Prediction of Route Choosing Behavior Based on Genetic Algorithm Approach

Pop, Mădălin-Dorin; Proştean, Octavian; Proștean, Gabriela

doi:10.1109/ines.2018.8523917

Cited by 1 publication

(1 citation statement)

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“…Due to the constraints of the area of the attraction, tourists' queuing at the scenic spots becomes a very important concern, so it is recommended to score the most efficient walking distance to the scenic route. Pop et al [12] proposed a route choosing behavior method of crossroads based on the genetic algorithm, which uses a new fitness function computation method. Yu et al [13] recommended serialization routes for multiple attractions in a city on a macro-scale.…”

Section: Introductionmentioning

confidence: 99%

A Novel Multi-Objective and Multi-Constraint Route Recommendation Method Based on Crowd Sensing

et al. 2021

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Nowadays, people choose to travel in their leisure time more frequently, but fixed predetermined tour routes can barely meet people’s personalized preferences. The needs of tourists are diverse, largely personal, and possibly have multiple constraints. The traditional single-objective route planning algorithm struggles to effectively deal with such problems. In this paper, a novel multi-objective and multi-constraint tour route recommendation method is proposed. Firstly, ArcMap was used to model the actual road network. Then, we created a new interest label matching method and a utility function scoring method based on crowd sensing, and constructed a personalized multi-constraint interest model. We present a variable neighborhood search algorithm and a hybrid particle swarm genetic optimization algorithm for recommending Top-K routes. Finally, we conducted extensive experiments on public datasets. Compared with the ATP route recommendation method based on an improved ant colony algorithm, our proposed method is superior in route score, interest abundance, number of POIs, and running time.

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Section: Introductionmentioning

confidence: 99%