Introduction to algorithms
Network flows: theory, algorithms, and applications
Network flows: theory, algorithms, and applications
Explicit Graphs in a Functional Model for Spatial Databases
IEEE Transactions on Knowledge and Data Engineering
CCAM: A Connectivity-Clustered Access Method for Networks and Network Computations
IEEE Transactions on Knowledge and Data Engineering
Time-Expanded Graphs for Flow-Dependent Transit Times
ESA '02 Proceedings of the 10th Annual European Symposium on Algorithms
Spatio-Temporal Databases
Time-Aggregated graphs for modeling spatio-temporal networks
CoMoGIS'06 Proceedings of the 2006 international conference on Advances in Conceptual Modeling: theory and practice
Capacity constrained routing algorithms for evacuation planning: a summary of results
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
A Lagrangian approach for storage of spatio-temporal network datasets: a summary of results
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Spatial-temporal data evaluation
AICT'11 Proceedings of the 2nd international conference on Applied informatics and computing theory
A Framework For Handling Local Broadcast Storm Using Probabilistic Data Aggregation In VANET
Wireless Personal Communications: An International Journal
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Given applications such as location based services and the spatio-temporal queries they may pose on a spatial network (e.g., road networks), the goal is to develop a simple and expressive model that honors the time dependence of the road network. The model must support the design of efficient algorithms for computing the frequent queries on the network. This problem is challenging due to potentially conflicting requirements of model simplicity and support for efficient algorithms. Time expanded networks, which have been used to model dynamic networks employ replication of the networks across time instants, resulting in high storage overhead and algorithms that are computationally expensive. In contrast, the proposed time-aggregated graphs do not replicate nodes and edges across time; rather they allow the properties of edges and nodes to be modeled as a time series. Since the model does not replicate the entire graph for every instant of time, it uses less memory and the algorithms for common operations are computationally more efficient than for time expanded networks. One important query on spatio-temporal networks is the computation of shortest paths. Shortest paths can be computed either for a given start time or to find the start time and the path that lead to least travel time journeys (best start time journeys). Developing efficient algorithms for computing shortest paths in a time variant spatial network is challenging because these journeys do not always display optimal prefix property, making techniques like dynamic programming inapplicable. In this paper, we propose algorithms for shortest path computation for a fixed start time. We present the analytical cost model for the algorithm and compare with the performance of existing algorithms.