Core Routing on Dynamic Time-Dependent Road Networks

Delling, Daniel; Nannicini, Giacomo

doi:10.1287/ijoc.1110.0448

Cited by 42 publications

(24 citation statements)

References 23 publications

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“…A similar space-saving approach is used by Core-ALT [40,88]. It first computes an overlay graph for the core graph, a (small) subset (e. g., 1 %) of vertices (which remain after "unimportant" ones are contracted), then computes landmarks for the core vertices only.…”

Section: Combinationsmentioning

confidence: 99%

Route Planning in Transportation Networks

Bast

Delling

Goldberg

et al. 2016

Lecture Notes in Computer Science

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461

388

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We survey recent advances in algorithms for route planning in transportation networks. For road networks, we show that one can compute driving directions in milliseconds or less even at continental scale. A variety of techniques provide different trade-offs between preprocessing effort, space requirements, and query time. Some algorithms can answer queries in a fraction of a microsecond, while others can deal efficiently with real-time traffic. Journey planning on public transportation systems, although conceptually similar, is a significantly harder problem due to its inherent time-dependent and multicriteria nature. Although exact algorithms are fast enough for interactive queries on metropolitan transit systems, dealing with continent-sized instances requires simplifications or heavy preprocessing. The multimodal route planning problem, which seeks journeys combining schedule-based transportation (buses, trains) with unrestricted modes (walking, driving), is even harder, relying on approximate solutions even for metropolitan inputs. *

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Section: Combinationsmentioning

confidence: 99%

Route Planning in Transportation Networks

Bast

Delling

Goldberg

et al. 2016

Lecture Notes in Computer Science

Self Cite

461

388

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show abstract

“…It includes some of the concepts we used in our research, such as the modelling of dynamic travel times, the augmented algorithm of Dijkstra, and so on. A number of speed-up techniques for dynamic shortest path routing, such as hierarchical routing [4,5] and bidirectional A* routing [6], have been proposed. Nevertheless, since we are routing both dynamically and stochastically, these cannot be directly applied in our system.…”

Section: Related Workmentioning

confidence: 99%

“…Subsequently, the temporary set is initialised with a singleton containing the origin node (line [3]). As long as the destination node is not permanent, that is, as long as its label is the infinity distribution or it is an element of the temporary set (line [4]), the following steps are executed. First, the node with the smallest label x is removed from the temporary set (line [5]).…”

Section: Routing Algorithmmentioning

confidence: 99%

Dynamic and stochastic routing for multimodal transportation systems

Demeyer¹,

Audenaert²,

Pickavet³

et al. 2014

IET Intelligent Transport Systems

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-We present a case study of a multimodal routing system that takes into account both dynamic and stochastic travel time information. A multimodal network model is presented that makes it possible to model the travel time information of each transportation mode differently.This travel time information can either be static or dynamic, or either deterministic or stochastic. Next to this, a Dijkstra-based routing algorithm is presented that deals with this variety of travel time information in a uniform way. This research focuses on a practical implementation of the system, which means that a number of assumptions were made, like, for example, the modeling of the stochastic distributions, comparing these distributions, etc. A tradeoff had to be made between the performance of the system and the accuracy of the results.Experiments have shown that our system produces realistic routes in a short amount of time. It is demonstrated that routing dynamically indeed results in a travel time gain in comparison to routing statically. By making use of the additional stochastic travel time information even better (i.e. faster) and more reliable routes can be calculated. Moreover, it is shown that routing in the 2 multimodal network may have its advantages over routing in a unimodal network, especially during rush hours.

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“…The solving performance had been significantly improved in this way. Delling and Nannicini [22] extended the heuristic algorithm of solving the pointto-point shortest path to the dynamic network and obtained the efficient algorithm by selecting the subnetwork. Lin et al [23,24] proved that the shortest path problem of timedependent network was a NP-Hard problem and put forward the concept of shortest path stability from three aspects including length stability of shortest path, optimal solution stability, and stable subbranch.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Path Planning of Emergency Vehicles Based on Travel Time Prediction

Zhao

Guo

Duan

2017

Journal of Advanced Transportation

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The dynamic paths planning problem of emergency vehicles is usually constrained by the factors including time efficiency, resources requirement, and reliability of the road network. Therefore, a two-stage model of dynamic paths planning of emergency vehicles is built with the goal of the shortest travel time and the minimum degree of traffic congestion. Firstly, according to the dynamic characteristics of road network traffic, a polyline-shaped speed function is constructed. And then, based on the real-time and historical data of travel speed, a new kernel clustering algorithm based on shuffled frog leaping algorithm is designed to predict the travel time. Secondly, combined with the expected travel time, the traffic congestion index is defined to measure the reliability of the route. Thirdly, aimed at the problem of solving two-stage target model, a two-stage shortest path algorithm is proposed, which is composed of K-paths algorithm and shuffled frog leaping algorithm. Finally, based on the data of floating vehicles of expressway in Beijing, a simulation case is used to verify the above methods. The results show that the optimization path algorithm meets the needs of the multiple constraints.

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Core Routing on Dynamic Time-Dependent Road Networks

Cited by 42 publications

References 23 publications

Route Planning in Transportation Networks

Route Planning in Transportation Networks

Dynamic and stochastic routing for multimodal transportation systems

Dynamic Path Planning of Emergency Vehicles Based on Travel Time Prediction

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