Shortest Paths in Time-Dependent FIFO Networks |
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Authors: | Frank Dehne Masoud T. Omran J?rg-Rüdiger Sack |
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Affiliation: | 1. Carleton University, Ottawa, Canada
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Abstract: | In this paper, we study the time-dependent shortest paths problem for two types of time-dependent FIFO networks. First, we consider networks where the availability of links, given by a set of disjoint time intervals for each link, changes over time. Here, each interval is assigned a non-negative real value which represents the travel time on the link during the corresponding interval. The resulting shortest path problem is the time-dependent shortest path problem for availability intervals ( $mathcal{TDSP}_{mathrm{int}}$ ), which asks to compute all shortest paths to any (or all) destination node(s) d for all possible start times at a given source node s. Second, we study time-dependent networks where the cost of using a link is given by a non-decreasing piece-wise linear function of a real-valued argument. Here, each piece-wise linear function represents the travel time on the link based on the time when the link is used. The resulting shortest paths problem is the time-dependent shortest path problem for piece-wise linear functions ( $mathcal{TDSP}_{mathrm{lin}}$ ) which asks to compute, for a given source node s and destination d, the shortest paths from s to d, for all possible starting times. We present an algorithm for the $mathcal{TDSP}_{mathrm{lin}}$ problem that runs in time O((F d +γ)(|E|+|V|log?|V|)) where F d is the output size (i.e., number of linear pieces needed to represent the earliest arrival time function to d) and γ is the input size (i.e., number of linear pieces needed to represent the local earliest arrival time functions for all links in the network). We then solve the $mathcal{TDSP}_{mathrm{int}}$ problem in O(λ(|E|+|V|log?|V|)) time by reducing it to an instance of the $mathcal{TDSP}_{mathrm{lin}}$ problem. Here, λ denotes the total number of availability intervals in the entire network. Both methods improve significantly on the previously known algorithms. |
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