Paper Info
Title
Network-wide Crowd Flow Prediction of Sydney Trains via Customized Online Non-negative Matrix Factorization.
Abstract
Crowd Flow Prediction (CFP) is one major challenge in the intelligent transportation systems of the Sydney Trains Network. However, most advanced CFP methods only focus on entrance and exit flows at the major stations or a few subway lines, neglecting Crowd Flow Distribution (CFD) forecasting problem across the entire city network. CFD prediction plays an irreplaceable role in metro management as a tool that can help authorities plan route schedules and avoid congestion. In this paper, we propose three online non-negative matrix factorization (ONMF) models. ONMF-AO incorporates an Average Optimization strategy that adapts to stable passenger flows. ONMF-MR captures the Most Recent trends to achieve better performance when sudden changes in crowd flow occur. The Hybrid model, ONMF-H, integrates both ONMF-AO and ONMF-MR to exploit the strengths of each model in different scenarios and enhance the models' applicability to real-world situations. Given a series of CFD snapshots, both models learn the latent attributes of the train stations and, therefore, are able to capture transition patterns from one timestamp to the next by combining historic guidance. Intensive experiments on a large-scale, real-world dataset containing transactional data demonstrate the superiority of our ONMF models.
Year
DOI
Venue
2018
10.1145/3269206.3271757
CIKM
Keywords
Field
DocType
Crowd flow prediction, online non-negative matrix factorization, city trains network
Data mining,Computer science,Matrix decomposition,Exploit,Schedule,Non-negative matrix factorization,Timestamp,Intelligent transportation system,Train,Transaction data
Conference
ISBN
Citations 
PageRank 
978-1-4503-6014-2
5
0.40
References 
Authors
16
6
Name
Order
Citations
PageRank
Yongshun Gong1235.85
Zhibin Li2254.18
Jian Zhang31305100.05
Wei Liu446837.36
Yu Zheng58939432.87
Christina Kirsch651.07