The problem of finding the most reliable routing strategy on urban transportation networks refers to determining the time-adaptive routing policy that maximizes the probability of on-time arrival at a destination give...
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The problem of finding the most reliable routing strategy on urban transportation networks refers to determining the time-adaptive routing policy that maximizes the probability of on-time arrival at a destination given an arrival time threshold. The problem is defined on a stochastic and time-dependent network that captures real-world transportation systems' inherent uncertainty and dynamism. To solve this problem, we present a dynamic programming-based algorithm that benefits from a node-time pairs queue implementation. In addition to improving the computational running time in most cases, this implementation supports different queue disciplines, leading to different algorithmic approaches: label-correcting and label-setting methods. We prove the correctness of the algorithm and derive its worst case time complexity. We present computational experiments over real-world, large-scale transportation networks with up to similar to 33,000 nodes, showing that the algorithm is a viable alternative to existing state-of-the-art methods. It can be four times faster for relatively tight arrival time thresholds and is competitive for looser ones. We also present experiments assessing the different queue disciplines used within the algorithm, the gains of the node-time pairs queue implementation, and comparing optimal strategies obtained from reliability and travel time objectives.
The Resource Constrained Shortest Path Problem (RCSPP) is a variant of the classical shortest path problem and is of great practical importance. The aim is to find the shortest path between a given pair of nodes under...
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The Resource Constrained Shortest Path Problem (RCSPP) is a variant of the classical shortest path problem and is of great practical importance. The aim is to find the shortest path between a given pair of nodes under additional constraints representing upper bounds on the consumption of resources along the path. In the scientific literature, different approaches have been defined to solve the RCSPP. In this work we propose an innovative interactive method to address the RCSPP, based on a novel search strategy of the criteria space. The performance of the proposed approach is evaluated on the basis of an extensive computational study by considering benchmark instances. A comparison with the state-of-the-art approaches developed for the RCSPP is also carried out. The computational results have shown that the developed solution strategy is competitive with the most efficient strategies known thus far.
In this paper we propose a novel computational technique to solve the Eikonal equation efficiently on parallel architectures. The proposed method manages the list of active nodes and iteratively updates the solutions ...
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In this paper we propose a novel computational technique to solve the Eikonal equation efficiently on parallel architectures. The proposed method manages the list of active nodes and iteratively updates the solutions on those nodes until they converge. Nodes are added to or removed from the list based on a convergence measure, but the management of this list does not entail an extra burden of expensive ordered data structures or special updating sequences. The proposed method has suboptimal worst-case performance but, in practice, on real and synthetic datasets, runs faster than guaranteed-optimal alternatives. Furthermore, the proposed method uses only local, synchronous updates and therefore has better cache coherency, is simple to implement, and scales efficiently on parallel architectures. This paper describes the method, proves its consistency, gives a performance analysis that compares the proposed method against the state-of-the-art Eikonal solvers, and describes the implementation on a single instruction multiple datastream (SIMD) parallel architecture.
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