Bicycle network design: model and solution algorithm

Mauttone, Antonio; Mercadante, Gonzalo; Rabaza, María Victoria; Toledo, Fernanda

doi:10.1016/j.trpro.2017.12.119

Cited by 27 publications

(22 citation statements)

References 13 publications

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“…Hyodo et al [ 43 ] have proposed a bicycle route choice model to plan bicycle lanes taking into account facility characteristics. 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 ].…”

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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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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“…The objective of routing problem is proposing some best routes between a specific origin and destination (OD). While, the bike network design problem considers some OD pairs and presents some directed bike lanes as a bike network (Buehler and Dill 2016 ; Hrncir et al 2015 ; Mauttone et al 2017 ; Song et al 2014 ).…”

Section: Review Of Literaturementioning

confidence: 99%

“…Also, the construction costs of network was related to the links and intersections (Duthie and Unnikrishnan 2014 ). Mauttone et al 2017 , with considering the interest of planners and users proposed a single objective model for bike network design that minimized the distance of bike trips given by an OD matrix. The interest of planner was provided by applying a budget constraint into the model (Mauttone et al 2017 ).…”

Section: Review Of Literaturementioning

confidence: 99%

“…This study with considering both objectives of planners and users proposes a bi-objective model for bike network design. In contrast with (Mauttone et al 2017 ), we consider the interest of planner as a model objective with minimizing the length of proposed network. Also, the potential OD demands for bike network are gathered by a digital payment service as a revealed preferences data.…”

Section: Review Of Literaturementioning

confidence: 99%

“…The previous studies built the OD matrix with a stated preferences data that were collected by home surveys (Duthie and Unnikrishnan 2014 ; Mauttone et al 2017 ; Mesbah and Thompson 2011 ). One of the big problem of previous studies for routing bike lanes or bike network design was the the big size of the problem and disability of exact methods for solving it (Hrncir et al 2015 ; Mauttone et al 2017 ; Song et al 2014 ). This study with identifying the key OD pairs in each cluster that have the most potential of moving to bike network decreases the size and complexity of problem.…”

Section: Review Of Literaturementioning

confidence: 99%

See 2 more Smart Citations

Designing bike networks using the concept of network clusters

2018

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In this paper, a novel method is proposed for designing a bike network in urban areas. Based on the number of taxi trips within an urban area, a weighted network is abstracted. In this network, nodes are the origins and destinations of taxi trips and the number of trips among them is abstracted as link weights. Data is extracted from the Taxi smart card system of a real city. Then, Communities i.e. clusters of this network are detected using a modularity maximization method. Each community contains the nodes with highest number of trips within the cluster and lowest number of trips with other clusters. Within each community, the nodes close enough to each other for being traveled by bicycle are detected as key points and some non-dominated bike network connecting these nodes are enumerated using a bi-objective optimization model. The total travel cost (distance or time) on the network and the path length are considered as objectives. The method is applied to Isfahan city in Iran and a total of seven regions with some non-dominated bike networks are proposed.

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Multi-objective bike-way network design problem with space–time accessibility constraint

Zhu

2019

Transportation

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Bicycle network design: model and solution algorithm

Cited by 27 publications

References 13 publications

Data-driven strategies for optimal bicycle network growth

Data-driven strategies for optimal bicycle network growth

Designing bike networks using the concept of network clusters

Multi-objective bike-way network design problem with space–time accessibility constraint

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