Leverage data to solve an equation with multiple unknowns

Fostering cycling as a forward looking urban mobility solution is a given today, when looking at the wide range of associated positive externalities, from air quality improvement and decrease in congestion to public space appeasement, also including public health. To contribute to a wider bicycle usage development, a broad range of measures can be introduced, but their impact is necessarily conditioned by their relevance with regards to practices and users’ needs. Hence, to properly and impactfuly foster bicycle usage, it is key to understand it by precisely identifying and qualifying the wide diversity of practices. Solving the bicycle challenge first requires understanding its ins and outs.

As a consequence, rolling out relevant mobility policies requires a detailed understanding of practices, at the local level, given mobility questions are deeply related to their urban context. To that end, local public authorities today have access to data related to bicycle use on their territory, through the Enquêtes Ménages Déplacements (EMD) surveys (in France), but also thanks to cycling flow studies. However, even if EMD surveys do offer reference data collected and handled according to a standardised approach, those only happen once every decade or so. With cycling practices unfolding rapidly and evolving continuously, real-time data is key to shape public policies that are closest to actual user practices.

Even if counting surveys provide accurate and spatialised data, those only provide data points regarding flow volumes on specific axes, without providing any indication beyond those measures, therefore leaving actual user behaviours widely unexplored.

Parking as a highly relevant alternative to understand bicycle practices

Localised, detailed and regularly updated data regarding origins and destinations of bicycle trips could be a precious material for cities aiming at promoting sustainable mobility across their territory. Given city authorities do not have full access to the data required to develop the most innovative and relevant bicycle policy, it is key not only to create this data, but also to make it available.

To that end, parking appears to be a highly interesting starting point to understand practices, even though it remains a largely unexplored field among the bicycle data realm. Existing data currently focuses on infrastructure: if we have a good understanding of the supply, we are clueless when it comes to demand and parking practices. It is all the more surprising that understanding individual bicycle users' parking habits could not only shed light on origins and destinations of their trips, but also on their timing and length. Looking at moments when bicycles are not moving would thus allow us to define, in an indirect way, bicycle practice typologies, therefore providing a way to properly understand those practices, the first necessary step to roll out relevant measures.

Bicycle data at the heart of local mobility policies

Leveraging parking data to provide an answer to the need for bicycle usage data appears to be a formidable opportunity to explore. This would, by extension, provide additional data points to master a solid understanding of urban dynamics and of our bicycle users’ habits related to the city, and contribute to shaping its territory. Improving bicycle users’ experience must therefore come with a wider approach to integrate them in full coherence with regards to other urban space practices. Collecting information to better understand behaviours and needs of bicycle users leveraging parking data constitutes a first necessary step, the obvious next one being to leverage those data to develop relevant urban mobility policies, or in other words, understanding practices to have cycling contribute to shaping the city.