Paper Info
Title
Multi-Task Learning for Transit Service Disruption Detection.
Abstract
With the rapid growth in urban transit networks in recent years, detecting service disruptions in a timely manner is a problem of increased interest to service providers. Transit agencies are seeking to move beyond traditional customer questionnaires and manual service inspections to leveraging open source indicators like social media for deteting emerging transit events. In this paper, we leverage Twitter data for early detection of metro service disruptions. Inspired by the multi-task learning framework, we propose the Metro Disruption Detection Model, which captures the semantic similarity between transit lines in Twitter space. We propose novel constraints on feature semantic similarity exploiting prior knowledge about the spatial connectivity and shared tracks of the metro network. An algorithm based on the alternating direction method of multipliers (ADMM) framework is developed to solve the proposed model. We run extensive experiments and comparisons to other models with real world Twitter data and transit disruption records from the Washington Metropolitan Area Transit Authority (WMATA) to justify the efficacy of our model.
Year
DOI
Venue
2018
10.5555/3382225.3382367
ASONAM '18: International Conference on Advances in Social Networks Analysis and Mining Barcelona Spain August, 2018
Keywords
Field
DocType
Social Media, Twitter, Event Detection, Metro Service Disruption Detection
Semantic similarity,Early detection,Social media,Leverage (finance),Multi-task learning,Computer science,Service provider,Artificial intelligence,Metropolitan area,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-5386-6051-5
2
0.36
References 
Authors
10
5
Name
Order
Citations
PageRank
Taoran Ji1173.39
Kaiqun Fu2215.24
Nathan Self31019.65
Chang-Tien Lu41097115.77
Naren Ramakrishnan51913176.25