Fibonacci heaps and their uses in improved network optimization algorithms
Journal of the ACM (JACM)
Shortest-path and minimum-delay algorithms in networks with time-dependent edge-length
Journal of the ACM (JACM)
Davenport-Schinzel sequences and their geometric applications
Davenport-Schinzel sequences and their geometric applications
Faster shortest-path algorithms for planar graphs
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
FATES: Finding A Time dEpendent Shortest path
MDM '03 Proceedings of the 4th International Conference on Mobile Data Management
Minimum Time and Minimum Cost-Path Problems in Street Networks with Periodic Traffic Lights
Transportation Science
Finding time-dependent shortest paths over large graphs
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
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
Information on the consequence of a move and its use for route improvisation support
COSIT'11 Proceedings of the 10th international conference on Spatial information theory
On the complexity of time-dependent shortest paths
Proceedings of the twenty-second annual ACM-SIAM symposium on Discrete Algorithms
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We study the problem of finding shortest paths in time-dependent networks with edge load forecasts where the behavior of each edge is modeled as a time-dependent arrival function with FIFO property. Here, we present a new algorithm that computes for a given start node s and destination node d, the shortest paths and earliest arrival times for all possible starting times. Our algorithm runs in time O((Fd + λ)(|E| + |V| log |V|)) where Fd is the output size (number of linear pieces needed to represent the earliest arrival time function) and λ is the input size (number of linear pieces needed to represent the local earliest arrival time functions for all edges in the network). Our method improves significantly on the best previously known algorithm which requires time O(Fmax |V||E|) where Fmax ≥ Fd is the maximum number of linear pieces needed to represent the earliest arrival time function between the start node s to any node in the network. It has been conjectured that there are cases where Fmax is of super-polynomial size; however, even in such cases, Fd might still be of linear size. In such cases, our algorithm would take polynomial time to find the solution, while other methods require super-polynomial time. Both of the above methods are not useful in practice for graphs where Fd is of super-polynomial size. For such graphs, we present the first approximation method to compute for all possible starting times at s, the earliest arrival times at d within error at most ε. Our algorithm runs in time O(Δ/ε(|E| + |V|log|V|)) where Δ is the difference between the earliest arrival times at d for the latest and earliest starting times at s.