Graph drawing by force-directed placement
Software—Practice & Experience
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Near-optimal sensor placements: maximizing information while minimizing communication cost
Proceedings of the 5th international conference on Information processing in sensor networks
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Logical and Relational Learning: From ILP to MRDM (Cognitive Technologies)
Logical and Relational Learning: From ILP to MRDM (Cognitive Technologies)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Understanding and modeling pedestrian mobility of train-station scenarios
Proceedings of the third ACM international workshop on Wireless network testbeds, experimental evaluation and characterization
Pedestrian flow prediction in extensive road networks using biased observational data
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
A Vector-Geometry Based Spatial kNN-Algorithm for Traffic Frequency Predictions
ICDMW '08 Proceedings of the 2008 IEEE International Conference on Data Mining Workshops
Scalable Sparse Bayesian Network Learning for Spatial Applications
ICDMW '08 Proceedings of the 2008 IEEE International Conference on Data Mining Workshops
Stacked Gaussian Process Learning
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Fast Visual Trajectory Analysis Using Spatial Bayesian Networks
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
Efficient and simple generation of random simple connected graphs with prescribed degree sequence
COCOON'05 Proceedings of the 11th annual international conference on Computing and Combinatorics
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In street-based mobility mining, traffic volume estimation receives increasing attention as it provides important applications such as emergency support systems, quality-of-service evaluation and billboard placement. In many real world scenarios, empirical measurements are usually sparse due to some constraints. On the other hand, pedestrians generally show some movement preferences, especially in closed environments, e.g., train stations. We propose a Gaussian process regression based method for traffic volume estimation, which incorporates topological information and prior knowledge on preferred trajectories with a trajectory pattern kernel. Our approach also enables effectively finding most informative sensor placements. We evaluate our method with synthetic German train station pedestrian data and real-world episodic movement data from the zoo of Duisburg. The empirical analysis demonstrates that incorporating trajectory patterns can largely improve the traffic prediction accuracy, especially when traffic networks are sparsely monitored.