A Cloud Information Monitoring and Recommendation Multi-Agent System with Friendly Interfaces for Tourism

Abstract

The tourism statistics of Taiwan's government state that the tourism industry is one of the fastest growing economic sources in the world. Therefore, the demand for a tourism information system with a friendly interface is growing. This research implemented the construction of a cloud information service platform based on numerous practical developments in the Dr. What-Info system (i.e., a master multi-agent system on what the information is), which developed universal application interface (UAI) technology based on the Taiwan government's open data with the aim of connecting different application programming interfaces (APIs) according to different data formats and intelligence through local GPS location retrieval, in support of three-stage intelligent decision-making and a three-tier address-based UAI technology comparison. This paper further developed a novel citizen-centric multi-agent information monitoring and recommendation system for the tourism sector. The proposed system was experimentally demonstrated as a successful integration of technology, and stands as an innovative piece of work in the literature. Although there is room for improvement in experience and maybe more travel-related agents, the feasibility of the proposed service architecture has been proven.

Tourism Information Data Processing Method Based on Multi‐Source Data Fusion

“…By eliminating redundant data and fusing various data sources for correlation and supplementary analysis, more valuable new information can emerge from the data, to achieve the effect of 1 + 1 > 2. Multimedia data collected from the Internet and mobile devices are typical unstructured data, which is significantly different from the traditional structured data format [21]. Therefore, the processing of multi-source heterogeneous data collected by different edge devices becomes an urgent problem in big data research [22].…”

A Data-Driven Approach to Trip Generation Modeling for Urban Residents and Non-local Travelers

“…The GPS data can provide trip makers' travel trajectories with high spatial accuracy. Researchers have used GPS data to obtain real-time traffic status on roadway segments [5] and to construct personal users' travel choice recommendation systems [6], however, GPS data are rarely used to analyze travel patterns on a large scale at a city level since users may not use navigation services for every trip, especially for their routine commuting trips, therefore, the GPS technique has a deficiency in collecting data of a sufficiently large sample size. The cellular phone data can provide trip information of a large population size, including for both residents and travelers with high temporal and spatial resolution.…”