Explanatory variables underlying the route choice decisions of tourists: The case of Geiranger Fjord in Norway
Peer reviewed, Journal article
MetadataShow full item record
Original versionTransportation Research Part A: Policy and Practice. 2020, 141 398-409. 10.1016/j.tra.2020.09.023
The tourism industry is rapidly growing, and the massification of certain areas is jeopardising the environment. Certain roads in protected areas or near tourist attractions are experiencing an increase in traffic volumes, leading to higher pollution and noise levels and greater discomfort in the travel experience. To recommend measures intended to ensure a sustainable tourism industry without compromising the environment, it is necessary to obtain further knowledge regarding tourists' travel behaviour as it may differ from that of other travellers, i.e., tourists might choose a route based on variables other than time and cost, such as landscape or tourist attractions. Using Geiranger Fjord in Norway as a case study, tourist route choice preferences were observed and analysed. In total, 60 routes from 12 different origins were observed based on 408 responses collected through a tourist survey administered in the summer of 2018. These data were combined with information regarding road features and the locations of tourist attractions. A path size correction logit model was selected to estimate the significant variables in the choice of one route over other routes. The results showed that tourists selected their routes mainly based on the travel time, road scenery (water bodies and forests), sightseeing places among first-time visitors, and outdoor activities among those living outside of Norway. In addition, the tourists preferred local roads, and 'caravan' tourists were more attracted to roads with fewer facilities. The findings of this study show that tourists’ route choice behaviour differs from that of other travellers. Policymakers should consider these differences to enhance transport systems. Further research should focus on incorporating these findings into a transport model to simulate tourists' travel patterns and quantify changes in traffic volumes due to different sustainable measures.