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

Christoph, Steinacker,; Storch, David-Maximilian; Timme, Marc; Schröder, Malte

doi:10.1038/s43588-022-00318-w

Cited by 9 publications

(4 citation statements)

References 40 publications

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“…This advancement would serve as a valuable complement to the current static and coarse-grained mapping of world populations 56,57 . By providing a more nuanced understanding of urban mobility patterns, DeepMobility will be instrumental in supporting the development of sustainable and livable cities worldwide [58][59][60] .…”

Section: Discussionmentioning

confidence: 99%

Learning the complexity of urban mobility with deep generative collaboration network

Li,

Yuan,

Ding

et al. 2023

Preprint

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City-scale individual movements, resulting population flows, and urban morphology intricately intertwine, collectively contributing to the complexity of urban mobility, impacting critical aspects of a city, including socioeconomic exchanges and epidemic transmission. Existing models, derived from the fundamental laws governing human mobility, often capture only partial facets of this complexity. This paper introduces DeepMobility, a powerful deep generative collaboration network to bridge the heterogeneous behaviors of individuals and collective behaviors emerging from the entire population via constructing a unified model that encapsulates the multifaceted nature of complex urban mobility. Our experiments, conducted on mobility trajectories and flows in cities of China and Senegal, reveal that, in contrast to state-of-the-art deep learning models that simply “memorize” observed data, DeepMobility excels in learning the intricate data distribution and successfully reproduces the existing universal scaling laws that characterize human mobility behaviors at both the individual and population levels. DeepMobility also exhibits robust generalization capabilities, enabling it to generate realistic trajectories and flows for cities lacking corresponding training data. Our approach underscores the feasibility of employing generative deep learning to model the underlying mechanism of human mobility, and establishes an effective generative machine learning framework to capture the complexity of urban mobility comprehensively.

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Section: Discussionmentioning

confidence: 99%

Learning the complexity of urban mobility with deep generative collaboration network

Li,

Yuan,

Ding

et al. 2023

Preprint

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show abstract

“…Note that the graph considered in this work shares some similarities with the one presented in [15], in which a preference graph is considered. There, the weight of each edge depends on a combination of various factors, which however are assumed to be known or measurable.…”

Section: B Statement Of Contributionmentioning

confidence: 99%

“…Note that the barycenters are computed by taking into account both the weights and their probability, that is, for a given cluster P c ⊂ P t , its barycenter is b c = ∑ p∈P c α P t p p. The collection of weights is thus reduced from P t to B, and B is employed to initialize the reduced collection of weights P * (line 12). In the final part of the algorithm, taking into account Proposition 2.6 and the need for a combinatorial search in the neighborhood of the weights, we try to refine each member of P * through a local search (For loop at lines [13][14][15][16][17]. For each p ℓ ∈ P * a search radius ρ is initialized with the radius of the corresponding cluster (line 14).…”

Section: Algorithmmentioning

confidence: 99%

Efficient solution algorithms for the Bounded Acceleration Shortest Path problem

Ardizzoni

Consolini

Laurini

et al. 2021

2021 60th IEEE Conference on Decision and Control (CDC)

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The behavior of cyclists when choosing the path to follow along a road network is not uniform. Some of them are mostly interested in minimizing the travelled distance, but some others may also take into account other features such as safety of the roads or pollution. Individuating the different groups of users, estimating the numerical consistency of each of these groups, and reporting the weights assigned by each group to different characteristics of the road network, is quite relevant. Indeed, when decision makers need to assign some budget for infrastructural interventions, they need to know the impact of their decisions, and this is strictly related to the way users perceive different features of the road network. In this paper, we propose an optimization approach to detect the weights assigned to different road features by various user groups, leveraging knowledge of the true paths followed by them, accessible, for example, through data collected by bike-sharing services.

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“…Adequate infrastructure is typically lacking, as is the collection and provision of data on bicycle infrastructure, which is necessary to harness the potential of data-driven bicycle network planning. Recent advances demonstrate that quantitative analyses of bicycle infrastructure on the network level can assist planning decisions and considerably improve the impact of planned investments (Natera Orozco et al, 2020;Olmos et al, 2020;Steinacker et al, 2022;Szell et al, 2022;Vybornova et al, 2022), but the necessary precondition of readily available and complete bicycle infrastructure data is typically not fulfilled. Moreover, even when such data are available, there is often little knowledge on data quality.…”

Section: Introductionmentioning

confidence: 99%

BikeDNA: A tool for bicycle infrastructure data and network assessment

Vierø

Vybornova

Szell

2023

Environment and Planning B: Urban Analytics and City Science

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Building high-quality bicycle networks requires knowledge of existing bicycle infrastructure. However, bicycle network data from governmental agencies or crowdsourced projects like OpenStreetMap often suffer from unknown, heterogeneous, or low quality, which hampers the green transition of human mobility. In particular, bicycle-specific data have peculiarities that require a tailor-made, reproducible quality assessment pipeline: For example, bicycle networks are much more fragmented than road networks, or are mapped with inconsistent data models. To fill this gap, we introduce BikeDNA, an open-source tool for reproducible quality assessment tailored to bicycle infrastructure data with a focus on network structure and connectivity. BikeDNA performs either a standalone analysis of one data set or a comparative analysis between OpenStreetMap and a reference data set, including feature matching. Data quality metrics are considered both globally for the entire study area and locally on grid cell level, thus exposing spatial variation in data quality. Interactive maps and HTML/PDF reports are generated to facilitate the visual exploration and communication of results. BikeDNA supports quality assessments of bicycle infrastructure data for a wide range of applications—from urban planning to OpenStreetMap data improvement or network research for sustainable mobility.

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

Cited by 9 publications

References 40 publications

Learning the complexity of urban mobility with deep generative collaboration network

Learning the complexity of urban mobility with deep generative collaboration network

Efficient solution algorithms for the Bounded Acceleration Shortest Path problem

BikeDNA: A tool for bicycle infrastructure data and network assessment

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