Journal article
Transit Pattern Detection Using Tensor Factorization
INFORMS JOURNAL ON COMPUTING, v 31(2), 193
22 Mar 2019
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Understanding citywide transit patterns is important for transportation management, including city planning and route optimization. The wide deployment of automated fare collection (AFC) systems in public transit vehicles has enabled us to collect massive amounts of transit records, which capture passengers' traveling activities. Based on such transit records, origin-destination associations have been studied extensively in the literature. However, the identification of transit patterns that establish the origin-transfer-destination (OTD) associations, in spite of its importance, is underdeveloped. In this paper, we propose a framework based on transit tensor factorization (TTF) to identify citywide travel patterns. In particular, we create a transit tensor, which summarizes the citywide OTD information of all passenger trips captured in the AFC records. The TTF framework imposes spatial regularization in the formulation to group nearby stations into meaningful regions and uses multitask learning to identify traffic flows among these regions at different times of the day and days of the week. Evaluated with large-scale, real-world data, our results show that the proposed TTF framework can effectively identify meaningful citywide transit patterns.
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Details
- Title
- Transit Pattern Detection Using Tensor Factorization
- Creators
- Bowen Du - Beihang UniversityWenjun Zhou - University of California, DavisChuanren Liu - Drexel UniversityYifeng Cui - Beihang UniversityHui Xiong - Rutgers State Univ, Management Sci & Informat Syst Dept, Edison, NJ 08820 USA
- Publication Details
- INFORMS JOURNAL ON COMPUTING, v 31(2), 193
- Publisher
- Informs
- Number of pages
- 14
- Grant note
- 51408018; 51778033; 91746301 / Natural Science Foundation of China; National Natural Science Foundation of China (NSFC) IIS-1648664 / National Science Foundation (NSF) Division of Information and Intelligent Systems; National Science Foundation (NSF); NSF - Directorate for Computer & Information Science & Engineering (CISE)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Drexel University
- Web of Science ID
- WOS:000468604000001
- Scopus ID
- 2-s2.0-85070418540
- Other Identifier
- 991021860693704721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Interdisciplinary Applications
- Operations Research & Management Science