A*-guided heuristic for a multi-objective bus passenger Trip Planning Problem

Fournier, Sylvain M. R.; Hülse, Eduardo Otte; Pinheiro, Éder Vasco

doi:10.1007/s12469-019-00204-1

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…The trip planning problem (TPP) in this paper is the problem that finds the optimal route to visit a series of point-ofinterests (POIs) and hotels over multiple days [1]- [4]. For example, Sylejmani et al [1] presented a method that solves the trip planning problem using a heuristic algorithm based on tabu search; Saeki et al [2] presented a method for planning a multi-objective trip using antcolony optimization; Fournier et al [3] showed a method that solves the bus passenger trip planning problem using an A*-guided and Pareto dominance-based heuristic; Garcia et al [4] presented two different methods to solving the time-dependent team orienteering problem with time windows; Shuai et al [5] presented a method that solves multiple traveling salesman problems by applying an NSGA-II framework; He et al [6] presented a hybrid method based on tabu search and intratour optimization to solve the multiple traveling salesman problems.…”

Section: A Trip Planning Problemmentioning

confidence: 99%

Trip Planning Based on subQUBO Annealing

Noguchi,

Fukada,

Bao

et al. 2023

IEEE Access

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The trip planning problem (TPP) can be formulated as a combinatorial optimization problem that searches for the best route to visit a series of landmarks and hotels. Meanwhile, Ising machines have attracted attention due to their efficiency in solving combinatorial optimization problems. The Ising machines solve the combinatorial optimization problems by transforming the problems into quadratic unconstrained binary optimization (QUBO) models. However, the possible input QUBO size of current Ising machines is quite limited. Thus, it is hard to directly embed a large-scale TPP onto the current Ising machines. In this paper, we propose a novel subQUBO annealing method based on the combined variable selection method to solve the TPP. The proposed method finds a quasi-optimal solution to a large problem by repeatedly partitioning the original QUBO model into small subQUBOs that can be embedded onto the Ising machine. Specifically, to construct a subQUBO, we select variables from the original QUBO model, which have small deviation values. Further, we select variables randomly from the original QUBO model, so as not to fall into the local optimum. We have conducted an evaluation experiment using Ising machines on TPP and confirmed that the proposed method outperforms the state-of-the-art methods in terms of POI satisfaction and POI cost.

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Section: A Trip Planning Problemmentioning

confidence: 99%

Trip Planning Based on subQUBO Annealing

Noguchi,

Fukada,

Bao

et al. 2023

IEEE Access

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“…Their approach consists of the use of connected vehicle technologies and the implementation and an adaptive optimization model of signal synchronization. The authors of [38] are also interested in the optimization of transit based on AVL data.…”

Section: Automatic Vehicle Locationmentioning

confidence: 99%

Review of Transit Data Sources: Potentials, Challenges and Complementarity

Sarhani

Voß

et al. 2021

Sustainability

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Public transport has become one of the major transport options, especially when it comes to reducing motorized individual transport and achieving sustainability while reducing emissions, noise and so on. The use of public transport data has evolved and rapidly improved over the past decades. Indeed, the availability of data from different sources, coupled with advances in analytical and predictive approaches, has contributed to increased attention being paid to the exploitation of available data to improve public transport service. In this paper, we review the current state of the art of public transport data sources. More precisely, we summarize and analyze the potential and challenges of the main data sources. In addition, we show the complementary aspects of these data sources and how to merge them to broaden their contributions and face their challenges. This is complemented by an information management framework to enhance the use of data sources. Specifically, we seek to bridge the gap between traditional data sources and recent ones, present a unified overview of them and show how they can all leverage recent advances in data-driven methods and how they can help achieve a balance between transit service and passenger behavior.

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