Data-driven strategies for optimal bicycle network growth

Abstract

Urban transportation networks, from sidewalks and bicycle paths to streets and rail lines, provide the backbone for movement and socioeconomic life in cities. These networks can be understood as layers of a larger multiplex transport network. Because most cities are car-centric, the most developed layer is typically the street layer, while other layers can be highly disconnected. To make urban transport sustainable, cities are increasingly investing to develop their bicycle networks. However, given the usually patchy nature of the bicycle network layer, it is yet unclear how to extend it comprehensively and effectively given a limited budget. Here we develop data-driven, algorithmic network growth strategies and apply them to cities around the world, showing that small but focused investments allow to significantly increase the connectedness and directness of urban bicycle networks. We motivate the development of our algorithms with a network component analysis and with multimodal urban fingerprints that reveal different classes of cities depending on the connectedness between different network layers. We introduce two greedy algorithms to add the most critical missing links in the bicycle layer: The first algorithm connects the two largest connected components, the second algorithm connects the largest with the closest component. We show that these algorithms outmatch both a random approach and a baseline minimum investment strategy that connects the closest components ignoring size. 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.

arXiv commentMain text (12 pages, 4 figures) + Supplementary Information (5 pages, 5 figures, 1 table)

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Cited by in corpus (11)

• Multimodal urban mobility and multilayer transport networks
• Growing Urban Bicycle Networks
• Automated Detection of Missing Links in Bicycle Networks
• Data-driven micromobility network planning for demand and safety
• BikeDNA: A Tool for Bicycle Infrastructure Data & Network Assessment
• How Good Is Open Bicycle Infrastructure Data? A Countrywide Case Study of Denmark
• Robust design of bicycle infrastructure networks
• Demand-driven design of bicycle infrastructure networks for improved urban bikeability
• Active-travel modelling: a methodological approach to networks for walking and cycling commuting analysis
• Network analysis of the Danish bicycle infrastructure: Bikeability across urban-rural divides
• A global framework to estimate urban spatial cycling patterns based on crowdsourced data