A survey on algorithmic approaches for solving tourist trip design problems

Gavalas, Damianos; Konstantopoulos, Charalampos; Mastakas, Konstantinos; Pantziou, Grammati

doi:10.1007/s10732-014-9242-5

Cited by 264 publications

(164 citation statements)

References 123 publications

Supporting

Mentioning

158

Contrasting

Unclassified

Order By: Relevance

“…Note that TTDP and TSP are different problems: the TTDP objective is not the shortest path but finding a suitable route for a particular user. The survey works [30,31] collect the tourism-centered algorithms to solve this problem. Solving the TTDP is a daunting task (commonly, using metaheuristics) and it needs a lot of information.…”

Section: Related Workmentioning

confidence: 99%

ANT: Agent Stigmergy-Based IoT-Network for Enhanced Tourist Mobility

López-Matencio¹,

Vales‐Alonso²,

Costa-Montenegro³

2017

Mobile Information Systems

View full text Add to dashboard Cite

In this work, we propose ANT, a network of agents which exchange information in a stigmergy-based fashion. In ANT, each system's actor distributes pheromones as a way of indicating places' attractiveness, as well as for building a proper routing path to those sites. The goal of ANT is to improve the chances of discovering points of interest, as well as to reduce the time required for doing so. We have applied ANT to a tourist mobility scenario, where both people and things (events, restaurants, performances, etc.) participate. ANT has achieved notable success in this example case. We find that probability of discovering temporary events and dates improves by more than 35%, while the mean time employed to determine static point decreases by more than a third. We also introduce a mobile-based architecture which performs ANT tasks efficiently and easily for the user.

show abstract

Section: Related Workmentioning

confidence: 99%

ANT: Agent Stigmergy-Based IoT-Network for Enhanced Tourist Mobility

López-Matencio¹,

Vales‐Alonso²,

Costa-Montenegro³

2017

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

“…Cycle-tourist routing is studied by Cerna et al [17]. Tourist trip design problems are studied by Gavalas et al [18].…”

Section: Literature Reviewmentioning

confidence: 99%

A Bi-Modal Routing Problem with Cyclical and One-Way Trips: Formulation and Heuristic Solution

Grinde

2017

Information Technology and Management Science

View full text Add to dashboard Cite

-A bi-modal routing problem is solved using a heuristic approach. Motivated by a recreational hiking application, the problem is similar to routing problems in business with two transport modes. The problem decomposes into a set covering problem (SCP) and an asymmetric traveling salesperson problem (ATSP), corresponding to a hiking time objective and a driving distance objective. The solution algorithm considers hiking time first, but finds all alternate optimal solutions, as inputs to the driving distance problem. Results show the trade-offs between the two objectives.

show abstract

“…In order to find popular POIs for travel route recommendations, several works (Lee and Sohn, 2006;Lee and Sohn, 2009;YU et al, 2009;Chen et al, 2014;Kurashima et al, 2010;Li, 2013) explored the wisdom of the crowd through overwhelming images shared by people on social media sites like Flickr. Furthermore, travel route search problems are commonly formulated as tourist design problems (Gavalas et al, 2014), such as Orienteering Problem with Time Windows (Chen et al, 2014;Li, 2013;Sylejmani and Dika, 2011;Vansteenwegen et al, 2009) and the Traveling Salesman Problem (Kurata and Hara, 2013). To solve the problems, various search algorithms with heuristics (Gavalas et al, 2014) are employed, such as an iterated local search (Vansteenwegen et al, 2009), a taboo search (Sylejmani and Dika, 2011), and a genetic algorithm (Kurata and Hara, 2013).…”

Section: Related Workmentioning

confidence: 99%

Multi-day Trip Planning System with Collaborative Recommendation

Aprilia¹,

Oh²,

Hong³

et al. 2016

Journal of Intelligence and Information Systems

View full text Add to dashboard Cite

․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․Planning a multi-day trip is a complex, yet time-consuming task. It usually starts with selecting a list of points of interest (POIs) worth visiting and then arranging them into an itinerary, taking into consideration various constraints and preferences. When choosing POIs to visit, one might ask friends to suggest them, search for information on the Web, or seek advice from travel agents; however, those options have their limitations. First, the knowledge of friends is limited to the places they have visited. Second, the tourism information on the internet may be vast, but at the same time, might cause one to invest a lot of time reading and filtering the information. Lastly, travel agents might be biased towards providers of certain travel products when suggesting itineraries. In recent years, many researchers have tried to deal with the huge amount of tourism information available on the internet. They explored the wisdom of the crowd through overwhelming images shared by people on social media sites. Furthermore, trip planning problems are usually formulated as 'Tourist Trip Design Problems', and are solved using various search algorithms with heuristics. Various recommendation systems with various techniques have been set up to cope with the overwhelming tourism information available on the internet. Prediction models of recommendation systems are typically built using a large dataset. However, sometimes such a dataset is not always available. For other models, especially those that require input from people, human computation has emerged as a powerful and inexpensive approach. This study proposes CYTRIP (Crowdsource Your TRIP), a multi-day trip itinerary planning system that draws on the collective intelligence of contributors in recommending POIs. In order to enable the crowd to collaboratively recommend POIs to users, CYTRIP provides a shared workspace. In the shared workspace, the crowd can recommend as many POIs to as many requesters as they can, and they can also vote on the POIs recommended by other people when they find them interesting. In CYTRIP, anyone can make a contribution by recommending POIs to requesters based on requesters' specified preferences. CYTRIP takes input on the recommended POIs to build a multi-day trip itinerary taking into account the user's preferences, the various time constraints, and the locations. The input then becomes a multi-day trip planning problem that is formulated in Planning Domain Definition Language 3 Priska ApriliaㆍKyeong-Jin OhㆍMyung-Duk HongㆍMyeong-Hyeon GaㆍGeun-Sik Jo 160 ․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․ (PDDL3). A sequence of actions formulated in a domain file is used to achieve the goals in the planning problem, which are the recommended POIs to be visited. The multi-day trip planning problem is a highly constrained problem. Sometimes, it is not feasible to visit all the recommended POIs with the limited resources av...

show abstract

A survey on algorithmic approaches for solving tourist trip design problems

Cited by 264 publications

References 123 publications

ANT: Agent Stigmergy-Based IoT-Network for Enhanced Tourist Mobility

ANT: Agent Stigmergy-Based IoT-Network for Enhanced Tourist Mobility

A Bi-Modal Routing Problem with Cyclical and One-Way Trips: Formulation and Heuristic Solution

Multi-day Trip Planning System with Collaborative Recommendation

Contact Info

Product

Resources

About