustering techniques. These methods could reduce computational complexity and extract valuable O–D pairs from network and crowd-sourced data.
The study also emphasizes the importance of predicting the evolution of key indicators during plan implementation and assessing the impact of cycling networks on motorized traffic. These insights can provide decision-makers with the tools they need to make informed choices that benefit both cyclists and the broader community.
For the energy sector, the implications are significant. As cities invest in cycling infrastructure, they reduce reliance on fossil fuels and lower carbon emissions. This shift not only supports sustainability goals but also opens up new commercial opportunities for energy providers. For instance, the growing popularity of e-bikes, which are mentioned in the study, creates a demand for innovative charging solutions and energy-efficient technologies.
Pinto’s research provides a foundation for future developments in urban cycling network planning. By leveraging advanced technologies and data-driven approaches, cities can create more effective and efficient cycling networks that support sustainable urban mobility. As Pinto puts it, “The future of urban transportation lies in our ability to integrate diverse data sources and advanced analytics to create networks that are not only efficient but also responsive to the needs of all stakeholders.”
In the end, this study is a call to action for urban planners, policymakers, and energy providers to collaborate and innovate. By doing so, they can pave the way for a greener, healthier, and more sustainable future.