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Abstract
The optimal path planning for tourist guides in tourist areas can effectively improve the utilization of tourism time. This paper studies and analyzes the application and improvement of ant colony algorithm in terminal tourism route planning under Android platform, and proposes an optimal tour guide path planning model based on ant colony algorithm. By giving the time constraint of tour guide path planning and the total length of tour guide path, we solve the objective function and complete the tour guide path planning for the best group in the tourist area. The experimental results show that the proposed model and the optimal path planning algorithm are more optimized.
... With the development of the national economy and the improvement of people's living standards, there is a growing emphasis on physical health and spiritual enrichment [1]. Outdoor leisure and sightseeing have become part of a kind of healthy lifestyle and have been widely accepted by citizens [2][3][4] due to their well-known effects on health and fitness, their recreational value, and the benefits of landscape appreciation [5,6]. ...
... A proper recommendation of sightseeing routes is indispensable to help citizens gain a higher satisfaction because they can schedule their visits according to the recommended information [7]. An optimal sightseeing route plan contributes to shortening travel durations, satisfying their interests, and helping tourists enjoy physical relaxation [4]. In addition, a well-designed sightseeing route can present the legends, history, and myths of tourist attractions that enhance the market of tourism products and contribute to the local economy [8]. ...
... Determine the scenic spots and their entrances and exits and construct the vertex set V according to (4) and (5); • Construct the edge set E according to (4) and (5); • Calculate all weight functions for all edges according to (6) and (7); ...
Scenic tourism route plans are usually generated by combining scenic Points of Interest (PoIs) and the scenic road network. Traditional algorithms map the road networks linking the PoIs into a route collection and build a corresponding graph model. However, a single PoI description mechanism for scenic spots with multiple entrances and exits is significantly different from the actual tour route, which has multiple entrances and exits. Furthermore, the preferences and needs of tourists are not considered in attraction selection in existing algorithms. In this study, we propose a double-weighted graph model that considers the multiple entrances and exits of the PoI and identifies the tourists’ preferences using social network data. According to tourists’ different preferences and demands, different optimized tourist routes closer to the actual optimal paths were generated through an ant colony algorithm based on the proposed double-weighted graph model. To address the efficiency of the proposed model, we applied it in Shanghai and compared it with the traditional model through the 2bulu app, which can record three-dimensional (3D) trajectories of tourists. The comparison results show that the proposed model using social network data is closer to the actual 3D trajectory than the traditional model.
... This problem is called the tourist trip design problem (TTDP) in published literature (see e.g. [5,13,14,16,17,26,32,33,37]. and is an extension of the team orienteering problem (TOP) (see e.g. ...
... Brito et al. [5] proposed a fuzzy GRASP algorithm to solve TTDP. Mei [26] developed an ant colony algorithm under the Android platform. ...
The tourist trip design problem (TTDP) is about to generate routes for tourists to maximize the points of interest (POIs) visited within specific time windows. In this study, new constraints: budget, weather and break are considered. First, the budget is required for entrance fees and the distance between two points where a taxi has to be used. Additionally, the expense of the break was taken into account. Then, the weather was considered for summer and for other seasons. On a summer day, tourists are likely to prefer visiting POIs, which are indoor areas, between specific times e.g. 11 a.m. to 3 p.m. to protect against the side effects of the sun. Furthermore, tourists need to take a break to relax during the trip. A mathematical model of the TTDP with these new constraints (TTDP-BWB) was developed. Then, a heuristic algorithm was developed with a new defined function that took the new constraints into account. The algorithm was codded using Android Studio and developed a mobile application for the case of Eskisehir in Türkiye. Problems are generated on the small and medium scale for the case of Eskisehir and used large-scale problems from published literature. The results of the algorithm were compared with the results of the mathematical model for the small scale problems. Additional, large-scale problems from literature were solved to see the performance of the heuristic algorithm. Computational results showed that the algorithm is promising.
... With negligible sight, ants search food in darkness and identify the shortest path from colony to food. This is attributed to the pheromone released by the ants during crawling to identify their paths, and can change their crawling direction according to the concentration of pheromone while searching food, until they reach the food source [22]. Since pheromone evaporates over the time, ants can find the shortest path between the colony and the food source by crawling multiple times. ...
Police patrol is an essential and important means to maintain the public security and social stability. However, the role of the experience-based patrol in the development of the social security prevention and control system is also declining. To address the problems of a fuzzy patrol path and irrational path planning in the conventional patrol mode, practical alert data from 2019-2021 was pre-processed and used. On the basis of the Maklink graph theory, optimized patrol paths were improved using the ant colony algorithm. Specifically, 2D path planning in presence of buildings was analyzed. In the study, we take two sites with high incidence of cases as key patrol points and finally calculates the shortest 2D walking patrol path to avoid obstacles between the two points. This study facilitates daily patrol by policemen in terms of enhanced accuracy and practical effectiveness.
... Therefore, this paper uses the travel distance to measure the path scheme. The mathematical expression of the path planning model [15] is: objective function: ...
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
... The terminal travel path planning of ant colony algorithm under Android platform is studied and analyzed, and an optimal guidance path planning model based on ant colony algorithm is proposed. By giving the time constraints of the tour guide path planning and the total length of the tour guide path, the objective function is solved to complete the tour guide path planning for the best group in the tourist area (Mei, 2018). Our model adds the parameter of historical culture. ...
... Te method solves the path planning problem with variable environments and is verifed by simulations and experiments. In [20], Mei proposes an optimal tour guide path planning model based on an ant colony algorithm. Te experimental results show that the proposed model and the optimal path planning algorithm are more optimized. ...
With the increasing frequency of autonomous driving, more and more attention is paid to personalized path planning. However, the path selection preferences of users will change with internal or external factors. Therefore, this paper proposes a personalized path recommendation strategy that can track and study user’s path preference. First, we collect the data of the system, establish the relationship with the user preference factor, and get the user’s initial preference weight vector by dichotomizing the K-means algorithm. The system then determines whether user preferences change based on a set threshold, and when the user’s preference changes, the current preference weight vector can be obtained by redefining the preference factor or calling difference perception. Finally, the road network is quantized separately according to the user preference weight vector, and the optimal path is obtained by using Tabu search algorithm. The simulation results of two scenarios show that the proposed strategy can meet the requirements of autopilot even when user preferences change.
... This type of algorithm is mainly used in global route planning. The scenic tour route planning problem studied by Mei et al. [8] is this type of problem. The route planning problem usually consists of two targets. ...
Weeds are one of the most important agricultural hazards. The widespread spraying of herbicides not only wastes chemicals but also pollutes the environment. In this paper, a precisely route planning method for weeding machine based on UAV(Unmanned Aerial Vehicle) images was proposed. A genetic algorithm (GA) was used to optimize the operation route. For genetic algorithm, a new route encoding approach and fitness function were presented. The GA-optimized operating route saves up to 80.03% of working time compared to uniform spraying in the experiment. This method could effectively plan the operation route of spraying machines and reduce herbicide usage. This was important for both cost-saving and environment protection.
... Literature [21] pointed out how to measure and analyze the damage of tourism to the ecological environment and control it on the basis of quantitative analysis, and how to develop the sustainable development of the tourism industry which has become the key research object in the field of tourism research in recent years. Literature [22] makes use of travel notes to explore the features of different scenic spots, and combines these features with the interests of users and scenic spots. Based on this tourism information, the tourism-related knowledge is excavated and personalized travel routes for users were planned. ...
In this study, aiming at the huge amount of information in the tourism field, the pressure of ecological environment, and tedious personalized route planning, an ecotourism personalized route planning system based on the ecological footprint model is designed. In order to recommend routes that meet the time limit and the starting and ending points of the user’s choice, the tourist route recommendation problem is studied as a directional problem on the basis of comprehensively considering the popularity of scenic spots and the user’s interest preferences as the scenic spots score. The scenic spot scoring strategy is scenic spot scoring, and the iterative local search strategy is used to plan tourist routes according to the optimization goal with the largest route score, and improve the real tourist routes, food, and accommodation strategies. On the basis of launched tourist routes, they recommend tourists’ favorite food and accommodation. The model finally completes the fine arrangement of scenic spots, food, and accommodation in the whole tourist route. The system test results show that the system has obvious advantages in personalized path planning effect, excellent user feedback effect, and certain application value.
... Huanwg introduced the chance algorithm into ACO, which has good effect in dynamic tourism route planning [22]. Mei took time as the key constraint, and gave a tourism route planning prototype combined with ant colony algorithm [44]. However, the above approaches suffer from two limitation when handling 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.
... Each of the methods presented above has its strengths and weaknesses. In many situations, some of them are combined to derive the desired path planner in the most effective mode [12,13]. ...
Obstacle avoidance is one of the essential and indispensable functions for autonomous mobile robots. Most of the existing solutions are typically based on single condition constraint and cannot incorporate sensor data in a real-time manner, which often fail to respond to unexpected moving obstacles in dynamic unknown environments. In this paper, a novel real-time multi-constraints obstacle avoidance method using Light Detection and Ranging(LiDAR) is proposed, which is able to, based on the latest estimation of the robot pose and environment, find the sub-goal defined by a multi-constraints function within the explored region and plan a corresponding optimal trajectory at each time step iteratively, so that the robot approaches the goal over time. Meanwhile, at each time step, the improved Ant Colony Optimization(ACO) algorithm is also used to re-plan optimal paths from the latest robot pose to the latest defined sub-goal position. While ensuring convergence, planning in this method is done by repeated local optimizations, so that the latest sensor data from LiDAR and derived environment information can be fully utilized at each step until the robot reaches the desired position. This method facilitates real-time performance, also has little requirement on memory space or computational power due to its nature, thus our method has huge potentials to benefit small low-cost autonomous platforms. The method is evaluated against several existing technologies in both simulation and real-world experiments.
... Each of the methods presented above has its strengths and weaknesses. In many situations, some of them are combined to derive the desired path planner in the most effective mode [12,13]. ...
Obstacle avoidance is one of the essential and indispensable functions for autonomous mobile robots. Most of the existing solutions are typically based on single condition constraint and cannot incorporate sensor data in a real-time manner, which often fail to respond to unexpected moving obstacles in dynamic unknown environments. In this paper, a novel real-time multi-constraints obstacle avoidance method based on Light Detection and Ranging(LiDAR) is proposed, which is able to, based on the latest estimation of the robot pose and environment, find the sub-goal defined by a multi-constraints function within the explored region and plan a corresponding optimal trajectory at each time step iteratively, so that the robot approaches the goal over time. Meanwhile, at each time step, the improved Ant Colony Optimization(ACO) algorithm is also used to re-plan optimal paths from the latest robot pose to the latest defined sub-goal position. While ensuring convergence, planning in this method is done by repeated local optimizations, so that the latest sensor data from LiDAR and derived environment information can be fully utilized at each step until the robot reaches the desired position. This method facilitates real-time performance, also has little requirement on memory space or computational power due to its nature, thus our method has huge potentials to benefit small low-cost autonomous platforms. The method is evaluated against several existing technologies in both simulation and real-world experiments.
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.
The Time Dependent Team Orienteering Problem with Time Windows (TDTOPTW) can be used to model several real life problems. Among them, the route planning problem for tourists interested in visiting multiple points of interest (POIs) using public transportation. The main objective of this problem is to select POIs that match tourist preferences, taking into account a multitude of parameters and constraints while respecting the time available for sightseeing in a daily basis and integrating public transportation to travel between POIs (Tourist Trip Design Problem, TTDP). TDTOPTW is NP-hard while almost the whole body of the related literature addresses the non time dependent version of the problem. The only TDTOPTW heuristic proposed so far is based on the assumption of periodic transit service schedules. Herein, we propose efficient cluster-based heuristics for the TDTOPTW which yield high quality solutions, take into account time dependency in calculating travel times between POIs and make no assumption on periodic service schedules. The validation scenario for our prototyped algorithms involved the transit network and real POI datasets compiled from the metropolitan area of Athens (Greece). Our TTDP algorithms handle arbitrary (i.e. determined at query time) rather than fixed start/end locations for derived tourist itineraries.
The tourist trip design problem (TTDP) refers to a route-planning problem for tourists interested in visiting multiple points of interest (POIs). TTDP solvers derive daily tourist tours, i.e., ordered visits to POIs, which respect tourist constraints and POIs attributes. The main objective of the problem discussed is to select POIs that match tourist preferences, thereby maximizing tourist satisfaction, while taking into account a multitude of parameters and constraints (e.g., distances among POIs, visiting time required for each POI, POIs visiting days/hours, entrance fees, weather conditions) and respecting the time available for sightseeing on a daily basis. The aim of this work is to survey models, algorithmic approaches and methodologies concerning tourist trip design problems. Recent approaches are examined, focusing on problem models that best capture a multitude of realistic POIs attributes and user constraints; further, several interesting TTDP variants are investigated. Open issues and promising prospects in tourist trip planning research are also discussed.
In order to improve the earlier stagnation in the conventional ant colony optimization, which easily leads to local optimal solution, an improved algorithm was proposed. In the algorithm, a new mechanism of trail information exchange between edges was introduced; on the other hand, the trail information volatilization was modified adaptively with the algorithm operating. By those, the function of positive feedback in ACO was suppressed to a reasonably degree so that the algorithm will not stopped earlier, the area of feasible solutions was expanded, and hence, a better solution could likely be got, at the same time the convergence speed was not reduced distinctly. Experimental results on three TSPs show that the algorithm has more powerful capacity of finding global solution and stability than conventional ant colony optimization.
Time-dependent vehicle routing problem (TDVRP) is concerned with vehicle routing optimization in road networks with fluctuant link travel time. The traditional vehicle routing problem (VRP) has been proven to be an NPhard problem, so it is difficult to solve TDVRP in considering traffic conditions. We design an improved ant colony optimization algorithm (ACO) for TDVRP. It uses nearest neighbor algorithm based on minimum cost(NNC algorithm) to generate the initial solution, improves feasible solution by local search operations, and updates pheromone with max/min ant system strategy. Test results show that compared with the nearest neighbor algorithm and genetic algorithm, the improved ACO algorithm is more efficient and able to get better solutions. Furthermore, this improved ACO algorithm show good performance in large scale TDVRP instances, even if the customer number of TDVRP reaches 1000, the computation time is still in an acceptable range.
Computer-aided design of pipe routing is of fundamental importance for complex equipments' developments. In this article, non-rectilinear branch pipe routing with multiple terminals that can be formulated as a Euclidean Steiner Minimal Tree with Obstacles (ESMTO) problem is studied in the context of an aeroengine-integrated design engineering. Unlike the traditional methods that connect pipe terminals sequentially, this article presents a new branch pipe routing algorithm based on the Steiner tree theory. The article begins with a new algorithm for solving the ESMTO problem by using particle swarm optimisation (PSO), and then extends the method to the surface cases by using geodesics to meet the requirements of routing non-rectilinear pipes on the surfaces of aeroengines. Subsequently, the adaptive region strategy and the basic visibility graph method are adopted to increase the computation efficiency. Numeral computations show that the proposed routing algorithm can find satisfactory routing layouts while running in polynomial time.
Many different types of construction projects set up work zones on roads. Especially in urban areas, lane closures as a result of work zones have a considerable impact on local traffic. However, for a construction project that consists of several work zones and several work crews, the traffic impact may be improved by appropriate scheduling. Therefore, this paper proposed a scheduling model based on the route-changing behavior of road users. The proposed model calculates the traffic delay of vehicles by microscopic simulation, and applies team ant colony optimization to search for a near-optimal schedule. The project planner then ensures that the contractor executes the activities according to the near-optimal schedule. The proposed model is applied to schedule a sewer system construction project in a city. The results of our study indicate that with our proposed model the total traffic delay is reduced by 11.1% when compared with a schedule proposed by the project planner.
Optimization of multiple constrains QoS routing based on an adaptive ant colony system algorithm
- G A O Jian
- Jian G.A.O.