Evaluating the effects of Data Sparsity on the Link-level Bicycling Volume Estimation: A Graph Convolutional Neural Network Approach
School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia（公共卫生与预防医学学院，墨尔本大学）；School of Civil and Environmental Engineering, Research Centre for Integrated Transport Innovation (rCITI)（土木与环境工程学院，综合交通创新研究中心）；School of Information and Communication Technology, Griffith University, Brisbane, Australia（信息与通信技术学院，格里菲斯大学）
Mohit Gupta, Debjit Bhowmick, Meead Saberi, Shirui Pan, Ben Beck
发表信息：
Journal of Cycling and Micromobility Research Volume 6, December 2025, 100086
Accurate bicycling volume estimation is crucial for making informed decisions and planning about future investments in bicycling infrastructure. However, traditional link-level volume estimation models are effective for motorized traffic but face significant challenges when applied to the bicycling context because of sparse data and the intricate nature of bicycling mobility patterns. To the best of our knowledge, we present the first study to utilize a Graph Convolutional Network (GCN) architecture to model link-level bicycling volumes and systematically investigate the impact of varying levels of data sparsity (0%--99%) on model performance, simulating real-world scenarios. We have leveraged Strava Metro data as the primary source of bicycling counts across 15,933 road segments/links in the City of Melbourne, Australia. To evaluate the effectiveness of the GCN model, we benchmark it against traditional machine learning models, such as linear regression, support vector machines, and random forest. Our results show that the GCN model outperforms these traditional models in predicting Annual Average Daily Bicycle (AADB) counts, demonstrating its ability to capture the spatial dependencies inherent in bicycle traffic networks. While GCN remains robust up to 80% sparsity, its performance declines sharply beyond this threshold, highlighting the challenges of extreme data sparsity. These findings underscore the potential of GCNs in enhancing bicycling volume estimation, while also emphasizing the need for further research on methods to improve model resilience under high-sparsity conditions. Our findings offer valuable insights for city planners aiming to improve bicycling infrastructure and promote sustainable transportation.
INSPIRE-GNN: Intelligent Sensor Placement to Improve Sparse Bicycling Network Prediction via Reinforcement Learning Boosted Graph Neural Networks
School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia（公共卫生与预防医学学院，墨尔本大学）；Department of Electrical and Computer System Engineering, Monash University, Melbourne, Australia（电气与计算机系统工程系，墨尔本大学）；School of Civil and Environmental Engineering, UNSW, Sydney, Australia（土木与环境工程学院，新南威尔士大学）；School of Information and Communication Technology, Griffith University, Brisbane, Australia（信息与通信技术学院，格里菲斯大学）
Explaining the Saliency Map Sparsity of Adversarially-Trained Neural Networks
Institute of Mathematics（数学研究所）；University of Würzburg（维尔茨堡大学）；Technical University of Munich（慕尼黑工业大学）；Institute of Computer Science（计算机科学研究所）；CAIDAS；Institute of Mathematics, CAIDAS（CAIDAS数学研究所）
Sparsity is All You Need: Rethinking Biological Pathway-Informed Approaches in Deep Learning
Computational Biomedicine Unit, Department of Medical Sciences, University of Torino（托斯尼亚大学医学科学系计算生物医学单元）；Department of Computer Science and Technology, University of Cambridge（剑桥大学计算机科学与技术系）
