Distributed Processing of k Shortest Path Queries over Dynamic Road Networks

Yu, Ziqiang; Yu, Xiaohui; Koudas, Nick; Liu, Yang; Li, Yifan; Chen, Yueting; Yang, Dingyu

doi:10.1145/3318464.3389735

Cited by 24 publications

(3 citation statements)

References 43 publications

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“…in distributed settings), have been proposed in the recent past and are worth being mentioned to complete the overview on available solutions to the problem, see e.g. [11,27,36,42,55,62,64].…”

Section: A Related Workmentioning

confidence: 99%

Top-k Distance Queries on Large Time-Evolving Graphs

D’ascenzo,

D’emidio

2023

IEEE Access

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Fast extraction of top-k distances from graph data is a primitive of paramount importance in the fields of data mining, network analytics and machine learning, where ranked distances are exploited for several purposes (e.g. link prediction or network classification). While investigation on computational methods to address this retrieval task for regularly sized, static inputs has been extensive, much less is known when managed graphs are massive, i.e. having millions of vertices/edges, and time-evolving, i.e. when their structure can grow over time, a scenario that introduces a number of scalability and effectiveness issues otherwise not arising. Since, nowadays, most real-world applications exploiting top-k distances have to handle inherently dynamic and rapidly growing graphs, in this paper we present the first dynamic indexing scheme that supports very fast queries on top-k distances when graphs are massive and incrementally timeevolving. We assess the scalability and effectiveness of our method through extensive experimentation on both real-world and artificial graph datasets.

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Section: A Related Workmentioning

confidence: 99%

Top-k Distance Queries on Large Time-Evolving Graphs

D’ascenzo,

D’emidio

2023

IEEE Access

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“…These algorithms have performed excellent results in offline static environments. For dynamic environments, Yu et al proposed a distributed algorithm (k Shortest Path in Dynamic Graphs, KSP-DG) for identifying k-shortest paths in dynamic graphs, which can find multiple shortest paths in dynamic road network environments [3] [4]. However, these algorithms rely too much on environmental data, and require a large amount of data to support path planning, and the effect is poor in an unknown environment, and they cannot be applied to environmental changes.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Driving Path Planning based on Sarsa-Dyna Algorithm

Hassanien¹,

Mononteliza²

2020

APJCRI

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In autonomous driving path planning, ensuring the computational efficiency and safety of planning is an important issue. The Dyna framework in reinforcement learning can solve the problem of planning efficiency. At the same time, the Sarsa algorithm in reinforcement learning can be effective in guaranteeing the safety of path planning. This paper proposes a path planning algorithm based on Sarsa-Dyna for autonomous driving, which effectively guarantees the efficiency and safety of path planning. The results show that the number of steps planned in advance is proportional to the convergence speed of the reinforcement learning algorithm. The Sarsa-Dyna will be proposed. The analysis of convergence speed and collision times has been done between the proposed Sarsa-Dyna, Q-learning, Sarsa and Dyna-Q algorithm. The proposed Sarsa-Dyna algorithm can reduce the number of collisions effectively, ensure safety during driving, and at the same time ensure convergence speed.

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“…The parallelization utilizes GPUs by Nvidia using Compute Unified Device Architecture (CUDA) mainly based on a parallel version of Dijkstra's algorithm. Another work by Yu et al [77] focuses on a distributed algorithm for the k-shortest path problem on graphs with dynamically changing edge weights. We have Parallelization:…”

Section: Related Workmentioning

confidence: 99%

k-shortest path: average-case analysis and practical improvements

Schickedanz

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The single-source shortest-path problem is a fundamental problem in computer science. We consider a generalization of the shortest-path problem, the $k$-shortest path problem. Let $G$ be a directed edge-weighted graph with $n$ nodes and $m$ edges and $s,t$ be two fixed nodes. The goal is to compute $k$ paths $P_1,\dots,P_k$ between two fixed nodes $s$ and $t$ in non-decreasing order of their length such that all other paths between $s$ and $t$ are at least as long as the $k$\nth path $P_k$. We focus on the version of the $k$-shortest path problem where the paths are not allowed to visit nodes multiple times, sometime referred to as $k$-shortest simple path problem. The probably best known $k$-shortest path algorithm is Yen's algorithm. It has a worst-case time complexity of O(kn\cdot scp(n,m)), where scp(n,m) is the complexity of the single-source shortest-path algorithm used as a subroutine. In case of Dijkstra's algorithm scp(n,m) is O(m + n\log n). One of the more recent improvements of Yen's algorithm is by Feng. Even though Feng's algorithm is much faster in practice, it has the same worst-case complexity as Yen's algorithm. The main results presented in this thesis are upper bounds on the average-case of Yen's and Feng's algorithm, as well as practical improvements and a parallel implementation of Yen's and Feng's algorithms including these improvements. The implementation is publicly available under GPLv3 open source license. We show in our analysis that Yen's algorithm has an average-case complexity of O(k \log(n)\cdot scp(n,m)) on G(n,p) graphs with at least logarithmic average-degree and random edge weights following a distribution with certain properties. On G(n,p) graphs with constant to logarithmic average-degree and uniform random edge-weights over $[0;1]$, we show an average-case complexity of O(k\cdot\frac{\log^2 n}{np}\cdot scp(n,m)). Feng's algorithm has an even better average-case complexity of O(k\cdot scp(n,m)) on unweighted G(n,p) graphs with logarithmic average-degree and for constant values of $k$. We further provide evidence that the same holds true for G(n,p) graphs with uniform random edge-weights over $[0;1]$. On the practical side, we suggest new heuristics to prune even more single-source shortest-path computations than Feng's algorithm and evaluate all presented algorithms on G(n,p) and Grid graphs with up to 256 million nodes. We demonstrate speedups by a factor of up to 40 compared to Feng's algorithm. Finally we discuss two ways to parallelize the suggested algorithms and evaluate them on grid graphs showing speedups by a factor of 2 using 4 threads and by a factor of up to 8 using 16 threads, respectively.

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Distributed Processing of k Shortest Path Queries over Dynamic Road Networks

Cited by 24 publications

References 43 publications

Top-k Distance Queries on Large Time-Evolving Graphs

Top-k Distance Queries on Large Time-Evolving Graphs

Autonomous Driving Path Planning based on Sarsa-Dyna Algorithm

k-shortest path: average-case analysis and practical improvements

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