Designing efficient bike path networks requires balancing multiple opposing constraints such as cost and safety. An adaptive demand-driven inverse percolation approach is proposed to generate efficient network structures by explicitly taking into account the demands of cyclists and their route choice behavior based on safety preferences.
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References
Natera Orozco, L. G., Battiston, F. & Iñiguez, G. & Szell, M. Data-driven strategies for optimal bicycle network growth. R. Soc. Open Sci. 7, 201130 (2020). This paper reports a percolation approach to connect the fragmented bike path network of a city.
Olmos, L. E. et al. A data science framework for planning the growth of bicycle infrastructures. Transp. Res. C 115, 102640 (2020). This paper reports a data-driven demand-based static approach used to identify which street segments should be equipped with bike lanes.
Verma, T. et al. Emergence of core–peripheries in networks. Nat. Commun. 7, 10441 (2016). This paper employs inverse percolation to model the core–periphery structure of airline networks.
Broach, J., Dill, J. & Gliebe, J. Where do cyclists ride? A route choice model developed with revealed preference GPS data. Transp. Res. A 46, 1730–1740 (2012). This paper reports a cyclist route choice model that provides accepted detours to avoid safety risks and inconveniences.
Folco, P., Gauvin, L., Tizzoni, M. & Szell, M. Data-driven bicycle network planning for demand and safety. Preprint at https://arxiv.org/abs/2203.14619 (2022). This preprint article reports a network planning approach including empirical accident risk.
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This is a summary of: Steinacker, C. et al. Demand-driven design of bicycle infrastructure networks for improved urban bikeability. Nat. Comput. Sci. https://doi.org/10.1038/s43588-022-00318-w (2022).
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Designing efficient urban bike path networks that meet the needs of cyclists. Nat Comput Sci 2, 630–631 (2022). https://doi.org/10.1038/s43588-022-00324-y
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DOI: https://doi.org/10.1038/s43588-022-00324-y
