Evolutionary minimization of traffic congestion

Böther, Maximilian; Schiller, Leon; Fischbeck, Philipp; Molitor, Louise; Krejca, Martin S.; Friedrich, Tobias

doi:10.1145/3449639.3459307

Cited by 5 publications

(2 citation statements)

References 29 publications

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“…The problem of traffic congestion on a road network occurs if the demand volume exceeds the road capacity, leading to important problems such as delays, increased fuel consumption, and additional pollution. Optimizing traffic flow hence represents one of the real-world challenges that can be modeled by using different strategies, such as classical optimization (see [12,27,150] for recent examples), 9 Different metrics can be used to define this time. A standard choice is TTS that is needed to observe an average probability X% of obtaining the ground state, denoted by TTS| X% .…”

Section: Traffic Flow Optimizationmentioning

confidence: 99%

Quantum annealing for industry applications: introduction and review

et al. 2022

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Quantum annealing is a heuristic quantum optimization algorithm that can be used to solve combinatorial optimization problems. In recent years, advances in quantum technologies have enabled the development of small- and intermediate-scale quantum processors that implement the quantum annealing algorithm for programmable use. Specifically, quantum annealing processors produced by D-Wave Systems have been studied and tested extensively in both research and industrial settings across different disciplines. In this paper we provide a literature review of the theoretical motivations for quantum annealing as a heuristic quantum optimization algorithm, the software and hardware that is required to use such quantum processors, and the state-of-the-art applications and proofs-of-concepts that have been demonstrated using them. The goal of our review is to provide a centralized and condensed source regarding applications of quantum annealing technology. We identify the advantages, limitations, and potential of quantum annealing for both researchers and practitioners from various fields.

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Section: Traffic Flow Optimizationmentioning

confidence: 99%

Quantum annealing for industry applications: introduction and review

et al. 2022

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“…Metaheuristics are stochastic methods that do not need a priori information on the function to solve. Even if these methods have no optimality guarantee, they have been well performing on different types of problems, from traffic congestion [3] to RNA design [19].…”

Section: Introductionmentioning

confidence: 99%

Automated Algorithm Selection for Radar Network Configuration

Renau,

Dreo,

Peres

et al. 2022

Preprint

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The configuration of radar networks is a complex problem that is often performed manually by experts with the help of a simulator. Different numbers and types of radars as well as different locations that the radars shall cover give rise to different instances of the radar configuration problem. The exact modeling of these instances is complex, as the quality of the configurations depends on a large number of parameters, on internal radar processing, and on the terrains on which the radars need to be placed. Classic optimization algorithms can therefore not be applied to this problem, and we rely on "trial-and-error" black-box approaches.In this paper, we study the performances of 13 black-box optimization algorithms on 153 radar network configuration problem instances. The algorithms perform considerably better than human experts. Their ranking, however, depends on the budget of configurations that can be evaluated and on the elevation profile of the location. We therefore also investigate automated algorithm selection approaches. Our results demonstrate that a pipeline that extracts instance features from the elevation of the terrain performs on par with the classical, far more expensive approach that extracts features from the objective function.

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Evolutionary Minimization of Traffic Congestion

Böther

Schiller

Fischbeck

et al. 2023

IEEE Trans. Evol. Computat.

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Traffic congestion is a major issue that can be solved by suggesting drivers alternative routes they are willing to take. This concept has been formalized as a strategic routing problem in which a single alternative route is suggested to an existing one. We extend this formalization and introduce the Multiple-Routes problem, which is given a start and destination and aims at finding up to 𝑛 different routes that the drivers strategically disperse over, minimizing the overall travel time of the system.Due to the NP-hard nature of the problem, we introduce the Multiple-Routes evolutionary algorithm (MREA) as a heuristic solver. We study several mutation and crossover operators and evaluate them on real-world data of Berlin, Germany. We find that a combination of all operators yields the best result, improving the overall travel time by a factor between 1.8 and 3, in the median, compared to all drivers taking the fastest route. For the base case 𝑛 = 2, we compare our MREA to the highly tailored optimal solver by Bläsius et al. [ATMOS 2020] and show that, in the median, our approach finds solutions of quality at least 99.69 % of an optimal solution while only requiring 40 % of the time.

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Evolutionary minimization of traffic congestion

Cited by 5 publications

References 29 publications

Quantum annealing for industry applications: introduction and review

Quantum annealing for industry applications: introduction and review

Automated Algorithm Selection for Radar Network Configuration

Evolutionary Minimization of Traffic Congestion

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