Stochastic Motion Planning and Applications to Traffic

Lim, Sejoon; Balakrishnan, Hari; Gifford, David; Madden, Samuel; Rus, Daniela

doi:10.1007/978-3-642-00312-7_30

Cited by 33 publications

(50 citation statements)

References 9 publications

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“…As well as system-level traffic flow optimizations, socially motivated criteria have also been considered. Recent work on traffic planning explored optimizing a drivers's route subject to congestion [7]. Social optimum planning models for computing vehicle paths are presented in [14], [15].…”

Section: A Related Workmentioning

confidence: 99%

Markov-based redistribution policy model for future urban mobility networks

Volkov

Aslam

Rus

2012

2012 15th International IEEE Conference on Intelligent Transportation Systems

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Abstract-In this paper we present a Markov-based urban transportation model that captures the operation of a fleet of taxis in response to incident customer arrivals throughout the city. We consider three different evaluation criteria: (1) minimizing the number of transportation resources for urban planning; (2) minimizing fuel consumption for the drivers; and (3) minimizing customer waiting time increase the overall quality of service. We present a practical policy and evaluate it by comparing against the actual observed redistribution of taxi drivers in Singapore. We show through simulation that our proposed policy is stable and improves substantially upon the default unmanaged redistribution of taxi drivers in Singapore with respect to the three evaluation criteria.

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Section: A Related Workmentioning

confidence: 99%

Markov-based redistribution policy model for future urban mobility networks

Volkov

Aslam

Rus

2012

2012 15th International IEEE Conference on Intelligent Transportation Systems

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“…Nikolova et al [9] prove hardness results for a broad class of objective functions and provide pseudopolynomial algorithms. Lim et al [6] provide empirical results showing that the independence assumption of edge distributions does not affect the accuracy of the answer by too much. …”

Section: Problem Statement and Preliminariesmentioning

confidence: 99%

High-Performance Heuristics for Optimization in Stochastic Traffic Engineering Problems

Nikolova

2010

Large-Scale Scientific Computing

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Abstract. We consider a stochastic routing model in which the goal is to find the optimal route that incorporates a measure of risk. The problem arises in traffic engineering, transportation and even more abstract settings such as task planning (where the time to execute tasks is uncertain), etc. The stochasticity is specified in terms of arbitrary edge length distributions with given mean and variance values in a graph. The objective function is a positive linear combination of the mean and standard deviation of the route. Both the nonconvex objective and exponentially sized feasible set of available routes present a challenging optimization problem for which no efficient algorithms are known. In this paper we evaluate the practical performance of algorithms and heuristic approaches which show very promising results in terms of both running time and solution accuracy.

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“…We build on prior work on stochastic path planning [15,19,20], which introduces stochastic path planning algorithms in this model. This prior work presents novel provably correct algorithms that are well suited for planning the path of a vehicle on small-scale graphs; however, the computational complexity of this prior suite of stochastic path planning algorithms makes these algorithms impractical to use for real-time path planning on city-scale road networks.…”

Section: Introductionmentioning

confidence: 99%

“…Our Contributions: The algorithm in this paper improves the state of the art on stochastic path planning [18,19,20] and its application to traffic routing [15]. The running time of the state-of-the-art stochastic-shortest-path algorithm is approximately quadratic in the number of nodes, rendering it too slow for real-time performance on city-scale networks.…”

Section: Introductionmentioning

confidence: 99%

“…Our preprocessing algorithm runs in quasi-linear time and it does not depend on the origin-destination pair or on the deadline, which is the main theoretical challenge. Our query algorithm runs in sublinear time (whereas the algorithms in [15] run in polynomial time). The running time is polylogarithmic, offering exponential speedups over previous algorithms and allowing for almost instantaneous response times (see Lemma 1 and Theorem 3 for details).…”

Section: Introductionmentioning

confidence: 99%

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Practical Route Planning Under Delay Uncertainty: Stochastic Shortest Path Queries

Lim¹,

Sommer²,

Nikolova³

et al. 2012

Robotics: Science and Systems VIII

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Abstract-We describe an algorithm for stochastic path planning and applications to route planning in the presence of traffic delays. We improve on the prior state of the art by designing, analyzing, implementing, and evaluating data structures that answer approximate stochastic shortest-path queries. For example, our data structures can be used to efficiently compute paths that maximize the probability of arriving at a destination before a given time deadline.Our main theoretical result is an algorithm that, given a directed planar network with edge lengths characterized by expected travel time and variance, pre-computes a data structure in quasi-linear time such that approximate stochastic shortestpath queries can be answered in poly-logarithmic time (actual worst-case bounds depend on the probabilistic model).Our main experimental results are two-fold: (i) we provide methods to extract travel-time distributions from a large set of heterogenous GPS traces and we build a stochastic model of an entire city, and (ii) we adapt our algorithms to work for realworld road networks, we provide an efficient implementation, and we evaluate the performance of our method for the model of the aforementioned city.

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Stochastic Motion Planning and Applications to Traffic

Abstract: Abstract

Cited by 33 publications

References 9 publications

Markov-based redistribution policy model for future urban mobility networks

Markov-based redistribution policy model for future urban mobility networks

High-Performance Heuristics for Optimization in Stochastic Traffic Engineering Problems

Practical Route Planning Under Delay Uncertainty: Stochastic Shortest Path Queries

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