A Travel Route Recommendation System Based on Smart Phones and IoT Environment

Bin, Chenzhong; Gu, Tianlong; Sun, Yanpeng; Chang, Liang; Sun, Lei

doi:10.1155/2019/7038259

Cited by 31 publications

(14 citation statements)

References 44 publications

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“…In the study, a clustering scheme was developed to prolong the network lifetime by distributing energy consumption among clusters. In [31], a novel travel route recommendation system was proposed that collected tourist on-site travel behavior data automatically regarding a specific POI that was based on smart phone and Internet of Things technologies. The tour-route-recommendation algorithm was then created to search and rank the tangible travel routes.…”

Section: Addressing Challenges For Researchmentioning

confidence: 99%

“…Ref. [31] collected tourists' traveling data on the website and analyzed the tourists' behaviour and, based on the website tourists' mobile data as well as the mined POIs, it set up the tour route recommendation algorithm. Ref.…”

Section: Introductionmentioning

confidence: 99%

“…Refs. [30][31][32] tended to study the big data that were obtained from social networks on mobile devices and website. The big data could be used as the basic data to do clustering on tourist attractions and tourists or could be used to study the tourists' mobility and traveling behaviour.…”

Section: Introductionmentioning

confidence: 99%

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Tour-Route-Recommendation Algorithm Based on the Improved AGNES Spatial Clustering and Space-Time Deduction Model

Zhou

Tian

2022

IJGI

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This study designed a tour-route-planning and recommendation algorithm that was based on an improved AGNES spatial clustering and space-time deduction model. First, the improved AGNES tourist attraction spatial clustering algorithm was created. Based on the features and spatial attributes, city tourist attraction clusters were formed, in which the tourist attractions with a high degree of correlation among attributes were gathered into the same cluster. It formed the precondition for searching tourist attractions that would match tourist interests. Using tourist attraction clusters, this study also developed a tourist attraction reachability model that was based on tourist-interest data and geospatial relationships to confirm each tourist attraction’s degree of correlation to tourist interests. A dynamic space-time deduction algorithm that was based on travel time and cost allowances was designed in which the transportation mode, time, and costs were set as the key factors. To verify the proposed algorithm, two control algorithms were chosen and tested against the proposed algorithm. Our results showed that the proposed algorithm had better results for tour-route planning under different transportation modes as compared to the controls. The proposed algorithm not only considered time and cost allowances, but it also considered the shortest traveling distance between tourist attractions. Therefore, the tourist attractions and tour routes that were suggested not only met tourist interests, but they also conformed to the constraint conditions and lowered the overall total costs.

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Section: Addressing Challenges For Researchmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Tour-Route-Recommendation Algorithm Based on the Improved AGNES Spatial Clustering and Space-Time Deduction Model

Zhou

Tian

2022

IJGI

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“…The traffic condition is used to find the best type of vehicle which has waiting time while traveling. Others existing system also tried to combine several attributes to define the routes [41,[52][53][54].…”

Section: Attributes Of Road Situationmentioning

confidence: 99%

Contextual Route Recommendation System in Heterogeneous Traffic Flow

et al. 2021

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The traffic composition in developing countries comprises of variety of vehicles which include cars, buses, trucks, and motorcycles. Motorcycles dominate the road with 77.5% compared to other types. Meanwhile, route recommendation such as navigation and Advanced Driver Assistance Systems (ADAS) is limited to particular vehicles only. In this research, we propose a framework for a contextual route recommendation system that is compatible with traffic conditions and vehicle type, along with other relevant attributes (traffic prediction, weather, temperature, humidity, heterogeneity, current speed, and road length). The framework consists of two phases. First, it predicts the traffic conditions by using Knowledge-Growing Bayes Classifier on which the dataset is obtained from crawling the public CCTV feeds and TomTom digital map application for each observed road. The performances of the traffic prediction are around 60.78–73.69%, 63.64–77.39%, and 60.78–73.69%, for accuracy, precision, and recall respectively. Second, to accommodate the route recommendation, we simulate and utilize a new measure, called road capacity value, along with the Dijkstra algorithm. By adopting the compatibility, the simulation results could show alternative paths with the lowest RCV (road capacity value).

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“…In [3] the authors identified that current recommendation systems rarely aim at recommending tangible itineraries for tourists within a specific POI due to the lack of onsite travel behavioural data and related route mining algorithms. To this end, a novel travel route recommendation system was proposed, which collected tourist onsite travel behaviour data automatically regarding a specific POI, based on smartphone and Internet of Things (IoT) technology.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Systems applied to tourism: A Survey

Duarte,

Torres,

Ribeiro

et al. 2020

Preprint

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Artificial Intelligence (AI) has been improving the performance of systems for a diverse set of tasks and introduced a more interactive generation of personal agents. Despite the current trend of applying AI for a great amount of areas, we have not seen the same quantity of work being developed for the tourism sector. This paper reports on the main applications of AI systems developed for tourism and the current state of the art for this sector. The paper also provides an up-to-date survey of this field regarding several key works and systems that are applied to tourism, like Personal Agents, for providing a more interactive experience. We also carried out an in-depth research on systems for predicting traffic human flow, more accurate recommendation systems and even how geospatial is trying to display tourism data in a more informative way and prevent problems before they arise.

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A Travel Route Recommendation System Based on Smart Phones and IoT Environment

Cited by 31 publications

References 44 publications

Tour-Route-Recommendation Algorithm Based on the Improved AGNES Spatial Clustering and Space-Time Deduction Model

Tour-Route-Recommendation Algorithm Based on the Improved AGNES Spatial Clustering and Space-Time Deduction Model

Contextual Route Recommendation System in Heterogeneous Traffic Flow

Artificial Intelligence Systems applied to tourism: A Survey

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