Growing urban bicycle networks

Abstract

Cycling is a promising solution to unsustainable urban transport systems. However, prevailing bicycle network development follows a slow and piecewise process, without taking into account the structural complexity of transportation networks. Here we explore systematically the topological limitations of urban bicycle network development. For 62 cities we study different variations of growing a synthetic bicycle network between an arbitrary set of points routed on the urban street network. We find initially decreasing returns on investment until a critical threshold, posing fundamental consequences to sustainable urban planning: cities must invest into bicycle networks with the right growth strategy, and persistently, to surpass a critical mass. We also find pronounced overlaps of synthetically grown networks in cities with well-developed existing bicycle networks, showing that our model reflects reality. Growing networks from scratch makes our approach a generally applicable starting point for sustainable urban bicycle network planning with minimal data requirements.

Demand-driven design of bicycle infrastructure networks for improved urban bikeability

“…Existing approaches to study bike path networks rely on various types of input data and focus on different properties; for example, improving connectivity of existing bike paths 24 , purely structural network growth models 26 or standard forward percolation models based on static route choice data 25 . Compared with these more abstract percolation models, our adaptive inverse percolation framework trades computational speed for the explicit inclusion of cyclist demand.…”

“…Testing all of these networks is impossible for real-world cities in reasonable time (see Supplementary Note 2 for a more detailed description of the underlying optimization problem). Recent approaches utilize forward network percolation models to construct bike path networks 24,26 or apply percolation models to a fixed cyclist flow 25 to find efficient networks.…”

“…More sophisticated approaches to find connected network structures have recently been proposed. Such approaches, based on percolation processes [24][25][26] , optimize the connectivity of the bike path network; however, connectivity alone is not sufficient to support demand, as routes along streets equipped with bike paths would likely be indirect and require large detours. Shortest path trees would optimally support the demand only from and to a single location.…”

“…All of these potential extensions naturally require more details in the input data, such as information on traffic signals, street quality or expected driving behavior for car traffic across the street network. Recent contributions to the data-driven analysis and planning of cycling infrastructure and route choice as well as data collection methods are essential to establish a foundation of input data and ensure reliable results and predictions [24][25][26] . Although, eventually, first-hand on-site experience and detailed case-by-case modelling must determine the sensibility and feasibility of the suggested networks, with sufficiently accurate input data our framework may provide scenarios for efficient bicycle infrastructure networks, helping to guide planned extensions of existing infrastructure networks 4,25 .…”

Transporte urbano-sustentável em cidades globais inteligentes: o caso de Milão

“…A inteligência das smart cities não vem somente de sua infraestrutura tecnológica (ação do homem num dado espaço), mas também do capital intelectual necessário para solucionar problemas antigos com uma perspectiva mais sustentável baseada na tecnologia de ponta (Almeida & Engel, 2019). Essa realidade ocorre porque as cidades se tornaram mais complexas e densas, gerando, por exemplo, problemas de mobilidade e planejamento (Almeida, 2020;Szell et al, 2022). Milão, por exemplo, é uma cidade reconhecida como sendo a capital mundial da moda e do Design.…”