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TimeDependent Route Planning
 Robust and Online LargeScale Optimization, LNCS
, 2009
"... Abstract. In this paper, we present an overview over existing speedup techniques for timedependent route planning. Apart from only explaining each technique one by one, we follow a more systematic approach. We identify basic ingredients of these recent techniques and show how they need to be augmen ..."
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Cited by 44 (17 self)
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Abstract. In this paper, we present an overview over existing speedup techniques for timedependent route planning. Apart from only explaining each technique one by one, we follow a more systematic approach. We identify basic ingredients of these recent techniques and show how they need to be augmented to guarantee correctness in timedependent networks. With the ingredients adapted, three efficient speedup techniques can be set up: CoreALT, SHARC, and Contraction Hierarchies. Experiments on realworld data deriving from road networks and public transportation confirm that these techniques allow the fast computation of timedependent shortest paths. 1
Alternative Routes in Road Networks
"... Abstract. We study the problem of finding good alternative routes in road networks. We look for routes that are substantially different from the shortest path, have small stretch, and are locally optimal. We formally define the problem of finding alternative routes with a single via vertex, develop ..."
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Cited by 10 (3 self)
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Abstract. We study the problem of finding good alternative routes in road networks. We look for routes that are substantially different from the shortest path, have small stretch, and are locally optimal. We formally define the problem of finding alternative routes with a single via vertex, develop efficient algorithms for it, and evaluate them experimentally. Our algorithms are efficient enough for practical use and compare favorably with previous methods in both speed and solution quality. 1
Practical Route Planning Under Delay Uncertainty: Stochastic Shortest Path Queries
"... 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 shortestpath queries ..."
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Cited by 2 (0 self)
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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 shortestpath 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, precomputes a data structure in quasilinear time such that approximate stochastic shortestpath queries can be answered in polylogarithmic time (actual worstcase bounds depend on the probabilistic model). Our main experimental results are twofold: (i) we provide methods to extract traveltime 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. I.
Efficient route compression for hybrid route planning
 In MedAlg
, 2012
"... Abstract. We describe an algorithmic framework for lossless compression of route descriptions. This is useful for hybrid route planning where routes are computed by a server and then transmitted to a client device in a car using some mobile radio communication where bandwidth may be low. Compressed ..."
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Abstract. We describe an algorithmic framework for lossless compression of route descriptions. This is useful for hybrid route planning where routes are computed by a server and then transmitted to a client device in a car using some mobile radio communication where bandwidth may be low. Compressed routes are represented by only a few via nodes which are the connection points when the route is decomposed into unique optimal segments. To reconstruct the route efficiently a client device needs basic but fast route planning capability. Contraction hierarchies make this approach fast enough for practice: Compressing takes only a few milliseconds. And previous experiments suggest that a client can decompress each route segment virtually instantaneously. So, as the segments can be decompressed successively while driving, it is not likely that the driver experiences any delay except for the time needed by the mobile communication. 1
Customizable Route Planning in Road Networks
, 2013
"... We propose the first routing engine for computing driving directions in largescale road networks that satisfies all requirements of a realworld production system. It supports arbitrary metrics (cost functions) and turn costs, enables realtime queries, and can incorporate a new metric in less th ..."
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We propose the first routing engine for computing driving directions in largescale road networks that satisfies all requirements of a realworld production system. It supports arbitrary metrics (cost functions) and turn costs, enables realtime queries, and can incorporate a new metric in less than a second, which is fast enough to support realtime traffic updates and personalized cost functions. The amount of metricspecific data is a small fraction of the graph itself, which allows us to maintain several metrics in memory simultaneously. The algorithm is the core of the routing engine currently in use by Bing Maps.
SpeedConsumption Tradeoff for Electric Vehicle Route Planning *
"... Abstract We study the problem of computing routes for electric vehicles (EVs) in road networks. Since their battery capacity is limited, and consumed energy per distance increases with velocity, driving the fastest route is often not desirable and may even be infeasible. On the other hand, the ener ..."
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Abstract We study the problem of computing routes for electric vehicles (EVs) in road networks. Since their battery capacity is limited, and consumed energy per distance increases with velocity, driving the fastest route is often not desirable and may even be infeasible. On the other hand, the energyoptimal route may be too conservative in that it contains unnecessary detours or simply takes too long. In this work, we propose to use multicriteria optimization to obtain Pareto sets of routes that trade energy consumption for speed. In particular, we exploit the fact that the same road segment can be driven at different speeds within reasonable intervals. As a result, we are able to provide routes with low energy consumption that still follow major roads, such as freeways. Unfortunately, the size of the resulting Pareto sets can be too large to be practical. We therefore also propose several nontrivial techniques that can be applied online at query time in order to speed up computation and filter insignificant solutions from the Pareto sets. Our extensive experimental study, which uses a realworld energy consumption model, reveals that we are able to compute diverse sets of alternative routes on continental networks that closely resemble the exact Pareto set in just under a secondseveral orders of magnitude faster than the exhaustive algorithm.
Heuristic Contraction Hierarchies with Approximation Guarantee
"... We present a new heuristic pointtopoint shortest path algorithm based on contraction hierarchies (CH). Given an ε ≥ 0, we can prove that the length of the path computed by our algorithm is at most (1 + ε) times the length of the optimal (shortest) path. Exact CH is based on node contraction: remov ..."
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We present a new heuristic pointtopoint shortest path algorithm based on contraction hierarchies (CH). Given an ε ≥ 0, we can prove that the length of the path computed by our algorithm is at most (1 + ε) times the length of the optimal (shortest) path. Exact CH is based on node contraction: removing nodes from a network and adding shortcuts to preserve shortest path distances. Our heuristic CH tries to avoid adding shortcuts even when a replacement path is (1 + ε) times longer. However, we cannot avoid all such shortcuts, as we need to ensure that errors do not stack. Combinations with goaldirected techniques bring further speedups.
ZAlternative Routes in Road Networks
"... We study the problem of finding good alternative routes in road networks. We look for routes that are substantially different from the shortest path, have small stretch, and are locally optimal. We formally define the problem of finding alternative routes with a single via vertex, develop efficient ..."
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We study the problem of finding good alternative routes in road networks. We look for routes that are substantially different from the shortest path, have small stretch, and are locally optimal. We formally define the problem of finding alternative routes with a single via vertex, develop efficient algorithms for it, and evaluate them experimentally. Our algorithms are efficient enough for practical use and compare favorably with previous methods in both speed and solution quality.