Tourism Route Planning Research Articles

Research on the Application of Chinese GPT based Generative Artificial Intelligence AI in Tourist Consumption

Information technology has brought dramatic breakthrough to the tourism industry. The pre-trained generative transformer AI mata model utilizes intelligent technology to optimize tourism information search, which is the key to travel planning. Although its progress brings surprising experiences, there is still a gap between user expectations and experiences. This project focuses on the Chinese market and examines the experience of tourists using relevant AI models in tourism route planning. It provides reference for enterprises to improve models and optimize services based on AI models, and also provides guidance for tourists to choose and improve the effectiveness of model use.

Design of Travel Route Planning Model Based on Deep Learning

Tourism route planning is affected by the factors such as tourism destination and tourists’ preference, which leads to poor automatic matching of routes. A mathematical model of tourism route planning based on deep learning is proposed. Under the condition of total route constraints, according to the historical tourism preference information and prior information of tourists, a big data model for the distribution of personalized characteristics of tourists is established, spatial constraints and time constraints parameters are input, and the feature matching of geographic location information and tourist interest parameter information is carried out by using the deep learning method, and the statistical feature quantity of the parameters of the tourism route planning model is extracted. Under the control of the deep learning and geographic information data set, Carry out multi-constraint and multi-objective hierarchical analysis of tourists and tourism destinations, and realize the optimization design of tourism route planning algorithm. The simulation results show that the accuracy of this method is high and the deviation is small. It improves the satisfaction level of tourists and helps users complete a series of visual analysis tasks such as route mining, route planning and destination analysis.

Retraction Note: GIS positioning based on optical sensing detection and simulation of tourism route planning in rural tourism scenic areas

Retraction Note: GIS positioning based on optical sensing detection and simulation of tourism route planning in rural tourism scenic areas

Tourists’ behavioural intentions to use ChatGPT for tour route planning: an extended TAM model including rational and emotional factors

ABSTRACT This study incorporates perceived coolness and perceived enjoyment as emotional factors into Technology Acceptance Model (TAM), which traditionally focuses on rational factors, to comprehensively understand how tourists develop usage intentions of ChatGPT for tour route planning. Structural equation modeling was utilized to analyse 491 online questionnaires. Findings support that perceived information quality is an antecedent factor of perceived ease of use and perceived usefulness. Consequently, perceived ease of use and perceived usefulness significantly influence tourists’ attitudes towards using ChatGPT for tour route planning, while perceived usefulness directly influences tourists’ behavioural intentions regarding ChatGPT usage. Moreover, perceived coolness directly affects tourists’ attitudes towards ChatGPT usage and indirectly affects their behavioural intentions through perceived enjoyment and attitudes. The primary contribution of this research lies in the integration of perceived coolness and perceived enjoyment into the traditional TAM, thus establishing a new rational-emotional extended TAM framework. This advancement provides a more comprehensive understanding of tourists’ adoption of generative AI tools such as ChatGPT for tour route planning.

RETRACTED ARTICLE: GIS positioning based on optical sensing detection and simulation of tourism route planning in rural tourism scenic areas

RETRACTED ARTICLE: GIS positioning based on optical sensing detection and simulation of tourism route planning in rural tourism scenic areas

Construction of a coupled evaluation index system of rural habitat environment and rural tourism in China based on multi-objective optimization algorithm

Abstract This paper presents a coupled evaluation of rural habitat environment as well as rural tourism in China based on a multi-objective optimization algorithm. Firstly, the ANN model is used as the three objective evaluation function values of CVBEC, LCC and LCCO2 as the dependent variables, and the NSGA-II algorithm is introduced as the optimization algorithm for the multi-objective optimization of rural architectural design. Secondly, based on the ANN-NSGA-II algorithm, an improvement strategy is proposed for the rural building environment algorithm, and the multi-objective discrete particle swarm algorithm with a dominant ranking number is applied to rural tourism route planning. Then the coupled evaluation system of rural habitat environment and rural tourism is constructed by first and second-level indicators. Finally, the coordination degree and spatial autocorrelation analysis of the multi-system coupling relationship are conducted for the coupling evaluation system. The study shows that the coordinated development degree P value increased from 0.02 in 2017 to 0.83 in 2022, Moran’s I index of living environment and ecological environment system is greater than 0, and the Z value is greater than 1.63.

Optimization design of waterfront tour routes under thermal comfort assessment in summer—a case study of West Lake in Hangzhou, China

ABSTRACT The existing research on waterfront tour route planning suggests that thermal comfort assessment has a direct impact on people’s outdoor tour experiences. It contributes to understanding the thermal comfort regulation mechanisms of landscape nodes and the scientific planning of tour routes. However, the research in this area is relatively limited and faces complex issues in data processing and analysis. This study takes the West Lake waterfront tour route in Hangzhou, China, as an example. Through empirical research, it validates the feasibility of tour route planning guided by thermal comfort. It employs field surveys, on-site measurements, and data analysis methods primarily based on the Rayman software to explore the relationship between urban outdoor waterfront spaces and microclimates. The study reveals the following findings: (i) Extensive water bodies surrounding an area positively regulate local microclimates. The closer one is to the water and the more water surrounds the area, the stronger the microclimate effect. (ii) Factors such as spatial openness, waterfront orientation, prevailing monsoons, etc. all have certain impacts on the microclimates of the lake and lakeside spaces. (iii) Air temperature has significant variability in its impact on human thermal comfort, with different PET (Physiological Equivalent Temperature) values at each measurement point during different time periods. The study combines the actual human thermal comfort conditions at eight measurement points around the lake with existing tour routes. It proposes two practical tour route optimization designs based on real tour microclimate factors. These designs employ thermal comfort assessment methods to effectively enhance and improve human thermal comfort during tours. This research carries social value and provides new insights for mitigating urban waterfront space microclimates, enhancing tour comfort, and improving landscape environments.

Analysis of the application of path finding system based on efficiency improvement in smart tourism

With the proposal and development of intelligent tourism destination, the problem of tourism route planning has become a hot topic in tourism research. This study proposes an intelligent tourism route planning model based on efficiency improvement for Guangxi tourism route planning. In order to improve the efficiency of the model, this paper combines ant colony algorithm with genetic algorithm and introduces smoothing factor and guiding factor, and obtains genetic algorithm (genetic algorithm-path smoothing ant colony optimization) with changing probability. Experimental results show that the convergence speed of the algorithm is fast, and it can converge in 78 iterations, and the operation efficiency is higher. The result error of the proposed algorithm is 1.39, which is smaller than that of other comparative algorithms. In the actual application effect evaluation, the average satisfaction of the planned route of the intelligent tourism route planning model based on efficiency improvement is 15.38, which is higher than that of the traditional genetic algorithm and simulated annealing algorithm. It shows that the model can not only efficiently plan the best travel route, but also comprehensively consider the whole trip of tourists, so that tourists can have a better travel experience. The research provides an intelligent route planning method that is more efficient and more in line with tourists' needs for tourism development.

Optimizing Tour Route Planning for Old Streets: The Case of Taichung Nantun Old Street in Taiwan

It is very important for tourists to optimize the tour route planning in advance. This article presents a quantifying and planning method for the optimal tour route of an old street based on an algebraic matrix technique. A quantified structure matrix (QISM) that combines the quantifying evaluation method and the interpretive structural model (ISM) is established. The proposed QISM is a Boolean matrix that can quantify the dependency and interaction strength between two tour spots and optimize tour route planning. Clustering and tearing algorithms are utilized to restructure a QISM, allowing the interdependent relationships within tour spots in the route planning process to be obtained. A systematic clustering method that is efficient and yet flexible is developed for tour route planning based on the transitive rule. The proposed QISM can enhance the performance of tour route planning management by reducing visit time, offering tour route variety, and raising tour satisfaction. A case study of Taichung Nantun Old Street is employed to illustrate the proposed method. We hope the results of this study can satisfy the needs of tourists and promote the development of old street sightseeing.

Study of Optimization of Tourists' Travel Paths by Several Algorithms

The purpose of this paper is to optimize the tourism path to make the distance shorter. The article first constructed a model for tourism route planning and then used particle swarm optimization (PSO), genetic algorithm (GA), and ant colony algorithms to solve the model separately. Finally, a simulation experiment was conducted on tourist attractions in the suburbs of Taiyuan City to compare the path optimization performance of the three algorithms. The three path optimization algorithms all converged during the process of finding the optimal path. Among them, the ant colony algorithm exhibited the fastest and most stable convergence, resulting in the smallest model fitness value. The travel route obtained through the ant colony algorithm had the shortest distance, and this algorithm required minimal time for optimization. The novelty of this article lies in the enumeration and description of various algorithms used for optimizing travel paths, as well as the comparison of three different travel route optimization algorithms through simulation experiments. Doi: 10.28991/HIJ-2023-04-02-012 Full Text: PDF

Tourism Route Recommendation Based on A Multi-Objective Evolutionary Algorithm Using Two-Stage Decomposition and Pareto Layering

Tourism route planning is widely applied in the smart tourism field. The Pareto-optimal front obtained by the traditional multi-objective evolutionary algorithm exhibits long tails, sharp peaks and disconnected regions problems, which leads to uneven distribution and weak diversity of optimization solutions of tourism routes. Inspired by these limitations, we propose a multi-objective evolutionary algorithm for tourism route recommendation (MOTRR) with two-stage and Pareto layering based on decomposition. The method decomposes the multi-objective problem into several subproblems, and improves the distribution of solutions through a two-stage method. The crowding degree mechanism between extreme and intermediate populations is used in the two-stage method. The neighborhood is determined according to the weight of the subproblem for crossover mutation. Finally, Pareto layering is used to improve the updating efficiency and population diversity of the solution. The two-stage method is combined with the Pareto layering structure, which not only maintains the distribution and diversity of the algorithm, but also avoids the same solutions. Compared with several classical benchmark algorithms, the experimental results demonstrate competitive advantages on five test functions, hypervolume (HV) and inverted generational distance (IGD) metrics. Using the experimental results of real scenic spot datasets from two famous tourism social networking sites with vast amounts of users and large-scale online comments in Beijing, our proposed algorithm shows better distribution. It proves that the tourism routes recommended by our proposed algorithm have better distribution and diversity, so that the recommended routes can better meet the personalized needs of tourists.

Research on Route Planning of Red Tourist Attractions in Guangzhou Based on Ant Colony Algorithm

The development of red cultural resources in Guangzhou is scattered, and there are problems such as many but not precise and repeated development. In this paper, the tourism group is divided into two kinds of red study groups and ordinary tourism tourists, based on the ant colony algorithm, with the shortest path as the objective function, for ordinary tourists this paper selects the top ten red attractions in Guangzhou combined with the surrounding characteristic attractions, and uses MATLAB programming to plan the tourism route; for red study groups, this paper divides the red attractions in Guangzhou into municipal districts, and does not consider the surrounding attractions in the same way to plan The best path is planned in the same way. In this way, we can promote the dissemination of red history and culture, help the development of red tourism, and provide some reference significance for red tourism route planning.

Construction of cultural heritage evaluation system and personalized cultural tourism path decision model: An international historical and cultural city

World cultural heritage (WHS) is of great significance to promoting the sustainable development of cities, and history culture is very important to achieve the goal of sustainable development. The purpose of this paper is to construct a cultural heritage evaluation system and to explore and optimize tourist routes. We take cultural heritage sites as the research object, and comprehensively use Analytic Hierarchy Process (AHP), Delphi method and fuzzy mathematics to establish a cultural heritage tourism resource evaluation system. According to the evaluation results and questionnaires, a decision-making system for cultural tourism route planning is established, and additional sites of Qin culture are found, and a complete Qin culture route is established to provide data and theoretical basis for Xi'an's perfect cultural tourism route. The traveling salesman problem is the most basic path problem. In this study, the ant colony algorithm is used to improve the model combined with the actual problem of Xi'an tourist routes, and the optimal solution of the cultural parade route is obtained. Xi'an authorities and managers can strategically use these findings to promote Xi'an as a green destination WHS.

TriPlan: an interactive visual analytics approach for better tourism route planning.

Continuous research and development of novel tourism routes is necessary for tourism service providers to improve the tourist experience and industrial competitiveness. However, the route planning is cumbersome due to the time-consuming, extensive, and costly field study. Most of the existing route planning studies focus on recommending tourism routes for users based on attraction characteristics or tourist behavior features, which are generally unexplainable due to the black-box approaches they use. Other solutions allow users to customize itineraries through an interactive interface but often lack guidance from the aspect of route evaluation or destination image perception. In this paper, we thoroughly discuss the requirements and design tasks with domain experts and propose TriPlan, an interactive visual analytics system that provides intuitive planning guidance for tourism product developers. We design and improve multiple coordinated visualizations to facilitate analysis from the perspectives of overall route pattern and individual destination image. We also develop a hierarchical planning view to display the structural information of a plan. In addition, we introduce an automatic route optimization algorithm and multiple interactions to assist users in optimizing and adjusting the itineraries. Finally, we evaluate the usability and effectiveness of our system through three case studies and quantitative and qualitative interviews with the domain experts on real-world datasets.Graphical abstract Electronic supplementary materialThe online version of this article (10.1007/s12650-022-00861-8) contains supplementary material, which is available to authorized users.

Improved On-Demand Travel Route Planning Model with Interest Fields.

Intelligent tourism route planning is an important element of smart tourism, and the current tourism route planning has problems such as strong subjectivity and low personalization considering tourists' interests. To solve the problems of current tourism route planning, an improved interest field travel route planning model is proposed. Firstly, an intelligent interest field extraction model is established. Secondly, an improved greedy algorithm is designed to reduce the risk of missing the optimal solution, strengthen the local search capability, and improve the solution accuracy of the algorithm. The extracted routes of interest sites are planned, and a motivated iterative value output model is established. The experimental results demonstrate that the selected routes are shorter and less expensive than the traditional model. By iterating the actual data to obtain the iterative values of different tourist route motivations and the sequential guide map of attractions based on tourist interests, the optimal and suboptimal routes that satisfy the tourist motivation interests are analyzed. This model has strong feasibility and practical significance for smart tourism route planning.

Algorithm of Scenic Spot Tourism Route Planning Based on Convolution Neural Network

Based on convolution neural network, a tourism route planning method for scenic spots is proposed. The method of performing primitive trajectory adaptive learning is used to plan and design the tourism route nodes and path space. On this basis, the shortest distribution grid structure model of tourism routes is constructed. Using the visual servo mobile route optimization control method, the constraint parameters of tourism route planning are optimized. In order to avoid the crowded area of tourists and the shortest path as the planning standard, the path planning problem is regarded as a constraint problem. The convolution neural network is used to activate the constraint function with the shortest path, and the output planning results are calculated iteratively. The simulation results show that this method has good learning control ability and convergence performance and improves the reliability of scenic spot tourism route planning.

Analysis and Study on Intelligent Tourism Route Planning Scheme Based on Weighted Mining Algorithm

In order to study the intelligent tourism route planning scheme based on a weighted mining algorithm, firstly, a traveling salesman problem based on an improved weighted mining algorithm is proposed. Then, based on the traveling salesman problem, the constraints of travel time and driving time are added, and the mathematical model aiming at the minimum number of travel days is established. The improved weighted mining algorithm is used to solve the model: firstly, the scenic spots are divided into regions according to provinces and solved; then, the regions of some scenic spots are modified and solved again according to the solution results, and the specific itinerary is given; finally, based on the route planning problem and considering a variety of transportation modes, a double objective mathematical model with the optimal tourism cost and the best tourism experience is established. When solving, the double objective is reasonably transformed into a single objective, the model is solved by comprehensively considering the tourism cost and tourism experience, and the specific schedule is given according to the solution results.

Analysis of the Intelligent Tourism Route Planning Scheme Based on the Cluster Analysis Algorithm.

In view of the problems of the traditional cluster analysis algorithm such as strong dependence on the initial cluster center, the traditional k-means cluster analysis algorithm is improved and the experiment proves that the improved algorithm has a better clustering effect; in view of the problems of the traditional tourism route planning, the improved k-means cluster analysis algorithm is applied to the intelligent tourism route planning scheme design and an intelligent tourism planning scheme based on the cluster analysis algorithm is proposed; the tourists' preference metric is fully considered, and the experimental results show that the scheme has certain reasonableness and reference value.

Intelligent Planning of Tourist Routes Based on Cloud Computing and Marching Algorithm

In order to improve the effect of modern tourism route planning, this article combines the cloud computing advancement algorithm to construct a tourism route intelligent planning system and combines the current people's tourism needs to carry out intelligent tourism route planning. Based on the fast forward algorithm of the program function equation, this article uses the fast forward algorithm to solve the discrete nonlinear program function equation and directly obtain the cloud computing data. In addition, the self-intersection phenomenon of the wave front must be considered when describing the characteristics of the cloud computing information field by using the process of the equivalent cloud computing information front expanding to the surrounding. Finally, this article constructs the intelligent system structure. The research results show that the intelligent travel route planning system based on cloud computing and marching algorithm proposed in this article can play an important role in modern smart tourism.

Tourism route optimization based on improved knowledge ant colony algorithm

With the rapid development of tourism in the economy, popular demand for tourism also increases. Unreasonable distribution arises a series of problems such as reduction of tourist satisfaction and decrease of the income in tourist attractions. Based on consideration of tourism route planning, a mathematical model which takes the maximization of the overall satisfaction of all tourist groups as the objective function is established by taking the age and preferences of tourists, the upper limits of the tourist carrying capacity in various tourism routes, etc. as constraints. It aims to maximize income in tourist attractions while improving tourist satisfaction. Based on the tourist data of a travel agency, the statistical ideas of hierarchical clustering and random sampling are utilized to process the acquired data to obtain the simulation examples in the article. Aiming at this model, a knowledge-based hybrid ant colony algorithm is designed. On this basis, the mechanism of bacterial foraging algorithm is introduced. It improves the performance of the algorithm and avoids the generation of local optimal solution. At the same time, two knowledge models are in addition to improve the solution quality of the algorithm. Typical simulation indicates that the improved ant colony algorithm can find the optimal solution at a higher efficiency when solving the tourism route planning problem. The model can also satisfy the economic benefit of enterprises and achieves favorable path optimization effect under different optional routes, thus further verifying the effect liveness of the model.