A Context-Aware Route Finding Algorithm for Self-Driving Tourists Using Ontology

Barzegar, Maryam; Sadeghi‐Niaraki, Abolghasem; Shakeri, Maryam; Choi, Soo-Mi

doi:10.3390/electronics8070808

Cited by 5 publications

(5 citation statements)

References 32 publications

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“…These various contents should be ranked according to the user's preferences in the AR systems. Finally, this research can be improved by combining it with routing methods [30,54] so that the user can be navigated to the ranked PoIs.…”

Section: Discussionmentioning

confidence: 99%

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Personalized Augmented Reality Based Tourism System: Big Data and User Demographic Contexts

et al. 2021

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A lack of required data resources is one of the challenges of accepting the Augmented Reality (AR) to provide the right services to the users, whereas the amount of spatial information produced by people is increasing daily. This research aims to design a personalized AR that is based on a tourist system that retrieves the big data according to the users’ demographic contexts in order to enrich the AR data source in tourism. This research is conducted in two main steps. First, the type of the tourist attraction where the users interest is predicted according to the user demographic contexts, which include age, gender, and education level, by using a machine learning method. Second, the correct data for the user are extracted from the big data by considering time, distance, popularity, and the neighborhood of the tourist places, by using the VIKOR and SWAR decision making methods. By about 6%, the results show better performance of the decision tree by predicting the type of tourist attraction, when compared to the SVM method. In addition, the results of the user study of the system show the overall satisfaction of the participants in terms of the ease-of-use, which is about 55%, and in terms of the systems usefulness, about 56%.

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Section: Discussionmentioning

confidence: 99%

“…There are several studies that focused on personalization in the recommendation systems [11,12,[23][24][25][26][27][28][29][30]. Al-Shamri et al [13] presented profiling the user in order to improve the demographic recommender systems by exploring the unified and isolated user profile approaches.…”

Section: Literature Reviewmentioning

confidence: 99%

Personalized Augmented Reality Based Tourism System: Big Data and User Demographic Contexts

et al. 2021

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“…Therefore, on one hand, the planning of tour transportation-oriented toward tourists shall meet the tourists' psychological needs (Lee and Abrahams, 2018;Zheng and Liao, 2019), consistent with the design of railway networks in the macro level of tour planning, and abide by the changing law of the mentality of tourist experience . On the other hand, the planning of tour routes shall focus on the rich architectural, cultural, and scenic values and interpret them based on the interests of tourists (Barzegar et al, 2019). Although few scholars have proposed the opinions that the planning of tour routes shall focus on the psychological features of tourists, it is an integral part of almost all relevant explorations.…”

Section: Tour Routesmentioning

confidence: 99%

Social Communication of Transportation: A Bridge Model Connecting Tourism Destination and Psychological Perception

Zhang

Wang

et al. 2022

Front. Psychol.

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As it is essential to explore the influence of social communications on transportation routes in tourism, this article aims to examine the impacts of social communications on transportation routes in the field of tourism and to further explore the relationship between tourism destinations and their psychological perceptions. In terms of links between different tourism destinations in space and time dimensions, our empirical analysis draws the following conclusions: (1) the behavior of tourist flow is a mediating variable on the links between tourist psychological perceptions and tour routes; (2) three modes of point-line interaction are presented in the space and time of tourism destinations; and (3) the scenic city’s location, name, and features are important to tourists’ psychological perceptions.

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“…Utilizing the smart environment applications trend has increased due to the recent development of the context-aware intelligent system. Significant effort has been made to represent the formal structure of a context-aware system by using the ontology approach (Goel et al, 2017 ; Barzegar et al, 2019 ). In the proposed system, we have developed an ontology (OWL 2) in which that sensor's data goes into ontology where intelligent agents [a1, a2 … a n ] can access the data and perform reasoning on it to infer the desired result.…”

Section: Context Modeling Of Mas Using Ontologymentioning

confidence: 99%

Situation aware intelligent reasoning during disaster situation in smart cities

et al. 2022

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Investigating prior methodologies, it has come to our knowledge that in smart cities, a disaster management system needs an autonomous reasoning mechanism to efficiently enhance the situation awareness of disaster sites and reduce its after-effects. Disasters are unavoidable events that occur at anytime and anywhere. Timely response to hazardous situations can save countless lives. Therefore, this paper introduces a multi-agent system (MAS) with a situation-awareness method utilizing NB-IoT, cyan industrial Internet of things (IIOT), and edge intelligence to have efficient energy, optimistic planning, range flexibility, and handle the situation promptly. We introduce the belief-desire-intention (BDI) reasoning mechanism in a MAS to enhance the ability to have disaster information when an event occurs and perform an intelligent reasoning mechanism to act efficiently in a dynamic environment. Moreover, we illustrate the framework using a case study to determine the working of the proposed system. We develop ontology and a prototype model to demonstrate the scalability of our proposed system.

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A Context-Aware Route Finding Algorithm for Self-Driving Tourists Using Ontology

Cited by 5 publications

References 32 publications

Personalized Augmented Reality Based Tourism System: Big Data and User Demographic Contexts

Personalized Augmented Reality Based Tourism System: Big Data and User Demographic Contexts

Social Communication of Transportation: A Bridge Model Connecting Tourism Destination and Psychological Perception

Situation aware intelligent reasoning during disaster situation in smart cities

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