A Framework for Generating Network-Based Moving Objects
Geoinformatica
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
State Space Neural Networks for Freeway Travel Time Prediction
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Finding Fastest Paths on A Road Network with Speed Patterns
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Continuous nearest neighbor monitoring in road networks
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Query processing in spatial network databases
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Finding time-dependent shortest paths over large graphs
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Scalable network distance browsing in spatial databases
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Spatio-temporal network databases and routing algorithms: a summary of results
SSTD'07 Proceedings of the 10th international conference on Advances in spatial and temporal databases
A case for time-dependent shortest path computation in spatial networks
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Efficient k-nearest neighbor search in time-dependent spatial networks
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part I
Online computation of fastest path in time-dependent spatial networks
SSTD'11 Proceedings of the 12th international conference on Advances in spatial and temporal databases
Ranking continuous nearest neighbors for uncertain trajectories
The VLDB Journal — The International Journal on Very Large Data Bases
Processing (multiple) spatio-temporal range queries in multicore settings
ADBIS'11 Proceedings of the 15th international conference on Advances in databases and information systems
Discovering patterns in traffic sensor data
Proceedings of the 2nd ACM SIGSPATIAL International Workshop on GeoStreaming
Towards k-nearest neighbor search in time-dependent spatial network databases
DNIS'10 Proceedings of the 6th international conference on Databases in Networked Information Systems
Similarity in (spatial, temporal and) spatio-temporal datasets
Proceedings of the 15th International Conference on Extending Database Technology
P2EST: parallelization philosophies for evaluating spatio-temporal queries
Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data
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A spatiotemporal network is a spatial network (e.g., road network) along with the corresponding time-dependent weight (e.g., travel time) for each edge of the network. The design and analysis of policies and plans on spatiotemporal networks (e.g., path planning for location-based services) require realistic models that accurately represent the temporal behavior of such networks. In this paper, for the first time we propose a traffic modeling framework for road networks that enables 1) generating an accurate temporal model from archived temporal data collected from a spatiotemporal network (so as to be able to publish the temporal model of the spatiotemporal network without having to release the real data), and 2) augmenting any given spatial network model with a corresponding realistic temporal model custom-built for that specific spatial network (in order to be able to generate a spatiotemporal network model from a solely spatial network model). We validate the accuracy of our proposed modeling framework via experiments. We also used the proposed framework to generate the temporal model of the Los Angeles County freeway network and publish it for public use.