Computing Urban Traffic Congestions by Incorporating Sparse GPS Probe Data and Social Media Data
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所属单位：计算机科学与技术学院/人工智能学院/软件学院
发表刊物：ACM TRANSACTIONS ON INFORMATION SYSTEMS
关键字：Social media traffic congestion matrix factorization data fusion
摘要：Estimating urban traffic conditions of an arterial network with GPS probe data is a practically important while substantially challenging problem, and has attracted increasing research interests recently. Although GPS probe data is becoming a ubiquitous data source for various traffic related applications currently, they are usually insufficient for fully estimating traffic conditions of a large arterial network due to the low sampling frequency. To explore other data sources for more effectively computing urban traffic conditions, we propose to collect various traffic events such as traffic accident and jam from social media as complementary information. In addition, to further explore other factors that might affect traffic conditions, we also extract rich auxiliary information including social events, road features, Point of Interest (POI), and weather. With the enriched traffic data and auxiliary information collected from different sources, we first study the traffic co-congestion pattern mining problem with the aim of discovering which road segments geographically close to each other are likely to co-occur traffic congestion. A search tree based approach is proposed to efficiently discover the co-congestion patterns. These patterns are then used to help estimate traffic congestions and detect anomalies in a transportation network. To fuse the multisourced data, we finally propose a coupled matrix and tensor factorization model named TCE_R to more accurately complete the sparse traffic congestion matrix by collaboratively factorizing it with other matrices and tensors formed by other data. We evaluate the proposed model on the arterial network of downtown Chicago with 1,257 road segments whose total length is nearly 700 miles. The results demonstrate the superior performance of TCE_ R by comprehensive comparison with existing approaches.
ISSN号：1046-8188
是否译文：否
发表时间：2017-08-01
合写作者：Zhang, Xiaoming,Cao, Jianping,He, Lifang,Stenneth, Leon,Yu, Philip S.,Li, Zhoujun,黄志球
通讯作者：王森章