Analyzing the effectiveness and applicability of co-training
Proceedings of the ninth international conference on Information and knowledge management
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
An Interactive-Voting Based Map Matching Algorithm
MDM '10 Proceedings of the 2010 Eleventh International Conference on Mobile Data Management
Driving with knowledge from the physical world
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 13th international conference on Ubiquitous computing
MAQS: a personalized mobile sensing system for indoor air quality monitoring
Proceedings of the 13th international conference on Ubiquitous computing
Discovering regions of different functions in a city using human mobility and POIs
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
T-share: A large-scale dynamic taxi ridesharing service
ICDE '13 Proceedings of the 2013 IEEE International Conference on Data Engineering (ICDE 2013)
Real-time air quality monitoring through mobile sensing in metropolitan areas
Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing
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Information about urban air quality, e.g., the concentration of PM2.5, is of great importance to protect human health and control air pollution. While there are limited air-quality-monitor-stations in a city, air quality varies in urban spaces non-linearly and depends on multiple factors, such as meteorology, traffic volume, and land uses. In this paper, we infer the real-time and fine-grained air quality information throughout a city, based on the (historical and real-time) air quality data reported by existing monitor stations and a variety of data sources we observed in the city, such as meteorology, traffic flow, human mobility, structure of road networks, and point of interests (POIs). We propose a semi-supervised learning approach based on a co-training framework that consists of two separated classifiers. One is a spatial classifier based on an artificial neural network (ANN), which takes spatially-related features (e.g., the density of POIs and length of highways) as input to model the spatial correlation between air qualities of different locations. The other is a temporal classifier based on a linear-chain conditional random field (CRF), involving temporally-related features (e.g., traffic and meteorology) to model the temporal dependency of air quality in a location. We evaluated our approach with extensive experiments based on five real data sources obtained in Beijing and Shanghai. The results show the advantages of our method over four categories of baselines, including linear/Gaussian interpolations, classical dispersion models, well-known classification models like decision tree and CRF, and ANN.