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With the growth of the Mobile Internet, people have become active in both the online and offline worlds. Investigating the relationships between users’ online and offline behaviors is critical for personalization and content caching, as well as improving urban planning. Although some studies have measured the spatial properties of online social rel...
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The compact city, as a sustainable concept, is intended to augment the efficiency of urban function. However, previous studies have concentrated more on morphology than on structure. The present study focuses on urban structural elements, i.e. urban hotspots consisting of high-density and high-intensity socioeconomic zones, and explores the economi...
The compact city, as a sustainable concept, is intended to augment the efficiency of urban function. However, previous studies have concentrated more on morphology than on structure. The present study focuses on urban structural elements, i.e., urban hotspots consisting of high-density and high-intensity socioeconomic zones, and explores the econom...
Citations
... Therefore, a layer in a network can vary according to the context (Kivelä et al. 2014). Thus, studies such as the spread of diseases, navigation, and synchronization in multilayer networks have attracted significant attention (Sahneh et al. 2019;Jacobsen et al. 2018;Lv et al. 2018). ...
In times of a global pandemic, public transit can be crucial to spreading viruses, especially in big cities. Many works have shown that the human infection risk could be extremely high due to the length of exposure time, transmission routes, and structural characteristics during public transportation, and this can result in the rapid spread of the infection. Vaccines are often part of strategies to reduce contagion; however, they can be scarce in pandemic settings. Considering real-world and large-scale traffic data, this work proposes using time-varying multilayer networks to identify the main critical places to be prioritized in interventions, such as vaccination campaigns, to help reduce contagion on public transit. We exemplify our strategy in different vaccination scenarios. First, when considering only critical bus stops as priority vaccination points, determined by our approach, we indicate that focusing on vaccination in these locations reduces the spread of infection using fewer doses than a random vaccination. In another experiment, we demonstrate the flexibility of our approach in identifying other critical points of interest, healthcare units in this case. Vaccination in these vital health units could also be a viable strategy to curb contagion using a predetermined number of doses. The approach proposed in this study is not limited to vaccination strategies. It also applies to other problems that share similar properties, even in several different contexts, such as optimization in public transit or exploring different points of interest to gather insights from other issues of interest.
... Portanto, uma camada em uma rede pode variar de acordo com o contexto [Kivelä et al. 2014]. Assim, estudos como disseminação de doenças, navegação e sincronização em redes multicamadas têm atraído significativa atenção [Sahneh et al. 2019, Jacobsen et al. 2018, Lv et al. 2018]. ...
Em tempos de pandemia, o transporte público pode ser crucial para a disseminação de vírus, principalmente nas grandes cidades. As vacinas costumam fazer parte das estratégias para reduzir o contágio; no entanto, estas podem ser escassas em cenários pandêmicos. Utilizando dados do sistema de transporte público, este trabalho propõe o uso de redes multicamadas variantes no tempo para identificar os principais locais críticos a serem considerados prioritários em intervenções, como campanhas de vacinação, para ajudar a reduzir o contágio nesse meio de locomoção. Nossa abordagem considera os pontos de ônibus críticos como pontos prioritários de vacinação, indicando que a vacinação nesses locais reduz a propagação da infecção usando menos doses do que uma vacinação aleatória. A abordagem proposta neste estudo não se limita às estratégias de vacinação, sendo também aplicável a outros problemas que compartilham propriedades semelhantes, mesmo em contextos diferentes.
... Based on the current requirement to address the personalization and information overload problem, research on recommender systems is boosted for the development of a highly intelligent and autonomous information retrieval system for the pertinent information generation. Recommender Systems (RSs) are employed in the various application 1 3 areas such as e-commerce, movies, books, music, tags, news and travel [1][2][3][4][5][6][7][8]. The recent research and development with the urban computing have brought Location-Based Services (LBSs) to the hands of a common man though GPS based mobile phones [2]. ...
The development of Recommender Systems (RSs) aims to generate recommendations with high quality, and on the other hand, the privacy of the user is not considered as a significant issue. Especially, when the RS utilizes the cloud platform for the recommendation generation process, the privacy of the user is needed to be preserved to ensure the security of user’s sensitive data. In this paper, we present an Improved MORE approach as a Fully Homomorphic Encryption algorithm to secure user’s data in the cloud environment. To generate secure location recommendations to the users, we present SECure RECommendation SYstem (SECRECSY) framework by protecting user’s sensitive privacy information in the cloud during the recommendation generation process. To meet the increasing demands of group recommendations, we extend our SECRECSY as Group Recommendation Model to suggest POIs to the group of users. The experimental results and findings are helpful to the researchers for developing better RSs for both individual and group users.
... Correspondingly, no sensitive data will be included in the sessions over the wireless transmission, and no one has access to the profile information. However, the profile independent trajectories expose the intent of users, which can be leveraged for PoI recommendation [7,8]. Besides, these MSN services and apps offer many ways to establish social connections (e.g., dating) between MSN users within a location proximity. ...
People or friends may encounter with each other offline, when they have a location proximity. With the rapid development of the wireless sensor network, smart city applications can leverage the sensed data of people’s mobility or trajectory to predict their future encounter opportunity and then arrange their offline activities (e.g., meeting, travel) accordingly. This paper studies the encounter prediction problem of mobile users by mining the similarity between their sensed mobile trajectories. We define the similarity of two mobile trajectories both temporally and spatially, and then propose two approaches, namely a probabilistic similarity maximization algorithm and a machine leaning based prediction algorithm, for addressing the encounter prediction problem. Results over a real-world social network dataset show that the proposed recurrent neural network based model can predict the encounter of two users precisely, and it outperforms the probabilistic algorithm and other algorithms, in terms of the precision and F1 score.
Video accounts for a large proportion of traffic on the Internet. Understanding its geographical viewing patterns is extremely valuable for the design of Internet ecosystems for content delivery, recommendation and ads. This paper aims to investigate the problem that whether there exists distinct viewing patterns among the neighborhoods of a large-scale city. To achieve this, we need to address several challenges including unknown of patterns profiles, complicate urban neighborhoods, and comprehensive viewing features. The contributions of this paper include two aspects. First, we design a framework to automatically identify geographical video viewing patterns in urban neighborhoods. Second, by using a dataset of two months real video requests in Shanghai collected from one major ISP of China, we make a rigorous analysis of video viewing patterns in Shanghai. Our study reveals the following important observations. First, there exists four prevalent and distinct patterns of video access behavior in urban neighborhoods, which are corresponding to four different geographical contexts. Second, there exists significant features that distinguish different patterns, e.g., the probabilities of viewing TV plays at midnight, and viewing cartoons at weekends can distinguish the two viewing patterns corresponding to downtown and suburb regions.
With the rapid growth of mobile networks and smart devices, a large number of people prefer to visiting video services via mobile devices. This generates massive data traffic, and thus increases the load of the cellular networks. To deal with it, we need to investigate the usage patterns in the video consumption. In this paper, we take a data-driven analysis of mobile video services by classifying them into three major types: traditional video portals, user-generated video services and personalized livestreaming. We collect a large dataset of 25,937,758 logs from 455 thousand users, and we find that 1) for the same kind of video services users exhibit high loyalty; 2) in consecutive days the traffic peaks have differences among different service types; 3) more video traffic is generated from personalized livestreaming services, and it keeps increasing after midnight at weekdays in the downtown; 4) traffic consumption of user-generated service exhibits great differences under different functional regions; 5) individual users are prone to click within the same service type but possible different time gaps during the consecutive views. Lastly, we utilize these findings to discuss the potential applications in the improvements of cellular networks and video services.
