Stochastic Shortest Path Finding in Path-Centric Uncertain Road Networks

Andonov, Georgi; Yang, Bin

doi:10.1109/mdm.2018.00020

Cited by 8 publications

(2 citation statements)

References 15 publications

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“…Another study considers ranking a set of POIs in a road network [37], which cannot be used for ranking paths. Probabilistic top-k shortest path queries [36], [39] rank paths w.r.t. the probability of arriving within a time budget, which is provided by end users.…”

Section: Top-k Queries On Road Networkmentioning

confidence: 99%

Context-Aware Path Ranking in Road Networks

Yang¹,

Guo²,

Yang³

2020

IEEE Trans. Knowl. Data Eng.

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Ranking paths becomes an increasingly important functionality in many transportation services, where multiple paths connecting a source-destination pair are offered to drivers. We study ranking such paths under specific contexts, e.g., at a departure time and for a specific driver. More specifically, we model ranking as a regression problem where we assign a ranking score to each path with the help of historical trajectories. The intuition is that if a driver's trajectory used path P at time t, we consider this as an evidence that path P is preferred by the driver at time t, thus should have a higher ranking score than other paths connecting the same source and destination. To solve the regression problem, we first propose an effective training data enriching method to obtain a compact and diversified set of training paths using historical trajectories, which provides a data foundation for efficient and effective learning. Next, we propose a multi-task learning framework that considers features representing both candidate paths and contexts. Specifically, a road network embedding is proposed to embed paths into feature vectors by considering both road network topology and spatial properties, such as distances and travel times. By modeling different departure times as a temporal graph, graph embedding is used to embed departure times into feature vectors. The objective function not only considers the discrepancies on ranking scores but also the reconstruction errors of the spatial properties of the paths, which in turn improves the final ranking estimation. Empirical studies on a substantial trajectory data set offer insight into the designed properties of the proposed framework, indicating that it is effective and practical in real world settings.

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Section: Top-k Queries On Road Networkmentioning

confidence: 99%

Context-Aware Path Ranking in Road Networks

Yang¹,

Guo²,

Yang³

2020

IEEE Trans. Knowl. Data Eng.

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“…Hence the solution for the stochastic shortest path problem still works and is especially valuable. Furthermore, the solution of this problem can also be applied in other areas, including emergency response [20], robot navigation [21], and road networks [22]. Therefore, the solution of SSP problem is advantageous in diverse areas, including the wireless sensor networks.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning Based Stochastic Shortest Path Finding in Wireless Sensor Networks

Xia

Guo

et al. 2019

IEEE Access

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Many factors influence the connection states between nodes of wireless sensor networks, such as physical distance, and the network load, making the network's edge length dynamic in abundant scenarios. This dynamic property makes the network essentially form a graph with stochastic edge lengths. In this paper, we study the stochastic shortest path problem on a directional graph with stochastic edge lengths, using reinforcement learning algorithms. we regard each edge length as a random variable following unknown probability distribution and aim to find the stochastic shortest path on this stochastic graph. We evaluate the performance of path-finding algorithms using regret, which represents the cumulative reward difference between the practical path-finding algorithm and the optimal strategy that chooses the global stochastic shortest path every time. We model the path-finding procedure as a Markov decision process and propose two online path-finding algorithms: Q SSP algorithm and SARSA SSP algorithm, both combined with specifically-devised average reward mechanism. We justify the convergence property and correctness of the proposed algorithms theoretically. Experiments conducted on two benchmark graphs illustrate the superior performance of the proposed Q SSP algorithm which outperforms the SARSA SSP algorithm and other competitive algorithms about the regret metric.

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