Practical Route Planning Under Delay Uncertainty: Stochastic Shortest Path Queries

Lim, Sejoon; Sommer, Christian; Nikolova, Evdokia; Rus, Daniela

doi:10.15607/rss.2012.viii.032

Cited by 31 publications

(35 citation statements)

References 30 publications

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“…It is shown that the utility function has to be either affine linear or exponential; otherwise, optimal substructure of the problem is not guaranteed. Nikolova et al [20] and Lim et al [14], [15] further extend the work and focus on stochastic road networks. In [27], Wu et al propose an approach to model risk-taking behavior based on the theory of stochastic dominance(SD), and use it to find optimal paths for different utility functions.…”

Section: Related Workmentioning

confidence: 97%

“…Most of research works done in this area use a meanrisk model [15], [21], [19] which combines the mean and variance of the travel time as a single linear objective function that needs to be optimized. Nevertheless, choosing appropriate coefficients for such a linear objective function is quite tricky and often heuristically done via experiments.…”

Section: Introductionmentioning

confidence: 99%

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DEPART: Dynamic Route Planning in Stochastic Time-Dependent Public Transit Networks

Dahlmeier³

et al. 2015

2015 IEEE 18th International Conference on Intelligent Transportation Systems

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Abstract-While providing intelligent urban transportation services for commuters is one of the key enablers for developing smart cities, existing route planners mainly rely on static schedules and hence fall short in dealing with uncertain and timedependent traffic situations. In this paper, by leveraging a large set of historical travel smart card data, we propose a method to build a stochastic time-dependent model for public transit networks. In addition, we develop DEPART 1 -a dynamic route planner that takes the stochastic models of both bus travel time and waiting time into account and optimizes both the speediness and reliability of routes. Experiments on real bus data set for the entire city confirm the quality and accuracy of the routes returned by DEPART in comparison to state-of-the-art route planners.

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Section: Related Workmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

DEPART: Dynamic Route Planning in Stochastic Time-Dependent Public Transit Networks

Dahlmeier³

et al. 2015

2015 IEEE 18th International Conference on Intelligent Transportation Systems

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“…An overview of the shortest path problem with probabilistic edge weights is found in [18]. In [19] a stochastic shortest path algorithm is used to solve a route planning problem in the presence of uncertain traffic delays. [20] uses expectations of link delays to solve a network routing problem.…”

Section: Delay Tolerant Networkingmentioning

confidence: 99%

“…Existing algorithms for KShP (classical version) include a modified Bellman-Ford algorithm that stores the top K shortest paths at each pass instead of storing only the shortest (JGraphT library), and Yen's algorithm [23]. The stochastic shortest path problem (KSfP with K = 1) is a non-convex combinatorial problem [19]. A dynamic programming approach is incorrect since sub-paths of optimal paths are not optimal.…”

Section: K-safest Paths Problem (Ksfp)mentioning

confidence: 99%

Raven: Energy aware QoS control for DRNs

Chenji

Smith

Stoleru

et al. 2013

2013 IEEE 9th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)

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Disaster Response Networks (DRNs) are disruption tolerant networks designed to deliver mission critical data during disaster recovery, while operating with limited energy resources. While Quality of Service is desired, it is difficult to offer guarantees because of the unpredictable nature of mobility in such DRNs. The variance of the packet delivery delay (PDV, more commonly called jitter), an important QoS metric which in DRNs is measured in tens of minutes instead of milliseconds, has not been sufficiently addressed in recent research. Smartphones used by first responders generate large data workloads, causing the PDV to further degrade. Reducing packet replication at these workloads will lower energy consumption, but reduces the packet delivery ratio (PDR). The complex interplay between these QoS metrics remains unclear, making their control difficult. We present Raven, a routing protocol for DRNs that offers control over QoS, especially the PDV. Stochastic graph theory which deals with probabilistic edge weights having a mean and variance is used to model mobility in the disaster area. A stochastic version of the K-Shortest Paths algorithm routes data over multiple paths simultaneously. Raven has been thoroughly evaluated in simulation using realistic settings. The dynamics between performance and energy consumption is analyzed mathematically, and its control is demonstrated.

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“…The conventional paradigm for solving the problem is to assign weights to edges and then to sum up the weights of a path's edges to compute the cost of the path [4,10,12,21,22,25]. This conventional paradigm is inadequate in terms of accuracy and efficiency.…”

Section: Introductionmentioning

confidence: 99%

Path cost distribution estimation using trajectory data

et al. 2016

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With the growing volumes of vehicle trajectory data, it becomes increasingly possible to capture time-varying and uncertain travel costs in a road network, including travel time and fuel consumption. The current paradigm represents a road network as a weighted graph; it blasts trajectories into small fragments that fit the underlying edges to assign weights to edges; and it then applies a routing algorithm to the resulting graph. We propose a new paradigm, the hybrid graph, that targets more accurate and more efficient path cost distribution estimation. The new paradigm avoids blasting trajectories into small fragments and instead assigns weights to paths rather than simply to the edges.We show how to compute path weights using trajectory data while taking into account the travel cost dependencies among the edges in the paths. Given a departure time and a query path, we show how to select an optimal set of weights with associated paths that cover the query path and such that the weights enable the most accurate joint cost distribution estimation for the query path. The cost distribution of the query path is then computed accurately using the joint distribution. Finally, we show how the resulting method for computing cost distributions of paths can be integrated into existing routing algorithms. Empirical studies with substantial trajectory data from two different cities offer insight into the design properties of the proposed method and confirm that the method is effective in real-world settings.

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Practical Route Planning Under Delay Uncertainty: Stochastic Shortest Path Queries

Cited by 31 publications

References 30 publications

DEPART: Dynamic Route Planning in Stochastic Time-Dependent Public Transit Networks

DEPART: Dynamic Route Planning in Stochastic Time-Dependent Public Transit Networks

Raven: Energy aware QoS control for DRNs

Path cost distribution estimation using trajectory data

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