ENHANCING TRAVELLER EXPERIENCE IN INTEGRATED MOBILITY SERVICES VIA BIG SOCIAL DATA ANALYTICS

Maria Teresa Cuomo, Ivan Colosimo, Lorenzo Ricciardi Celsi, Roberto Ferulano, Giuseppe Festa and Michele La Rocca

Technological Forecasting and Social Change, 2022, vol. 176, issue C

Abstract: The research intends to propose a data-driven approach to boost the tourist experience in integrated mobility services and discuss how the experience may be improved. In particular, the data-driven approach, owing to the design of a recommendation system based on a big-data analytics engine, makes it possible to: i) rank the tourist preferences for the most attractive Italian destinations on Google; ii) rank the main attractions – leisure, entertainment, culture, etc. – associated with single tourist destinations, obtained from the analysis of relevant thematic websites such as Tripadvisor, Minube, and Travel365. This study is dependent on the support of big social data for the concept of tourism experience co-design, with a focus on integrated mobility services. From a technological viewpoint, analytics on big social data is enabled by relying on a cloud-based data platform, such as Amazon web services (AWS), Microsoft Azure, or Google cloud platform (GCP). This has proved to be the key to regularly collecting, updating, and processing data from several heterogeneous sources such as Google search queries accessible via Google Trends, or any social data scraped from websites, as well as extracting relevant insights that can meet the business needs expressed by mobility companies.

Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0040162521008957
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:176:y:2022:i:c:s0040162521008957

DOI: 10.1016/j.techfore.2021.121460

Access Statistics for this article

Technological Forecasting and Social Change is currently edited by Fred Phillips

More articles in Technological Forecasting and Social Change from Elsevier
Bibliographic data for series maintained by Catherine Liu ().