Reliable network design under supply uncertainty with probabilistic guarantees

Szeto, W.Y.; Wang, Anny B.

doi:10.1080/23249935.2016.1154625

Cited by 13 publications

(5 citation statements)

References 70 publications

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“…Tourists start from the starting point t S and visit the scenic spots. When the cost of sightseeing in each scenic spot is confirmed and fixed, the cost on the way is an important criterion to determine the quality of the tourist routes [7,8]. Therefore, the problem is transformed into finding the route with the lowest cost based on the given p number of scenic spots.…”

Section: Tourism Route Planning Algorithm Based On the Probability Sc...mentioning

confidence: 99%

City administrative region tourism route recommendation algorithm based on naive Bayes data mining

Zhou

Tian²,

Liu³

et al. 2022

International Conference on Signal Processing and Communication Security (ICSPCS 2022)

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Aiming at the problems existing in the current tourism route planning, this paper constructs a city administrative region tourism route recommendation algorithm based on Naive Bayes data mining. The Naive Bayes classifier model is constructed through the attribute tag data of tourists' once-visited scenic spots, and then the scenic spots in the target city are classified. The scenic spots are sorted according to the weighted Bayesian probability value of each classification, so as to recommend the scenic spots with the highest probability value for tourists. Based on the scenic spots with the optimal probability values, this paper constructs a tourism route algorithm with the lowest cost. Combined with the different travel modes of tourists, it searches the city tourism routes with the lowest cost on traveling distance, time and fee. At the same time, it provides two tourism decision-making plans according to the actual needs of tourists. Experiment shows that the proposed algorithm can recommend the scenic spots with the highest weighted probability value and satisfy the needs of tourists, and the traveling cost on the searched tourism route is the lowest.

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Section: Tourism Route Planning Algorithm Based On the Probability Sc...mentioning

confidence: 99%

City administrative region tourism route recommendation algorithm based on naive Bayes data mining

Zhou

Tian²,

Liu³

et al. 2022

International Conference on Signal Processing and Communication Security (ICSPCS 2022)

View full text Add to dashboard Cite

show abstract

“…If one or certain ones of tourist sights in a tour route cannot conform to the interests and needs of the tourist, or if they cannot provide the expected tour experience for tourists, then this tour route will directly influence tourist's total feel and experience on the whole trip, decrease the satisfaction degree and finally negatively influence tourist's evaluation on the tourism city as well as its tourists sights. The long-term consequence will be the negative decisions of subsequent tourists [39][40][41][42]. Thus, tour route planning should firstly lay emphasis on precise tourist sight mining to ensure each one of the tourist sight in the tour route conforms to tourists' interests and needs.…”

Section: Optimal Tour Route Planning Algorithm Based On Interest Labelmentioning

confidence: 99%

Tour Route Planning Algorithm Based on Precise Interested Tourist Sight Data Mining

Zhou¹,

Sun

et al. 2020

IEEE Access

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Aimed at current problems in tour route planning, this research proposes a tour route planning algorithm based on the data mining of tourist sights based on their precise interest. It further studies on the aspect of tourist interests and needs, tourist sight feature attributes, data mining of tourist sights based on their precise interest and optimal tour route planning, and then designs and performs experiment to testify the feasibility and superiority of the algorithm. First, tourist interest and need label matrix and tourist sight feature attribute matrix are set up. Tourist sight attribute factor is formed by mining text encyclopedic data, and combining with other factors, tourist sight clustering algorithm is set up to output tourist sight clusters. Second, data mining algorithm of tourist sights based on their precise interest is set up, which outputs the optimal tourist sights not only match the tourists' interests and needs but also optimally distribute around to produce the lowest travel costs. Third, based on precise interested tourist sights, combining with geographic information data, traffic information data and tourist sight information data, optimal tour route planning algorithm based on interest label is set up. It outputs maximum heap and complete binary tree with descending order interval motive values and corresponding edge clipping circle, which directly reflects the satisfaction content of each tour route on tourists. Tourist sight domain and geographic spatial data of Zhengzhou city are collected as basic data to design the experiment, and it testifies the algorithm's feasibility, effectiveness and practicalness. Meanwhile, the experiment brings in three commonly used route planning algorithms as control group to make comparison with the developed algorithm on the aspect of optimal route, motive value, motive value difference, algorithm performance and guide map,etc., which testifies that the developed algorithm has superiority to traditional algorithms. The research indicates that this developed algorithm solves the problems on tour route planning and it is an effective, feasible and practical algorithm.

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“…Furthermore, the travel behavior of users concerning transit lines and frequencies is modeled by a transit assignment sub-model, which is necessary to measure the performance of the network design strategy (Cancela et al 2015). Hence, the MTNDP is usually modeled as a bi-level programming problem (BLPP) (Szeto and Jiang 2014;Szeto et al 2015;Szeto and Wang 2016), where the upper-level problem optimizes the transit network based on feasibility constraints by the planner, and the lower-level problem describes the response of passengers to the designed scenarios by the upper-level.…”

Section: Literature Reviewmentioning

confidence: 99%

Multimodal transit network design in a hub-and-spoke network framework

Huang

Liu

et al. 2018

Transportmetrica A: Transport Science

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This study focuses on the reconfiguration of bus services in an urban area with a newly constructed rail system. A hub-and-spoke network framework is introduced for the multimodal transit system. The rail services are taken as the backbone, and the bus services (main bus lines and feeder bus lines) are reconfigured to better integrate with rail services forming the core of a Mobility as a Service (MaaS). A cluster-based approach is used for selecting hubs from rail stations. Bus stops are taken as non-hub nodes. Main bus lines are designed based on a heuristic line generation approach and feeder bus lines are developed by solving a traveling salesman problem. A bi-level programming model is proposed to determine frequencies of each mode and addressed by the artificial bee colony algorithm. The effectiveness of the proposed methods is illustrated by numerical examples and applied to the Mandl's benchmark compared with existing studies.

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Reliable network design under supply uncertainty with probabilistic guarantees

Cited by 13 publications

References 70 publications

City administrative region tourism route recommendation algorithm based on naive Bayes data mining

City administrative region tourism route recommendation algorithm based on naive Bayes data mining

Tour Route Planning Algorithm Based on Precise Interested Tourist Sight Data Mining

Multimodal transit network design in a hub-and-spoke network framework

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