Numerous studies have underscored the undeniable benefits of active transport, revealing its potential to not only reduce greenhouse gas emissions but also foster public health and elevate the overall quality of urban life. However, it is vital to acknowledge that the current research landscape can do much more to contribute to a truly sustainable and equitable transition.
Significance of Active Transport
Active transport circumscribes activities like cycling, walking, skating, and similar. Unlike motorized transport, these forms of transportation are a sustainable and eco-friendly option. By opting for cycling and walking over cars for short-distance trips, individuals can reduce their carbon footprint.
Also, engaging in active transportation has been linked to a range of positive health outcomes for individuals. It improves cardiovascular fitness, reduces the risk of chronic diseases like obesity, diabetes, and heart disease, and enhances mental well-being. Additionally, it decreases air and noise pollution, providing benefits to society as a whole.
Challenges in Mobility Transition
Despite the many benefits of cycling and active transport, there are significant barriers to widespread adoption. Among them, inadequate infrastructure and safety concerns are consistently cited in surveys. However, cities that want to redistribute public space between different road users often encounter bitter conflicts. Such discussions have shaped the political picture in many metropolises. Copenhagen went through some serious debates back in the 1970s when expanding bicycle infrastructure. Currently, in Paris, opinions diverge largely on the rapid expansion of bicycle lanes. In Berlin, transportation was an important issue in the last election.
The Contribution of Research
Research can contribute to these discussions by providing information to enable data-based decision-making. There is currently a significant gap in data on cycling volume. In Berlin, for example, bicycles are counted at only 22 permanent and 18 temporary locations – despite the city’s extensive network of over 5,300 km of roads. More detailed data on cycling activity would help policymakers prioritize infrastructure improvements where they are most needed. One promising solution is to use existing data and machine learning algorithms to estimate street-level cycling volumes. Instead of relying solely on costly counting stations, cycling activity can be extrapolated using various available data sources, such as crowdsourced information from sports apps (e.g. Strava), weather data, and infrastructure indicators. Within CATALYSE we are pursuing this idea.
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