报告题目:Multi-task Learning for Transit Service Disruption Detection
报告时间:7月19日下午14:00
报告地点:计算机楼B321
报 告 人:吕昌田 教授
报告人简介:
吕昌田博士,美国弗吉尼亚理工大学教授,北弗州校区计算机系主任,数据挖掘与知识发现研究中心副主任。2001获得明尼苏达大学双子城校区博士学位。曾担任第十八届IEEE人工智能工具国际会议程序委员会主席、第十七届 ACM地理信息系统国际会议和2017年空间/时间数据库国际研讨会会议主席。目前主要从事空间数据库、数据挖掘、人工智能、城市计算和智能交通系统等方面的研究。在ACM KDD、IEEE CDM、ACM GIS、IJCAI、AAAI等高水平会议、期刊共发表150多篇文章。目前担任ACM Transactions on Spatial Algorithms and Systems、Data & Knowledge Engineering、GeoInformatica等期刊副主编。研究工作获得美国国家科学基金(NSF) 、美国国家卫生研究院(NIH) 、国防部 (DoD) 、国防高等研究计划署(IARPA) 、弗吉尼亚州交通局 (VDOT)以及哥伦比亚特区交通局 (DCDOT) 等基金支持,获评美国计算机学会杰出科学家 (ACM Distinguished Scientist)。
报告内容简介:
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.
主办单位:
伟德BETVlCTOR1946
伟德bv国际体育软件学院
伟德bv国际体育计算机科学技术研究所
符号计算与知识工程教育部重点实验室
伟德bv国际体育国家级计算机实验教学示范中心