Tour recommendation for groups

Consider a group of people who are visiting a major touristic city, such as NY, Paris, or Rome. It is reasonable to assume that each member of the group has his or her own interests or preferences about places to visit, which in general may differ from those of other members. Still, people almost always want to hang out together and so the following question naturally arises: What is the best tour that the group could perform together in the city? This problem underpins several challenges, ranging from understanding people’s expected attitudes towards potential points of interest, to modeling and providing good and viable solutions. Formulating this problem is challenging because of multiple competing objectives. For example, making the entire group as happy as possible in general conflicts with the objective that no member becomes disappointed. In this paper, we address the algorithmic implications of the above problem, by providing various formulations that take into account the overall group as well as the individual satisfaction and the length of the tour. We then study the computational complexity of these formulations, we provide effective and efficient practical algorithms, and, finally, we evaluate them on datasets constructed from real city data

Generating Travel Itinerary Using Ant Collony Optimization

Travelling is one of the activities needed by everyone to overcome weariness. The number of information about the tourism destination on the internet sometimes does not provide easiness for oncoming tourists. This paper proposes a system capable of making travel itinerary, for tourists who want to visit an area within a few days. For generating itinerary, the system considers several criterias (Multi-criteria-based), which include the popularity level of tourist attractions to visit, tourist visits that minimize budgets or tourist visits with as many destinations as possible. To handle multi criteria-based itinerary, we use the concept of multi attribute utility theory (MAUT). The running time of multi criteria-based itinerary is not significantly different from time-based itinerary. In addition, the number of tourist attractions in the itinerary is more than time-based itinerary, because the combination of solutions from each ant becomes more diverse

N-Days Tourist Route Recommender System in Yogyakarta Using Genetic Algorithm Method

Tourism is one of the proven solutions for the Indonesian economy. Tourism in certain regions, such as Yogyakarta, can significantly affect the region's economic development, including creating new jobs, creating new business opportunities, and increasing regional income. However, for tourists from outside Yogyakarta, it requires planning a tour before traveling in Yogyakarta, especially if he wants to spend several days on a tour. Many previous studies have developed systems that can recommend tourist routes, but not within a few days of tourist visits. In this study, we propose the use of Genetic Algorithm (GA) for automatically generating optimal travel itinerary for some days visit (n-days tour route). We develop the recommender system by combining GA and the concept of Multi-Attribute Utility Theory (MAUT). This MAUT used for accommodating user needs based some criteria such as rating, cost, and time. Based on our experimental results, GA is optimal in terms of execution time and number of attractions visited in n-days visit. The average execution time obtained is 59.62%, and the average number of attractions visited obtained is 45.95%. These results show that this method can generate tourist routes efficiently

Food tour recommendation using modified ant colony algorithm

Food tour is popular and becomes one of the most dynamic and creative segments of tourism.Popular itinerary of food tour can be extracted from the information in the Internet, but preference of the user must also be taken into consideration.This paper proposed a modified Ant Colony algorithm to find best possible itineraries through approximation and heuristic method by taking majority preferences of users into account when computing the recommended itinerary.The experiments were conducted on a food tour of restaurants in Yaowarat, a Bangkok’s China-town of Thailand.The results show that our proposed algorithm can recommend itineraries with rank-accuracy 0.88-0.97, which is better than the original Ant Colony algorithm with rank-accuracy 0.61-0.63

Travel route scheduling based on user’s preferences using simulated annealing

Nowadays, traveling has become a routine activity for many people, so that many researchers have developed studies in the tourism domain, especially for the determination of tourist routes. Based on prior work, the problem of determining travel route is analogous to finding the solution for travelling salesman problem (TSP). However, the majority of works only dealt with generating the travel route within one day and also did not take into account several user’s preference criteria. This paper proposes a model for generating a travel route schedule within a few days, and considers some user needs criteria, so that the determination of a travel route can be considered as a multi-criteria issue. The travel route is generated based on several constraints, such as travel time limits per day, opening/closing hours and the average length of visit for each tourist destination. We use simulated annealing method to generate the optimum travel route. Based on evaluation result, the optimality of the travel route generated by the system is not significantly different with ant colony result. However, our model is far more superior in running time compared to Ant Colony method

Application of Genetic Algorithm in solving Tourist Routing Problem

Normally, tourist will experience dilemma in planning their tour route especially when they visited foreign country for the first time. Manually mapping the cities and searching the information on the Internet can be very exhaustive. Besides these, tourist also faced a dilemma on how to travel across different cities efficiently and at shortest distance. This can also be known as Tourist Routing Problem (TRP). TRP is a variance of Travelling Salesman Problem (TSP) which can defined by finding the optimal path to travel from point A to point B by going through the same place not more than twice at a shortest distance. After completing a thorough comparative study, the author decided to apply Genetic Algorithm (GA), which is one of the best heuristic solutions to date in solving TRP. A rapid-prototyping methodology had been chosen because the author can immediately alter the prototype if there are any changes in the requirements. An Android mobile application will be utilized as a platform to test the effectiveness of GA in solving TRP. To support this, simulation and experiments will be conducted to evaluate the performance and speedup of the algorithm. Besides focusing on finding the best shortest distance route to travel, this application will enable tourist to select places to visit according to their preferences and activities that will be happening at that particular place

Travel plan for tourists: minimum access path and route circuit in Jalapão State Park

This article presents the proposal for a model travel plan for tourists in the Jalapão State Park [PEJ - Parque Estadual do Jalapão], located in the State of Tocantins, Brazil. The research shows the use of the Gurobi Optimizer library in Python Software associated with using Miller-Tucker-Zemlin (MTZ) constraints to ensure a viable route circuit. Through the Traveling Salesman Problem (TSP), two viable optimal routes are presented for two research problems: i) minimize the distance of access to the PEJ from the city of Palmas -TO and ii) find an optimal route path for tourists considering some of the most relevant points of the PEJ. The study presents a viable solution to route problems and contributes with an actual model, showing that TSP and the use of restrictions MTZ can be adequate to solve these problems and others to be solved in PEJ.This article presents the proposal for a model travel plan for tourists in the Jalapão State Park [PEJ - Parque Estadual do Jalapão], located in the State of Tocantins, Brazil. The research shows the use of the Gurobi Optimizer library in Python Software associated with using Miller-Tucker-Zemlin (MTZ) constraints to ensure a viable route circuit. Through the Traveling Salesman Problem (TSP), two viable optimal routes are presented for two research problems: i) minimize the distance of access to the PEJ from the city of Palmas -TO and ii) find an optimal route path for tourists considering some of the most relevant points of the PEJ. The study presents a viable solution to route problems and contributes with an actual model, showing that TSP and the use of restrictions MTZ can be adequate to solve these problems and others to be solved in PEJ

A Critical Analysis of a Tourist Trip Design Problem with Time-Dependent Recommendation Factors and Waiting Times

Data Availability Statement: Publicly available datasets were analyzed in this study. This data can be found here: http://github.com/cporrasn/TTDP_TDRF_WT_NWT.git.Acknowledgments: C.P. has been supported by a scholarship from AUIP Association coordinated with the Universidad de Granada. B.P.-C. was supported by the Erasmus+ programme of the European Union. The authors are grateful to the editors and the anonymous reviewers for their constructive comments and suggestions.The tourist trip design problem (TTDP) is a well-known extension of the orienteering problem, where the objective is to obtain an itinerary of points of interest for a tourist that maximizes his/her level of interest. In several situations, the interest of a point depends on when the point is visited, and the tourist may delay the arrival to a point in order to get a higher interest. In this paper, we present and discuss two variants of the TTDP with time-dependent recommendation factors (TTDP-TDRF), which may or may not take into account waiting times in order to have a better recommendation value. Using a mixed-integer linear programming solver, we provide solutions to 27 real-world instances. Although reasonable at first sight, we observed that including waiting times is not justified: in both cases (allowing or not waiting times) the quality of the solutions is almost the same, and the use of waiting times led to a model with higher solving times. This fact highlights the need to properly evaluate the benefits of making the problem model more complex than is actually needed.Projects PID2020-112754GB-I0, MCIN/AEI/10.13039/501100011033FEDER/Junta de Andalucía, Consejería de Transformación Económica, Industria, Conocimiento y Universidades/ Proyecto (B-TIC-640-UGR20