Designing bike networks using the concept of network clusters

Akbarzadeh, Meisam; Mohri, Syed Sina; Yazdian, Ehsan

doi:10.1007/s41109-018-0069-0

Cited by 15 publications

(7 citation statements)

References 46 publications

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“…Trip patterns in a city are not uniformly distributed geographically, and community finding methods have been used to partition study areas into localized areas that experience a disproportionate number of trips within them. Akbarzadeh et al (2018) use a modularity maximization approach (Blondel et al, 2008) on taxi trip data to identify 7 different communities in the city of Isfahan, Iran. An optimization problem is then formulated to connect nodes within each community with cycling infrastructure, with the emphasis being on connectivity within the communities, not between them.…”

Section: Ethical Underpinnings and Proposed Approachmentioning

confidence: 99%

A Road Segment Prioritization Approach for Cycling Infrastructure

Mahfouz,

Arcaute,

Lovelace

2021

Preprint

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Understanding the motivators and deterrents to cycling is essential for creating infrastructure that gets more people to adopt cycling as a mode of transport. This paper demonstrates a new approach to support the prioritization of cycling infrastructure and cycling network design, accounting for cyclist preferences and the growing emphasis on 'filtered permeability' and 'Low Traffic Neighborhood' interventions internationally. The approach combines distance decay, route calculation, and network analysis methods to examine where future cycling demand is most likely to arise, how such demand could be accommodated within existing street networks, and how to ensure a fair distribution of investment. Although each of these methods has been applied to cycling infrastructure prioritization in previous research, this is the first time that they have been combined, creating an integrated road segment prioritization approach. The approach, which can be applied to other cities, as shown in the Appendix, is demonstrated in a case study of Manchester, resulting in cycling networks that balance directness against the need for safe and stress-free routes under different investment scenarios. A key benefit of the approach from a policy perspective is its ability to support egalitarian and cost-effective strategic cycle network planning.

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Section: Ethical Underpinnings and Proposed Approachmentioning

confidence: 99%

A Road Segment Prioritization Approach for Cycling Infrastructure

Mahfouz,

Arcaute,

Lovelace

2021

Preprint

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“…Other studies have used input data from bicycle share systems [ 44 ] or origin destination matrices [ 45 ] to plan bicycle lanes. More recently, taxi trips have been used to identify susceptible clusters for bicycle infrastructure [ 46 ]. Here we attempt an alternative approach: since hundreds of bicycle network components already exist in most cities, we aim at consolidating the existing infrastructure by making strategic connections between components rather than starting from scratch.…”

Section: Defining Bicycle Network Growth Strategies and Quality Metrimentioning

confidence: 99%

Data-driven strategies for optimal bicycle network growth

et al. 2020

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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.

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“…Community detection has been applied to urban bicycle-sharing systems using a variety of approaches (Rosvall et al, 2009;Borgnat et al, 2011;Austwick et al, 2013;Akbarzadeh et al, 2018;Munoz-Mendez et al, 2018;Xie and Wang, 2018;He et al, 2019;Kobayashi et al, 2019). Zhu et al (2018) applied k-means clustering to undirected, time-dependent usage data from bicycle-sharing systems and other urban systems in New York City.…”

Section: Introductionmentioning

confidence: 99%

Role detection in bicycle-sharing networks using multilayer stochastic block models

Carlen¹,

Pont²,

Mentus³

et al. 2022

Net Sci

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In urban systems, there is an interdependency between neighborhood roles and transportation patterns between neighborhoods. In this paper, we classify docking stations in bicycle-sharing networks to gain insight into the human mobility patterns of three major cities in the United States. We propose novel time-dependent stochastic block models, with degree-heterogeneous blocks and either mixed or discrete block membership, which classify nodes based on their time-dependent activity patterns. We apply these models to (1) detect the roles of bicycle-sharing stations and (2) describe the traffic within and between blocks of stations over the course of a day. Our models successfully uncover work blocks, home blocks, and other blocks; they also reveal activity patterns that are specific to each city. Our work gives insights for the design and maintenance of bicycle-sharing systems, and it contributes new methodology for community detection in temporal and multilayer networks with heterogeneous degrees.

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Designing bike networks using the concept of network clusters

Cited by 15 publications

References 46 publications

A Road Segment Prioritization Approach for Cycling Infrastructure

A Road Segment Prioritization Approach for Cycling Infrastructure

Data-driven strategies for optimal bicycle network growth

Role detection in bicycle-sharing networks using multilayer stochastic block models

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