This article provides insights into consumer behavior, and the results have important implications for designers, managers, marketers, and system providers of 3G value-added services to explore a conceptual model for analyzing customers' perceptions of using 3G value-added services. An empirical investigation was carried out to test the hypotheses. The samples include 826 professional participants. Structural equation modeling (SEM) is used to test the relationships of the model. After reviewing the previous research papers, a conceptual model of customer adoption is developed and nine important factors are proposed, namely, perceived usefulness, perceived ease of use, perceived security, perceived price, use experience, perceived enjoyment, need for uniqueness, social influence, context and compatibility. Then a big sample of questionnaire investigation in Chinese 3G value-added service market is conducted. The empirical findings are as follows: (a) security and social influence are two of the most important factors in 3G market; (b) 3G value-added services must be enjoyable and useful; (c) using context should be friendly and compatible. The results not only help to develop a sophisticated understanding of 3G adoption theories for researchers but also offer useful knowledge to those involved in promoting 3G value-added services to potential purchasers.
Space–time correlation analysis has become a basic and critical work in the research on road traffic congestion. It plays an important role in improving traffic management quality. The aim of this research is to examine the space–time correlation of road networks to determine likely requirements for building a suitable space–time traffic model. In this paper, it is carried out using traffic flow data collected on Beijing’s road network. In the framework, the space–time autocorrelation function (ST-ACF) is introduced as global measure, and cross-correlation function (CCF) as local measure to reveal the change mechanism of space–time correlation. Through the use of both measures, the correlation is found to be dynamic and heterogeneous in space and time. The finding of seasonal pattern present in space–time correlation provides a theoretical assumption for traffic forecasting. Besides, combined with Simpson’s rule, the CCF is also applied to finding the critical sections in the road network, and the experiments prove that it is feasible in computability, rationality and practicality.
Security provisioning has become a key issue in wireless multimedia networks due to their vital roles in supporting various services. Conventional security solutions have deficiencies in computing efficiency and addressing emerging security challenges. In addition, traditional upper-layer authentication ignores the protection of the physical layer, leading to severe privacy data leakage. In this paper, we envision a new deep learning (DL)-enabled security authentication scheme to overcome these issues, while implementing Blind Feature Learning (BFL) and Lightweight Physical Layer Authentication (LPLA). Specifically, an intelligent authentication method is developed by exploring neural networks at the data collection unit to learn the characteristics of data. Then we propose a holistic authentication scheme based on machine learning to identify malicious multimedia devices. Experimental analysis verifies that the proposed scheme can guarantee the privacy of wireless multimedia sensors and achieve lightweight authentication. Performance results indicate that artificial intelligence-enabled authentication scheme improves the overall security of multimedia networks. INDEX TERMS Multimedia network, machine learning, security, neural network.
With the rapid development of metro systems, it has become increasingly important to study phenomena such as passenger flow distribution and passenger boarding behavior. It is difficult for existing methods to accurately describe actual situations and to extend to the whole metro system due to the limitations from parameter uncertainties in their mathematical models. In this article, we propose a passenger‐to‐train assignment model to evaluate the probabilities of individual passengers boarding each feasible train for both no‐transfer and one‐transfer situations. This model can be used to understand passenger flows and crowdedness. The input parameters of the model include the probabilities that the passengers take each train and the probability distribution of egress time, which is the time to walk to the tap‐out fare gate after alighting from the train. We present the likelihood method to estimate these parameters based on data from the automatic fare collection and automatic vehicle location systems. This method can construct several nonparametric density estimates without assuming the parametric form of the distribution of egress time. The EM algorithm is used to compute the maximum likelihood estimates. Simulation results indicate that the proposed estimates perform well. By applying our method to real data in Beijing metro system, we can identify different passenger flow patterns between peak and off‐peak hours.
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