|
Name:
|
Personalized Sightseeing Planning System
|
|
Description:
|
When a tourist arrives to a new place (country, city or region) he would certainly appreciate to have a quite simple tool to assist him in planning his staying, according to his objectives, preferences, knowledge, budget and staying period, instead of having to look for guide prospects/bulletins which sometimes can be quite confuse, or having to follow a standardized plan which does not fulfil his needs.
The tour planning task can be quite complex and time consuming due to the numerous attractions to visit, restaurants to eat and hotels available, when combining this task with the selection of transportation modes to move between selected places the complexity level raises, which is generally a discouraging factor for a tourist when trying to establish a plan that fulfils his needs, including preferences, wishes and constraints.
In this project we propose the development of a Personalised Sightseeing Planning System (PSiS) which intends to help tourists in finding a personalized tour plan allowing them to use their time efficiently and promote the culture and national tourism. Hence, this research focuses on tour planning support, we aim at defining and adapt a visit plan combining, in a tour, the most adequate tourism products, namely interesting places to visit, attractions, restaurants and accommodation, according to tourist’s specific profile (which includes interests, personal values, wishes, constraints and disabilities) and available transportation modes between the selected products. Functioning schedules will be considered as well as transportation schedules.
To develop such a system we need to efficiently address the core of the tour planning process. Hence, we have to define an optimization model that clearly represents the described tour planning problem and design a heuristic algorithm that effectively tackles that problem. The Traveling Salesman Problem (TSP) and some of its variations with additional constraints like time windows (TSPTW) or the Prize Collecting Traveling Salesman Problem (PCTSP) have been used as basis for the development of algorithms that address tour planning issues. The TSP has been extensively studied by some of the members of this research. Following our TSP research, we intend to explore the application to the tour planning problem of advanced concepts and methods for the construction of effective neighborhood structures such as those derived from ejection chain methods and take advantage of the emerging methodologies, especially those that exploit the concept of adaptive memory programming.
Generated tour plans will be added to a “tour basket”, here the tourist has the possibility of eliminate selected attractions or add new ones, according his actual interest, motivation or availability. The system gathers knowledge about the tourist’s profiles, creating groups and stereotypes with specific interests and features, allowing characteristics inheritance. The “tour basket” stores tourist’s travel history, where all the places he visited are stored, which leads to accumulated knowledge about personal profiles. This knowledge, together with tourist stereotypes offer a mean of learning about general and specific interests of tourists, so that this information can serve as a basis for studying new forms of tourist products, which can be useful for the tourism sector, namely public entities (e.g. city council) as well as for travel agencies.
It will, also, be possible, for the tourist, to return information on accomplished tours. So, the system gathers knowledge about tourist’s opinions and preferences. Based on this knowledge and on profile groups categorised information can be delivered according to tourist specific interests, namely events, factual information, useful tips, promotional offers, recommended places to visit and more.
|