Tourism Route Planning Research Articles

Tour-Route-Recommendation Algorithm Based on the Improved AGNES Spatial Clustering and Space-Time Deduction Model

This study designed a tour-route-planning and recommendation algorithm that was based on an improved AGNES spatial clustering and space-time deduction model. First, the improved AGNES tourist attraction spatial clustering algorithm was created. Based on the features and spatial attributes, city tourist attraction clusters were formed, in which the tourist attractions with a high degree of correlation among attributes were gathered into the same cluster. It formed the precondition for searching tourist attractions that would match tourist interests. Using tourist attraction clusters, this study also developed a tourist attraction reachability model that was based on tourist-interest data and geospatial relationships to confirm each tourist attraction’s degree of correlation to tourist interests. A dynamic space-time deduction algorithm that was based on travel time and cost allowances was designed in which the transportation mode, time, and costs were set as the key factors. To verify the proposed algorithm, two control algorithms were chosen and tested against the proposed algorithm. Our results showed that the proposed algorithm had better results for tour-route planning under different transportation modes as compared to the controls. The proposed algorithm not only considered time and cost allowances, but it also considered the shortest traveling distance between tourist attractions. Therefore, the tourist attractions and tour routes that were suggested not only met tourist interests, but they also conformed to the constraint conditions and lowered the overall total costs.

GIS-Based Touring Route Planning for Forest Parks - Baiwangshan Forest Park as an Example

With the popularization of low-carbon concept, residents pay more and more attention to the demand of urban forest parks. In this essay, the GIS software based on remote sensing technology is used to analyze the elevation, slope and slope direction of Baiwangshan Forest Park and carry out superimposed data processing, followed by distance analysis of park nodes, and finally obtain different types of tour organization routes such as cultural experience, ecological civilization, sports and fitness, to realize the efficiency and rationality of forest park tour route design, improve the utilization rate of forest resources, and provide reference for future tour route planning of the same type of The tour route design of forest park is efficient and reasonable, and the utilization rate of forest resources is improved.

An improved ant colony optimization algorithm based on context for tourism route planning.

To solve the problem of one-sided pursuit of the shortest distance but ignoring the tourist experience in the process of tourism route planning, an improved ant colony optimization algorithm is proposed for tourism route planning. Contextual information of scenic spots significantly effect people's choice of tourism destination, so the pheromone update strategy is combined with the contextual information such as weather and comfort degree of the scenic spot in the process of searching the global optimal route, so that the pheromone update tends to the path suitable for tourists. At the same time, in order to avoid falling into local optimization, the sub-path support degree is introduced. The experimental results show that the optimized tourism route has greatly improved the tourist experience, the route distance is shortened by 20.5% and the convergence speed is increased by 21.2% compared with the basic algorithm, which proves that the improved algorithm is notably effective.

A Novel Urban Tourism Path Planning Approach Based on a Multiobjective Genetic Algorithm

One of the most important variables that leads to effective individual and group tours is the tourism route planning approach, which enables tourists to engage with tourism with ease, speed, and safety. However, current methods of designing tourist routes have some glitches, such as relying only on external objectives to find the best route. In this paper, a novel urban tourism path planning method based on a multiobjective genetic algorithm is proposed. The main goal of this paper is to enhance the accuracy of the genetic algorithm (GA) by adopting new parameters and selecting the optimal tourism path by combining external and internal tourist site potentials. Moreover, the GA and analytical hierarchy process (AHP) were used in our proposed approach to evaluate urban tourism route planning under multiple conflicting objectives. To visualize and execute the proposed approach, the geographic information system (GIS) environment was used. Our suggested approach has been applied to develop the tourist road network of Chengdu City in China. Compared with existing tourism path planning approaches, our proposed approach is more accurate and straightforward than other approaches used to choose routes.

A Thousand Words Express a Common Idea? Understanding International Tourists’ Reviews of Mt. Huangshan, China, through a Deep Learning Approach

Tourists’ experiential perceptions and specific behaviors are of importance to facilitate geographers’ and planners’ understanding of landscape surroundings. In addition, the potentially significant role of online user generated content (UGC) in tourism landscape research has only received limited attention, especially in the era of artificial intelligence. The motivation of the present study is to understand international tourists’ online reviews of Mt. Huangshan in China. Through a state-of-the-art natural language processing network (BERT) analyzing posted reviews across international tourists, our results facilitate relevant landscape development and design decisions. Second, the proposed analytic method can be an exemplified model to inspire relevant landscape planners and decision-makers to conduct future researches. Through the clustering results, several key topics are revealed, including international tourists’ perceptual image of Mt. Huangshan, tour route planning, and negative experience of staying.

Modified ALNS Algorithm for a Processing Application of Family Tourist Route Planning: A Case Study of Buriram in Thailand

This research presents a solution to the family tourism route problem by considering daily time windows. To find the best solution for travel routing, the modified adaptive large neighborhood search (MALNS) method, using the four destructions and the four reconstructions approach, is applied here. The solution finding performance of the MALNS method is compared with an exact method running on the Lingo program. As shown by various solutions, the MALNS method can balance travel routing designs, including when many tourist attractions are present in each path. Furthermore, the results of the MALNS method are not significantly different from the results of the exact method for small problem sizes. For medium and large problem sizes, the MALNS method shows a higher performance and a smaller processing time for finding solutions. The values for the average total travel cost and average travel satisfaction rating derived by the MALNS method are approximately 0.18% for a medium problem and 0.05% for a large problem, 0.24% for a medium problem, and 0.21% for a large problem, respectively. The values derived from the exact method are slightly different. Moreover, the MALNS method calculation requires less processing time than the exact method, amounting to approximately 99.95% of the time required for the exact method. In this case study, the MALNS algorithm result shows a suitable balance of satisfaction and number of tourism places in relation to the differences between family members of different ages and genders in terms of satisfaction in tour route planning. The proposed solution methodology presents an effective high-quality solution, suggesting that the MALNS method has the potential to be a great competitive algorithm. According to the empirical results shown here, the MALNS method would be useful for creating route plans for tourism organizations that support travel route selection for family tours in Thailand.

Research on Experiential Tourism Route Planning Based on Multi-source Data Algorithm in Rich Energy and Cultural Resources Areas

Historical blocks are the carrier of urban history and culture, reflecting the continuation and development of urban history and culture, embodying a local historical context and regional cultural characteristics, and are an important part of historical and cultural cities. Taking the ancient city of Xi’an as the research area, this paper discusses the design method of the cultural promenade in the ancient city from the aspects of development condition analysis, route selection, theme determination, spatial organization and support system, etc., relying on multi-source data algorithms, and designed 4 Special theme promenade, on this basis, summarizes the planning and design methods of cultural promenade.

Application of Improved Dijkstra Algorithm in Coastal Tourism Route Planning

Chen, Y., 2020. Application of improved Dijkstra algorithm in coastal tourism route planning. In: Gong, D.; Zhang, M., and Liu, R. (eds.), Advances in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 106, pp. 251–254. Coconut Creek (Florida), ISSN 0749-0208.With the development of computer science and geographic information science, there are more and more applications for GIS. Shortest path analysis is a key issue in GIS geographic network analysis. One of the classical algorithms for calculating the shortest path is the Dijkstra algorithm, which is used in many projects to solve the shortest path problem. However, when the traditional Dijkstra algorithm solves the shortest path between nodes, a large number of nodes outside the identified nodes are calculated, which affects the speed of the algorithm. The Dijkstra algorithm is a classical algorithm for solving the shortest path in the embedded GIS system. By analyzing the Dijkstra algorithm, changing the storage structure, and examining graphing methods, the binary ordering tree algorithm based on the rectangular restricted area reduces the memory storage space. Shortening the query time requires optimizing the shortest path calculation process; the actual data test also shows the effectiveness of the algorithm.

Construction of Tourism Planning Information System Based on Ant Colony Algorithm

Tourism planning is a vital link in tourism. Compared with the traditional tourism planning based on experience, it is more scientific and reasonable to formulate through mathematical modeling methods. This paper mainly studies the construction of tourism planning information system based on ant colony(AC) algorithm. In the solution process, for the problems with more attractions, you need to divide the area first, then solve each area separately, and then transform the result of the solution into the regional self-driving tour route planning, and finally form a self-driving tour route planning. The experiments in this article found that most of the area tour time is closer to 15 days, which reduces the number of outings in a year and effectively reduces the round-trip time. In this paper, the system construction of self-driving tour route planning problems and ideas for solving specific problems are suitable for route planning in scenic spots or scenic spots, and have certain reference value.

Smart Tour Route Planning Algorithm Based on Naïve Bayes Interest Data Mining Machine Learning

A smart tour route planning algorithm based on a Naïve Bayes interest data mining machine learning is brought forward in the paper, according to the problems of current tour route planning methods. A machine learning model of Naïve Bayes interest data mining is set up by learning a mass of training data on tourists’ interests and needs. Through the recommended interest tourist site classifications from the machine learning module, the optimal tourist site mining algorithm based on the membership degree searching propagating tree of a tourist’s temporary accommodation is set up, which mines and outputs the optimal tourist sites. The mined optimal tourist sites are taken as seed points to set up a tour route planning algorithm based on the optimal propagating tree of a closed-loop structure. Through the proposed algorithm, an experiment is designed and performed to output optimal tour routes conforming to tourists’ needs and interests, including the propagating tree closed-loop structures, a minimum heap of propagating tree weight function value, and a weight function value complete binary tree. We prove that the proposed algorithm has the features of intelligence and accuracy, and it can learn tourists’ needs and interests to output optimal tourist sites and tour routes and ensure that tourists can get the best motive benefits and travel experience in the tour process, by analyzing the experiment data and results.

Map Matching for Urban High-Sampling-Frequency GPS Trajectories

As a fundamental component of trajectory processing and analysis, trajectory map-matching can be used for urban traffic management and tourism route planning, among other applications. While there are many trajectory map-matching methods, urban high-sampling-frequency GPS trajectory data still depend on simple geometric matching methods, which can lead to mismatches when there are multiple trajectory points near one intersection. Therefore, this study proposed a novel segmented trajectory matching method in which trajectory points were separated into intersection and non-intersection trajectory points. Matching rules and processing methods dedicated to intersection trajectory points were developed, while a classic “Look-Ahead” matching method was applied to non-intersection trajectory points, thereby implementing map matching of the whole trajectory. Then, a comparative analysis between the proposed method and two other new related methods was conducted on trajectories with multiple sampling frequencies. The results indicate that the proposed method is not only competent for intersection matching with high-frequency trajectory data but also superior to two other methods in both matching efficiency and accuracy.

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

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.

Application of an Improved Ant Colony Algorithm in Coastal Tourism Route Optimization

Zhang, W., 2019. Application of an improved ant colony algorithm in coastal tourism route optimization. In: Li, L.; Wan, X., and Huang, X. (eds.), Recent Developments in Practices and Research on Coastal Regions: Transportation, Environment and Economy. Journal of Coastal Research, Special Issue No. 98, pp. 84–87. Coconut Creek (Florida), ISSN 0749-0208.The ant colony algorithm has strong robustness, good adaptability, is easy to combine with other algorithms, and has applications in data mining and cluster analysis. Through the study of a basic ant colony algorithm and coastal travel route planning, two improvements to the ant colony algorithm have put forward, which include the adventurous algorithm and provide a new route-seeking method. The main purpose of this research was to make the ant colony algorithm meet the problem of route planning. The experimental results show that the improved ant colony algorithm achieves good results both in optimal path solution and in dynamic coastal travel route planning. The dynamic planning of the route can effectively realize the load balancing of tourist attractions and, at the same time, guide tourists to current small tourist attractions. It reflects the humanization and intelligence of coastal tourism route planning.

RETRACTED ARTICLE: Optimal individualized multimedia tourism route planning based on ant colony algorithms and large data hidden mining

This paper collects the coordinates of longitude and latitude of each city and the actual inter-city train tickets and air tickets aiming at the route optimization of 34 cities in China. Ant colony algorithm is used for heuristic search on the basis of this large amount of data, and a reasonable and optimal travel route is given for practical problems. At the same time, the increment of pheromone was adjusted by positive and negative feedback, and the volatile factors of pheromone were randomized, it enables the ant colony algorithm to automatically adjust the pheromone amount on the path to improve the performance of the ant colony algorithm. Finally, the performance advantages of the proposed algorithm in personalized tourism route planning are verified by simulation experiments.

Tourism Route Planning Route Based on Tourist Category using Orientee ring Problem with Time Windows

Indonesia has a large number of favorable tourism destinations, for example like Besuki in East Java that has some potential tourist attractions. The complexity to plan a tourism travel routes for particular tourist category become a problem. Hence, travel recommendations for tourists are required. To solve this problem, the first step is accumulating data from questionnaires, Google Map, TripAdvisor, and website of the Tourism Office in Besuki. The questionnaires were distributed to respondents with dominant age between 15-24 years (60%) and most of them were students (44%). Most respondents were single (74%) and traveling with their family (52%). Using LINGO, model Orienteering Problem with Time Windows (OPTW) with objective function is maximizing scores and having restrictions in opening and closing hours of each tourist attractions. The database that required are opening and closing hours, the distance between tourist attractions, and maximum tourist’s hours per day. Score for each tourist attraction and service time will be taken from each tourist category. There are 3 tourists’ categories such as Simplicity Searchers (42%), Cultural Purists (34%), and Reward Hunters (24%). Eventhough there is no significant difference among service time for all categories, the median service time will give different results in total score and routes.

Smart Tour Route Planning Algorithm Based on Clustering Center Motive Iteration Search

Tour route planning is hot research issue of smart tourism and tourism GIS. Reasonable tour route can help tourists get best motive benefit satisfaction. According to common tour route planning method and existing problems, smart tour route planning algorithm based on clustering center motive iteration search is brought forward in the study. Tourist sight data set and subset are set up and interest tourist sight empty vector is formed to store optimal interest tourist sight. Based on individual needs, tourist’s temporary accommodation in the downtown is set as clustering center to build optimal tourist sight extracting algorithm. Motive iteration interval and sub-interval are defined to build one-way shortest path algorithm to output shortest path between clustering center and tourist sight and between two tourist sights. Combining with positive and negative motive benefit impact factors, sub motive iteration values and motive iteration values are formed by iteration functions. Then descending order complete binary tree is generated to output and store motive iteration values and the optimal closed-loop structure with tour route, meanwhile, sub-optimal closed-loop structures with tour routes are also output. Experiment is designed and performed, sample tourists are selected to provide interest needs, and optimal tourist sight generation tree, all closed-loop structures and motive iteration values are obtained. Experiment indicates that the algorithm can solve problems of common tour route planning, meet the individual needs of tourists and provide them with optimal tourist sights and route to get best motive benefits.

江苏省5A级景区旅游路线规划

江苏省5A级景区旅游路线规划

Marketplace Location Decision Making and Tourism Route Planning

This research addresses the problem of planning tourism routes and finding appropriate shopping (market place) locations for agricultural product transportation. Generally, tourists visit popular tourism attractions; and generally, unpopular tourism attractions do not stimulate the economy, trade, or local income. Popular tourism attractions that are located far away from each other require the transportation of local products, and tourists must make decisions as to which locations to visit when planning their vacation. Planning a tourism route while balancing tourism attractions and shopping markets is important for the economic stimulation of tourism. This work presents a problem-solving method for tourism route-planning for a particular case study in Chiang Rai province, Thailand, using the Adaptive Large Neighborhood Search (ALNS) method. Six main destruction and five repair cycles in the ALNS method were applied to solve the tourism route design problem and to find the best solution so that tourists can visit all of the main attractions. We found that 13 tourism routes provide the shortest travel distance for each travel route. The total distance traveled was 2538.02 km for all routes. To balance the tourism on all routes, the popular and less popular tourism attractions were combined. For all routes, the shopping market location is the best place for tourism products to be sold and where tourist relaxation occurs. The results from ALNS were compared with the results from those obtained by the exact Lingo program V11. The ALNS algorithm results were not significantly different from the Lingo results. For the computational results for all examined cases, the ALNS algorithm was shown to be competitive, with short processing times given the sizes of the problems. For the traveling distance, the ALNS result significantly differs from the exact method by approximately 1.12%, and had a better effect than the exact method by approximately 99% in terms of processing time. Therefore, the proposed methodology provides an effective and high-quality solution for tourism route planning.

City Tour Route Planning Model Based on Improved Floyd Algorithm

On the basis of analyzing current problems of city tour route planning, this paper brings forward city tour route planning model based on improved Floyd algorithm. Firstly, interest sight spot coding and selecting model are set up to form sight spot classification code. Intelligent sight spot element selecting method is brought forward. And then city tour route planning algorithm model is set up according to selected sight spot element and optimal motive iteration output model of the shortest route is obtained. Take Zhengzhou for example, motive iteration values and sight spot guide maps of different tour routes are obtained. Meanwhile, optimal tour routes and several suboptimal routes are obtained, which meet the needs of tourists’ best motive benefit. Example testifies that the algorithm conforms to the reality and it is feasible and practical to intelligent tourism route planning.

Optimization of tourism route planning algorithm for forest wetland based on GIS

The traditional route planning algorithm has some problems, such as the ambiguity of objective function and the poor convergence, etc. Therefore, a tourism route optimization planning algorithm based on GIS for forest wetland is proposed. The genetic particle swarm optimization algorithm is designed, and then the GIS model is solved by the algorithm to improve the convergence performance of the traditional algorithm. Finally, the simulation experiments are done by parameter setting and environment simulation. The experimental results show that the algorithm has better convergence performance, higher degree of customer satisfaction and good optimization performance. It can be applied to the optimal tourism route planning of forest wetland.