| dc.description.abstract | The fast development of machine learning and artificial intelligence has led to a great improvement of the smart
tourism recommendation system, however many problems associated with the choice of transport modes in city
tourism have yet to be solved. This research attempts to address this issue by proposing a model of customized
day itineraries with consideration of transport mode choice. With improved particle swarm optimization and
differential evolution algorithm, a nondominated sorting heuristic approach was devised. A case study was
carried out in Chengdu, China to examine the performance of our approach. The results show that compared with
extant methods, our approach achieves better performance. In addition, our approach can create more sensible,
multifarious, and customized itineraries than previous methods. Tourism organizations and mobile map app
providers could integrate our proposed model into their existing smart service systems, as part of their e-business
or digital strategy for enhancing tourist experience | vi |
