Urban impedance is an important consideration in assessments of transportation and land-use systems. This work leverages checkin records obtained from mobile social networks to build a fine-grained but inexpensive urban impedance model. Check-in records and road networks are collected and used to calculate and adjust the various parameters of the model, including path length, number and angle of turns, number and direction of junctions, and population density. Check-in records can filter functional locations and supply the time factor, thereby providing excellent advantages over traditional models that do not employ this data type. The proposed model is more accurate than traditional impedance models, as verified by experiments using Sina Weibo data in Tianjin City.
The research of topic trend prediction can be a good reference for maximizing the propagation effects of network advertisements as well as guiding and controlling the network consensus. This paper proposes PTEP(the Prediction of Topic Energy Peak) method to model the life cycle of a topic and predicts the time when a hot topic will outbreak. Firstly, taking the number and the authority of followers and the interest of users to a topic into consideration, we design a topic-related user authority (TRUA) algorithm to measure the authority of users. Secondly, we calculate the energy value considering both the tweets and users authority related to the topic. Thirdly, we measure the fluctuation of the energy value based on wavelet transformation. Finally, we present rules to predict topic trend. Experimental results show that our method can effectively predict the peak of a topic in advance with a low omission rate.
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