A Tractable Class of Algorithms for Reliable Routing in Stochastic Networks

Samaranayake, Samitha; Blandin, Sébastien; Bayen, Alexandre M.

doi:10.1016/j.sbspro.2011.04.521

Cited by 38 publications

(83 citation statements)

References 21 publications

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“…This problem is abbreviated as SOTA (Stochastic On Time Arrival) [8,14,16]. In papers [8,19] for the definition of the optimal routing policy, an additional notation is introduced. Let Definition 1 [8].…”

Section: Stochastic On-time Arrival Problemmentioning

confidence: 99%

“…In either case, the optimality condition for routing can be determined in different ways depending on the used objective function. The following types of objective functions can be used: 1) minimization of the least expected travel time [4 -7]; 2) maximization of the probability of arriving at the destination at a predetermined time interval [8,9]; 3) maximization of the probability that travel time is less than a given threshold [10,11]; 4) minimization of the worst possible travel time [12]; 5) minimization of the travel time to guarantee a given likelihood of arriving on-time in a stochastic network [13]. These types of objective functions can be divided into two groups: the least expected time (LET) (1) and the reliable shortest path (RSP) problem (2)(3)(4)(5).…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, in a number of practical tasks, the path with the least expected time may not be suitable, since it does not take into account the dispersion of the travel time and does not give any guarantees of reliability. In many cases, road users decided to increase travel time and to choose a more reliable route [8]. The problem of finding a reliable shortest path was investigated in [10,11].…”

Section: Introductionmentioning

confidence: 99%

“…The problem of finding a reliable shortest path was investigated in [10,11]. In [14,15], a shortest path algorithm was proposed, taking into account current and predictive information about the transport flows in the network, which is a modification of the algorithm from [8]. However, the algorithms described in the works are computationally complex and cannot be used to determine the shortest path in large-scale networks in real time.…”

Section: Introductionmentioning

confidence: 99%

“…Acceleration of the algorithm is achieved by choosing a subset of the graph nodes used to find the shortest path. The algorithm proposed by the group of authors in [8] is used as a basic algorithm.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Acceleration of the reliable shortest path algorithm in a time-dependent stochastic transport network

Abdulganiev¹,

Agafonov²

2017

Collection of Selected Papers of the III International Conference on Information Technology and Nanotechnology

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In this paper, we propose a modification of the reliable routing algorithm in a stochastic time-dependent network. We consider a stochastic ontime arrival problem. Reliability means maximization the probability of arrival at a destination within a given period of time. Modification of the shortest-path algorithm is aimed to decrease the computation time of the algorithm. The base idea of the proposed modification is to select a certain subset of nodes and links of the graph which can be used for calculating the shortest path. We propose two methods for selecting subset of nodes: based on a bounding box and based on the k-shortest path algorithm. Experimental studies of the base and modified algorithms are carried out on the transport network of Samara, Russia.

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Section: Stochastic On-time Arrival Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Acceleration of the reliable shortest path algorithm in a time-dependent stochastic transport network

Abdulganiev¹,

Agafonov²

2017

Collection of Selected Papers of the III International Conference on Information Technology and Nanotechnology

View full text Add to dashboard Cite

show abstract

The Internet of Things and Machine Learning, Solutions for Urban Infrastructure Management

Arandia

Eck

McKenna

et al. 2019

Mathematics of Planet Earth

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Tractable Pathfinding for the Stochastic On-Time Arrival Problem

Niknami

Samaranayake

2016

Lecture Notes in Computer Science

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Abstract. We present a new and more efficient technique for computing the route that maximizes the probability of on-time arrival in stochastic networks, also known as the path-based stochastic on-time arrival (SOTA) problem. Our primary contribution is a pathfinding algorithm that uses the solution to the policy-based SOTA problem-which is of pseudopolynomial-time complexity in the time budget of the journey-as a search heuristic for the optimal path. In particular, we show that this heuristic can be exceptionally efficient in practice, effectively making it possible to solve the path-based SOTA problem as quickly as the policybased SOTA problem. Our secondary contribution is the extension of policy-based preprocessing to path-based preprocessing for the SOTA problem. In the process, we also introduce Arc-Potentials, a more efficient generalization of Stochastic Arc-Flags that can be used for both policy-and path-based SOTA. After developing the pathfinding and preprocessing algorithms, we evaluate their performance on two different real-world networks. To the best of our knowledge, these techniques provide the most efficient computation strategy for the path-based SOTA problem for general probability distributions, both with and without preprocessing.

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A Tractable Class of Algorithms for Reliable Routing in Stochastic Networks

Cited by 38 publications

References 21 publications

Acceleration of the reliable shortest path algorithm in a time-dependent stochastic transport network

Acceleration of the reliable shortest path algorithm in a time-dependent stochastic transport network

The Internet of Things and Machine Learning, Solutions for Urban Infrastructure Management

Tractable Pathfinding for the Stochastic On-Time Arrival Problem

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