Towards building real-time, convenient route recommendation system for public transit

Bajaj, Garvita; Agarwal, Rachit; Bouloukakis, Georgios; Singh, Pushpendra; Georgantas, Nikolaos; Issarny, Valérie

doi:10.1109/isc2.2016.7580779

Cited by 8 publications

(3 citation statements)

References 16 publications

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“…Several recommendation approaches have been proposed in various smart city contexts, including smart health care, 7,25 smart markets, 8 smart transportation, 26,27 smart parking, 9,10 ride sharing, 11 e‐governance, 12 smart mobility, 13 smart tourism services, and attractions 14–16 (see Table 2). To achieve the smart service recommendation task, various methods have been adopted, mainly heuristic based and model based.…”

Section: Related Workmentioning

confidence: 99%

Cross‐network service recommendation in smart cities

Mezni,

Sellami,

Al‐Rasheed

et al. 2024

Concurrency and Computation

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SummaryNowadays, Internet of Things, artificial intelligence, cloud computing, and other revolutionary technologies (e.g., edge and fog computing) have become the pillar of smart cities. These latter make users' lives easier, thanks to a wide variety of smart services offered in different dimensions (e.g., smart living, smart mobility, smart economy, smart governance). However, the rapid adoption of smart services by users and the full servicelization of several cities around the world is faced with two major issues: the lack of knowledge regarding smart services' capacities (e.g., features, contextual requirements, pricing models, privacy policies, provisioning terms, etc.), and the lack of unified rating and quantification of smart services' QoS behavior. Indeed, interested users often exploit traditional search tools (e.g., Web search engines, social networks) to find and rate the needed services. This behavior has scattered the smart services' usage data (e.g., users contexts, ratings) across multiple providers platforms, which makes the search task beyond the capacity of users and, even, other service providers. Although recommender systems are a natural solution to exempt users from exploring the huge space of the offered smart services, current recommendation approaches for smart city environments are unable to deliver correct recommendations. In fact, they have been initially designed to single‐network settings (a single service repository), while smart services' consumers often are involved in multiple provider platforms. To the best of our knowledge, there exists no approach that treated smart service recommendation across multiple information networks. Therefore, the goal of this paper is to propose a cross‐network recommender system for smart cities. We first model the multiplex network of smart services' providers as a multirelational fuzzy lattice family thanks to fuzzy relational concept analysis (fuzzy RCA), which is a powerful mathematical method for data analysis and clustering. We also use the concept of anchor users to connect providers networks via the users involved in more than one provider platform. Guided by anchors' cross‐network relations, we compute the similarity between users and we define algorithms for exploring the smart services' information network, i.e. lattice family. Extensive experiments have proved the effectiveness of cross‐network recommendation and the quality of produced recommendations, compared to state‐of‐the‐art single‐network recommendation.

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Section: Related Workmentioning

confidence: 99%

Cross‐network service recommendation in smart cities

Mezni,

Sellami,

Al‐Rasheed

et al. 2024

Concurrency and Computation

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“…• It requires the stability of the Internet connection. [37] • An Android App to evaluate the commuter's satisfaction and identify the necessity for personalized route recommendations in public transit based on commuters' feedback.…”

Section: Figure 2 Schematic Description Of the City Platform Layer Amentioning

confidence: 99%

Enabling Customizable Services for Multimodal Smart Mobility With City-Platforms

et al. 2021

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In the last decades, the cities' capacity for generating digital information has grown exponentially. In this context, the successful implementation of smart cities' concept depends on the current possibility of handling the significant volumes of sensed data. This is particularly notorious in the case of urban mobility. Researchers in the field of urban planning have shown a great interest in urban mobility problems, proposing different route recommendation services towards making it easier and safer to move around the city. This paper addresses the development of an urban data platform and how to obtain and integrate information from sensors and other data sources to provide aggregated and intelligent views of raw data to support urban mobility. With the aim of evaluating the efficiency of the developed platform, we present an intelligent urban mobility solution, where the context-awareness, user preferences, and environmental factors play a significant role in the process of route planning. Finally, our work provides an experiment to assess different long-range wireless communication technologies to enable its implementation within an urban environment.

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“…RabbitMQ, uma das implementações open-source mais populares deste paradigma, permite que aplicações se comuniquem de maneira assíncrona através da publicação e consumo de mensagens em um message broker [Dobbelaere and Esmaili 2017]. A decisão de utilizar um message brokeré baseada na forma como o CityPulse Framework [Puiu et al 2016] realiza a comunicação entre seus diferentes módulos: o RabbitMQ, também foi utilizado em [Bajaj et al 2016] com o mesmo objetivo. Outra vantagem de utilizarmos o RabbitMQ,é o fato de ser uma solução de message broker multiplataforma.…”

Section: Ferramenta De Extração E Tratamentounclassified

Um Framework de Extração e Etiquetamento de Informações de Trânsito

Teteo

Moura

Soares

et al. 2019

Anais Do Workshop Em Desempenho De Sistemas Computacionais E De Comunicação (WPerformance)

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Com o grande avanço nas tecnologias computacionais, está cada vez mais possı́vel usar as redes sociais para a coleta e análise de informações de indivı́duos, comunidades e a respeito da dinâmica das cidades em tempo real. O excesso de informação, porém, é um desafio até mesmo no contexto das cidades, onde muitos eventos ocorrem em paralelo. Neste contexto, este trabalho propõe um framework que tem como função facilitar a extração, tratamento e identificação de eventos e suas localidades em tweets escritos na Lı́ngua Portuguesa utilizando a técnica Conditional Random Fields para realizar a tarefa de Reconhecimento de Entidades Nomeadas. Os resultados obtidos demonstram o potencial da ferramenta em identificar eventos de trânsito em uma dada localidade de interesse.

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Towards building real-time, convenient route recommendation system for public transit

Cited by 8 publications

References 16 publications

Cross‐network service recommendation in smart cities

Cross‐network service recommendation in smart cities

Enabling Customizable Services for Multimodal Smart Mobility With City-Platforms

Um Framework de Extração e Etiquetamento de Informações de Trânsito

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