Development and evaluation of a context‐driven, mobile tourist guide

Abstract

The behavior of tourists strongly depends on the availability and quality of information. Little information as well as a flood can be disorienting and forces many tourists to join the majority visiting major sights. This causes a few crowded places in contrast to many under-utilized. A Destination Management Organization (DMO) has the goal to spread the tourists more evenly, whereas the tourists would like to enjoy the destination to its full potential according to their personal interests. The target of the Dynamic Tour Guide (DTG) is supporting both goals by means of pervasive computing based on the actual context which is defined by personal interests, location and schedule of a tourist. It enables a personalized, spontaneous and guided tour. A field trial was conducted in the summer of 2005 to study the following questions as a precondition for the development: (1) Is it possible to seed generic interest profiles in the mobile context that allow the accurate prediction of actual rankings? (2) Are the interest profiles sufficiently diverse to base personalized tours on individual interest profiles instead of interest prototypes? (3) How do personalized tours affect the spatial behavior of tourist? Three methods to elicit the generic preferences of tourist in the mobile context are compared with actual rankings using Spearman's rank order coefficient. The diversity of the interest profiles is analyzed in various ways leading to the conclusion that personalized interest profiles are necessary. For the gathered profiles tours are being computed and simulated in order to gain a first insight into the effect on the spatial behavior of tourists.

Social workflows—Vision and potential study

“…Kramer et al [40] developed a context-driven mobile tourist guide. They pursue the idea to have a local guide, who understands the individual interests and timeframe, knows the local situation and gives a personal tour to each tourist.…”

The impact of Web 3.0 technologies on Tourism Information Systems

“…Intelligent systems in tourism have been developed to provide services traditionally delivered by tour operators and travel guides, such as travel planning, navigation, interpretation, etc. [18]. ere are several di erent examples of intelligent systems, like YourTour application that uses sophisticated algorithms as a function of the dynamic packaging of trav-el arrangements.…”

Logistical routing of park tours with waiting times: case of Beijing Zoo

“…Literature review: Routing with traveling salesman problem One goal of tour route planning is to maximize the expected efficiency in terms of minimal time or travel distance. This goal is achieved by taking into consideration different tourist objectives under various conditions (Godart, 2003;Hagen, Modsching, & Krarner, 2005;Hyde & Lawson, 2003;Kramer, Modsching, & Hagen, 2007;Rodriguez et al, 2012). The Traveling Salesman Problem (TSP), which requires finding the shortest path to visit each location only once and returning to the starting location, is frequently employed for tour route planning.…”