In recent years, with the rapid development of economy, more and more urban residents, while owning their own motor vehicles, are also troubled by the traffic congestion caused by the backward traffic facilities or traffic management methods. The loss of productivity, car accidents, high emissions, and environmental pollution caused by traffic congestion has become a huge and increasingly heavy burden on all countries in the world. Therefore, the prediction of urban road network traffic flow and the rapid and accurate evaluation of traffic congestion are of great significance to the study of urban traffic solutions. This paper focuses on how to apply data science technologies on vehicular networks data to present a prediction method for traffic congestion based on both real-time and predicted traffic data. Two evaluation frameworks are established, and existing methods are used to compare and evaluate the accuracy and efficiency of the presented method.

With the development of wireless Internet and the popularity of location sensors in mobile phones, the coupling degree between social networks and location sensor information is increasing. Many studies in the Location-Based Social Network (LBSN) domain have begun to use social media and location sensing information to implement personalized Points-of-interests (POI) recommendations. However, this approach may fall short when a user moves to a new district or city where they have little or no activity history and social network friend information. Thus, a need to reconsider how we model the factors influencing a user’s preferences in new geographical regions in order to make personalized and relevant recommendation. A POI in LBSNs is semantically enriched with annotations such as place categories, tags, tips or user reviews which implies knowledge about the nature of the place as well as a visiting person’s interests. This provides us with opportunities to better understand the patterns in users’ interests and activities by exploiting the annotations which will continue to be useful even when a user moves to unfamiliar places. In this research, we proposed a location-aware POI recommendation system that models user preferences mainly based on user reviews, which shows the nature of activities that a user finds interesting. Using this information from users’ location history, we predict user ratings by harnessing the information present in review text as well as consider social influence from similar user set formed based on matching category preferences and similar reviews. We use real data sets partitioned by city provided by Yelp, to compare the accuracy of our proposed method against some baseline POI recommendation algorithms. Experimental results show that our algorithm achieves a better accuracy.

It is very important to have a comprehensive understanding of the health status of a country’s population, which helps to develop corresponding public health policies. Correct inference of the underlying cause-of-death for citizens is essential to achieve a comprehensive understanding of the health status of a country’s population. Traditionally, this relies mainly on manual methods based on medical staff’s experiences, which require a lot of resources and is not very efficient. In this work, we present our efforts to construct an automatic method to perform inferences of the underlying causes-of-death for citizens. A sink algorithm is introduced, which could perform automatic inference of the underlying cause-of-death for citizens. The results show that our sink algorithm could generate a reasonable output and outperforms other stat-of-the-art algorithms. We believe it would be very useful to greatly enhance the efficiency of correct inferences of the underlying causes-of-death for citizens.

Traveling to a new region has become a very common thing for people, due to work or life requirement. With the development of recommendation engine and the popularity of social media network, people are more and more used to relying on personalized Points-of-Interest (POI) recommendations. However, traditional approaches can fail if users moves to a region where they had little or no active history or even social network friends information before. Under the requirement of smart city construction, the need to give high quality personalized POI recommendation when a user travels to a new region has arisen. Fortunately, with the widespread of wireless Internet, the booming of Internet-of-Things (IoT) and the common-usage of location sensors in mobile phones, the coupling degree between social media networks and location information is ever increasing, which could leads us to a new way to solve this problem in the ear of Big Data. In this research, we presented New Place Recommendation Algorithm (N-PRA) which is designed based on Latent Factor model. Many different types of social media contexts (time-related and location-related), such as a user's interest fluctuation, different types of POIs' popularity fluctuation, types of POIs, the influence of geographical neighborhood on POIs, and user's social network friendship are taken into consideration in this approach. The algorithm presented is verified on Yelp, an open-source real urban data-set, and compared against several other baseline POI recommendation algorithms. Experimental results show that the algorithm presented in this paper could achieve a better accuracy.

Through reviewing previous research papers based on Theory of Reasoned Action and Technology Acceptance Model, combined with the characteristics of mobile value-added Coloring Ring Back Tone business, this study discussed the factors influencing consuming intention of Coloring Ring Back Tone, developed a theoretical model of Coloring Ring Back Tone consumption intention and put forward research hypotheses and scale. Based on questionnaire survey, reliability and validity test were performed using SPSS13.0, hypotheses test were conducted by LISREL8.7. The results show that variables of perceived usefulness, perceived ease of use, perception entertainment and perceived personality have impact on attitude, subjective norm and perceived price are the important influencing factors of consuming intention as well.