Improving Mobility In Smart Cities With Intelligent Tourist Trip Planning

Selecting the most interesting tourist attractions and planning optimal sightseeing tours can be a difficult task for individuals visiting unfamiliar tourist destinations. On the other hand, the massive amounts of tourists in big cities can collapse certain areas causing transport inefficiency, unbalanced economic growth and nuisance among tourists and citizens. Therefore, the tourist trip planning problem should take into account the possibility for the city government to manage the urban environment and achieve a balanced and sustainable growth. In this paper we introduce the tourist trip planning problem which covers both individual (tourist) and global (city) needs. The planning problem is modelled as an extension of the mixed orienteering problem and can be controlled by deployment of mobility policies which put restrictions on points of interest and routes between them. We propose an algorithmic approach and a software tool to solve this hard combinatorial optimisation problem using variable neighbourhood search. The performance of the proposed algorithm and the tool is assessed over a real-life dataset related to the city of Barcelona. Computational results confirm the efficiency of the algorithm and ability to help both individuals in planning their trips and city governments in achieving sustainable mobility objectives.