BikeNodePlanner: a data-driven decision support tool for bicycle node network planning

• URL: http://arxiv.org/abs/2412.20270v1
• Date: Sat, 28 Dec 2024 20:55:04 GMT
• Title: BikeNodePlanner: a data-driven decision support tool for bicycle node network planning
• Authors: Anastassia Vybornova, Ane Rahbek Vierø, Kirsten Krogh Hansen, Michael Szell,
• Abstract summary: A bicycle node network is a wayfinding system targeted at recreational cyclists, consisting of numbered signposts. The BikeNodePlanner is a fully open-source decision support tool, consisting of modular Python scripts to be run in the free and open-source geographic information system QGIS. The BikeNodePlanner provides data-driven decision support for bicycle node network planning, and can hence be of great use for regional planning, cycling tourism, and the promotion of rural cycling.
• Score: 0.0
• License:
• Abstract: A bicycle node network is a wayfinding system targeted at recreational cyclists, consisting of numbered signposts placed alongside already existing infrastructure. Bicycle node networks are becoming increasingly popular as they encourage sustainable tourism and rural cycling, while also being flexible and cost-effective to implement. However, the lack of a formalized methodology and data-driven tools for the planning of such networks is a hindrance to their adaptation on a larger scale. To address this need, we present the BikeNodePlanner: a fully open-source decision support tool, consisting of modular Python scripts to be run in the free and open-source geographic information system QGIS. The BikeNodePlanner allows the user to evaluate and compare bicycle node network plans through a wide range of metrics, such as land use, proximity to points of interest, and elevation across the network. The BikeNodePlanner provides data-driven decision support for bicycle node network planning, and can hence be of great use for regional planning, cycling tourism, and the promotion of rural cycling.

Related papers

• Understanding the Structure and Resilience of the Brazilian Federal Road Network Through Network Science [44.99833362998488]
  This paper models the federal road network in Brazil as weighted networks. We aim to unveil its topological characteristics and identify key locations (cities) that play important roles for the country through 75,000 kilometres of roads. Our findings aim to bring clarity to the overall structure of federal roads in Brazil, thus providing actionable insights for improving infrastructure planning and prioritising resources to enhance network resilience.
  arXiv  Detail & Related papers  (2024-12-20T13:02:50Z)
• Evaluating the effects of Data Sparsity on the Link-level Bicycling Volume Estimation: A Graph Convolutional Neural Network Approach [54.84957282120537]
  We present the first study to utilize a Graph Convolutional Network architecture to model link-level bicycling volumes. We estimate the Annual Average Daily Bicycle (AADB) counts across the City of Melbourne, Australia using Strava Metro bicycling count data. Our results show that the GCN model performs better than these traditional models in predicting AADB counts.
  arXiv  Detail & Related papers  (2024-10-11T04:53:18Z)
• BikeDNA: A Tool for Bicycle Infrastructure Data & Network Assessment [0.0]
  BikeDNA is an open-source tool for reproducible quality assessment of bicycle infrastructure data. BikeDNA supports quality assessments of bicycle infrastructure data for a wide range of applications.
  arXiv  Detail & Related papers  (2023-03-02T13:06:59Z)
• Predicting Citi Bike Demand Evolution Using Dynamic Graphs [81.12174591442479]
  We apply a graph neural network model to predict bike demand in the New York City, Citi Bike dataset. In this paper, we attempt to apply a graph neural network model to predict bike demand in the New York City, Citi Bike dataset.
  arXiv  Detail & Related papers  (2022-12-18T21:43:27Z)
• Automated Detection of Missing Links in Bicycle Networks [0.15293427903448023]
  We develop the IPDC procedure (Identify, Prioritize, Decluster, Classify) for finding the most important missing links in urban bicycle networks. We first identify all possible gaps following a multiplex network approach, prioritize them according to a flow-based metric, decluster emerging gap clusters, and manually classify the types of gaps. Our results show how network analysis with minimal data requirements can serve as a cost-efficient support tool for bicycle network planning.
  arXiv  Detail & Related papers  (2022-01-10T15:35:14Z)
• Growing Urban Bicycle Networks [0.755972004983746]
  We study different variations of growing a synthetic bicycle network between an arbitrary set of points routed on the urban street network. We find initially decreasing returns on investment until a critical threshold, posing fundamental consequences to sustainable urban planning. We also find pronounced overlaps of synthetically grown networks in cities with well-developed existing bicycle networks, showing that our model reflects reality.
  arXiv  Detail & Related papers  (2021-07-05T18:00:04Z)
• Dynamic Bicycle Dispatching of Dockless Public Bicycle-sharing Systems using Multi-objective Reinforcement Learning [79.61517670541863]
  How to use AI to provide efficient bicycle dispatching solutions based on dynamic bicycle rental demand is an essential issue for dockless PBS (DL-PBS) We propose a dynamic bicycle dispatching algorithm based on multi-objective reinforcement learning (MORL-BD) to provide the optimal bicycle dispatching solution for DL-PBS.
  arXiv  Detail & Related papers  (2021-01-19T03:09:51Z)
• Dynamic Planning of Bicycle Stations in Dockless Public Bicycle-sharing System Using Gated Graph Neural Network [79.61517670541863]
  Dockless Public Bicycle-sharing (DL-PBS) network becomes increasingly popular in many countries. redundant and low-utility stations waste public urban space and maintenance costs of DL-PBS vendors. We propose a Bicycle Station Dynamic Planning (BSDP) system to dynamically provide the optimal bicycle station layout for the DL-PBS network.
  arXiv  Detail & Related papers  (2021-01-19T02:51:12Z)
• Urban Bike Lane Planning with Bike Trajectories: Models, Algorithms, and a Real-World Case Study [13.781010691827072]
  We study an urban bike lane planning problem based on the fine-grained bike trajectory data made available by smart city infrastructure such as bike-sharing systems. As bike-sharing systems become widespread in the metropolitan areas over the world, bike lanes are being planned and constructed by many municipal governments to promote cycling and protect cyclists. We develop tractable formulations and efficient algorithms to solve the large-scale optimization problem.
  arXiv  Detail & Related papers  (2020-08-21T18:46:51Z)
• Progressive Graph Convolutional Networks for Semi-Supervised Node Classification [97.14064057840089]
  Graph convolutional networks have been successful in addressing graph-based tasks such as semi-supervised node classification. We propose a method to automatically build compact and task-specific graph convolutional networks.
  arXiv  Detail & Related papers  (2020-03-27T08:32:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.