Urban Security System Based on Quadrants

Gómez, Jorge Gómez; Hernandez, Velssy Liliana; Cobo, Luis

doi:10.1016/j.procs.2015.05.063

Cited by 6 publications

(13 citation statements)

References 6 publications

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“…Most authors took a content-based filtering approach in the middle and proposed recommendation systems with context, secured, and mapped features. 13,15,25 3. Collaborative filtering: This recommender system delivers recommendations in which people have the same tastes.…”

Section: Recommendation Approachesmentioning

confidence: 99%

“…and group offer features. 7,10,14,15,25,27,30,[32][33][34][35][36][37][38] 5. Semantic-based: This recommender system utilized web resources in the form of ontologies.…”

Section: Recommendation Approachesmentioning

confidence: 99%

“…Some authors also highlighted this approach in taxi recommendations in taxi searching and public security. 25,28,40 8. Demographic-based: Demographic factor also plays a vital role in personalized recommendations.…”

Section: Recommendation Approachesmentioning

confidence: 99%

“…Authors prefers their work to be implemented in this platform. 15,25,28,45,58,88,110,115 3. Web: This is a favorite and effects place to analyze the taxi recommender system, such that the real-time location of drivers and passengers can be maintained by utilizing POI, GPS, and RFID.…”

Section: Platform and Devices Of Recommender Systemsmentioning

confidence: 99%

“…In this phase user send his location, POI, information to GPS devices and track fast, vacant, and nearby taxi. 15,18,25,30,32,33,46,53,97,98,103,105,108,110,[227][228][229][230][231]…”

Section: Platform and Devices Of Recommender Systemsmentioning

confidence: 99%

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Towards the significance of taxi recommender systems in smart cities

Katarya

2022

Concurrency and Computation

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Since their launch in the early 1990's, recommender systems (RSs) have played an essential role in information filtering and providing personalized information to users by utilizing the web, mobile, context, personalized, and practical applications. Many research articles have been published in various application domains such as movies, books, documents, news, images, music, shopping, TV programs, tourism, group, social, taxi, and others. However, from these domains, taxi recommendation is a new and thrust area of research, as publications in this domain are few and recently published.Taxi recommendations are associated with drivers-passengers with their: behavior, hotspots, trajectory, point of interest, route planning or prediction, taxi finding, anomaly or fraud detection, urban computing, mobility patterns and so forth. Due to this diversity and various agendas in taxi recommendations, more exploration is required, as it is a less mature and growing field than other domains. So we have done a literature review in the taxi domain by classification, incorporating reputed articles published from 2001 to August 2022. We include the reputed papers which highlight, analyze and perform studies only on the taxi domain. This article categorizes and discusses the research and development in taxi recommendations as nature, approaches, algorithms, methods, datasets, models, devices, information, and evaluation techniques. This survey article will actively help the researchers and professionals understand the current situations in taxi RS and resolve future trends, opportunities, and research.

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Section: Recommendation Approachesmentioning

confidence: 99%

“…and group offer features. 7,10,14,15,25,27,30,[32][33][34][35][36][37][38] 5. Semantic-based: This recommender system utilized web resources in the form of ontologies.…”

Section: Recommendation Approachesmentioning

confidence: 99%

Section: Recommendation Approachesmentioning

confidence: 99%

Section: Platform and Devices Of Recommender Systemsmentioning

confidence: 99%

“…In this phase user send his location, POI, information to GPS devices and track fast, vacant, and nearby taxi. 15,18,25,30,32,33,46,53,97,98,103,105,108,110,[227][228][229][230][231]…”

Section: Platform and Devices Of Recommender Systemsmentioning

confidence: 99%

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