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实现最优自行车网络增长的数据驱动策略。

Data-driven strategies for optimal bicycle network growth.

作者信息

Natera Orozco Luis Guillermo, Battiston Federico, Iñiguez Gerardo, Szell Michael

机构信息

Department of Network and Data Science, Central European University, 1100 Vienna, Austria.

Department of Computer Science, Aalto University School of Science, 00076 Aalto, Finland.

出版信息

R Soc Open Sci. 2020 Dec 16;7(12):201130. doi: 10.1098/rsos.201130. eCollection 2020 Dec.

DOI:10.1098/rsos.201130
PMID:33489269
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7813224/
Abstract

Urban transportation networks, from pavements and bicycle paths to streets and railways, provide the backbone for movement and socioeconomic life in cities. To make urban transport sustainable, cities are increasingly investing to develop their bicycle networks. However, it is yet unclear how to extend them comprehensively and effectively given a limited budget. Here we investigate the structure of bicycle networks in cities around the world, and find that they consist of hundreds of disconnected patches, even in cycling-friendly cities like Copenhagen. To connect these patches, we develop and apply data-driven, algorithmic network growth strategies, showing that small but focused investments allow to significantly increase the connectedness and directness of urban bicycle networks. We introduce two greedy algorithms to add the most critical missing links in the bicycle network focusing on connectedness, and show that they outmatch both a random approach and a baseline minimum investment strategy. Our computational approach outlines novel pathways from car-centric towards sustainable cities by taking advantage of urban data available on a city-wide scale. It is a first step towards a quantitative consolidation of bicycle infrastructure development that can become valuable for urban planners and stakeholders.

摘要

城市交通网络,从人行道、自行车道到街道和铁路,构成了城市中人员流动和社会经济生活的主干。为了使城市交通具有可持续性,城市正越来越多地投资建设其自行车网络。然而,在预算有限的情况下,如何全面而有效地扩展这些网络尚不清楚。在此,我们研究了世界各地城市自行车网络的结构,发现即使在像哥本哈根这样对骑行友好的城市,它们也由数百个互不相连的区域组成。为了连接这些区域,我们开发并应用了数据驱动的算法网络增长策略,结果表明,小规模但目标明确的投资能够显著提高城市自行车网络的连通性和直达性。我们引入了两种贪心算法,以增加自行车网络中最关键的缺失路段,重点关注连通性,并表明它们优于随机方法和基线最小投资策略。我们的计算方法通过利用全市范围内可用的城市数据,勾勒出了从以汽车为中心向可持续城市发展的新途径。这是迈向自行车基础设施发展定量整合的第一步,对城市规划者和利益相关者可能具有重要价值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de48/7813224/51f2400981c8/rsos201130-g3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de48/7813224/34f902a4d8c5/rsos201130-g1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de48/7813224/0106599c6cd6/rsos201130-g2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de48/7813224/51f2400981c8/rsos201130-g3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de48/7813224/34f902a4d8c5/rsos201130-g1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de48/7813224/0106599c6cd6/rsos201130-g2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de48/7813224/51f2400981c8/rsos201130-g3.jpg

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