Browsing by Subject "Markov processes"

Now showing 1 - 3 of 3
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    Analyzing information flow in social networks for knowledge discovery
    (2013-02) Pathak, Nishith
    In the last few years the online world has seen a surge in users’ social behavior. No longer is the image of a lone user surfing the web relevant anymore and with social sites such as Facebook, Twitter, etc. online users can now actively interact with other users. It is now quite common for web businesses to offer support for friends lists, forums, private message systems, community maintenance tools etc. As as result, not only are users finding more social satisfication online, but the businesses themselves are now able to interact with and monitor the communities around them. Consequently, large amounts of data are being collected from such “social systems”, which capture users’ participation in the community. The data can include user-user interactions as well as their activities with time stamps. The data is also unique in that it captures complex social phenomenon in a much more comprehensive manner and at a much more finer granularity, than any other traditional source of communication data. This presents rich opportunities for the development of knowledge discovery algorithms which will find immense value in revealing trends, latent structures or interesting behaviors in these social systems. In any social system, communication exposes people to information, opinions as well as behavior of other users. According to a well studied phenomenon in social science, summarized in the theory of contagion, users in such networks tend to develop beliefs, attitudes and assumptions that are similar to those of others around them. By “word-of-mouth” rumors, ideas, opinions, information, etc. can propagate to different regions in the network. The research presented in this thesis explores the analysis of such information flow in social networks from a variety of perspectives, including the network topology, actors’ interests, actors’ cognition and actors’ influence. It is shown that the proposed analyses techniques can discover valuable knowledge regarding community structure, user interests and sentiments, as well as prominent users in the community. Such knowledge is of immense value to online business owners, as it allows them to monitor and identify factors for improving the overall experience of their users.
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    Dynamic Estimation of Origin-Destination Patterns in Freeways
    (Minnesota Department of Transportation, 1994-05) Davis, Gary A.
    Any proposed traffic management action is essentially a forecast that the action will result in certain traffic conditions, but uncertainty concerning the amount and distribution of traffic demand will introduce random error between what is expected and what actually occurs. This report treats the problem of forecasting whether or not a given set of freeway on-ramp volumes are likely to cause over-capacity demand at some point in the freeway mainline. The main source of uncertainty in these forecasts concerns the freeway's origin-destination matrix, and four different methods for estimating this matrix from loop detector data are evaluated using Monte Carlo simulation. Only the method which explicitly modeled freeway traffic flow produced reasonably unbiased and efficient estimates, and it was concluded that successful estimation must be coupled with a good model of freeway traffic flow.
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    Real-Time Traffic Prediction for Advanced Traffic Management Systems: Phase I
    (Intelligent Transportation Systems Institute, University of Minnesota, 1995-10) Davis, Gary A.; Stephanedes, Yorgos J.; Kang, Jeong-Gyu
    It has been recommended that Advanced Traffic Management Systems (ATMS) must work in real-time, must respond to and predict changes in traffic conditions, and must included areawide detection surveillance. To support such ATMS, this project developed a tractable, stochastic model of freeway traffic flow and travel demand which satisfies three primary objectives. First, the model should generate real-time estimates of traffic state variables from loop detector data, which can in turn be used as time-varying initial conditions for more comprehensive simulation models, such as KRONOS or FREESIM. Second, the model should generate its own predictions of mainline and off-ramp traffic volumes, as well as calculate the expected error associated with these predictions, thus supporting the use of both deterministic and stochastic optimization for determining traffic management actions. Third, the model should be capable of full on-line implementation, in that it should be capable of estimating required parameters from traffic detector data. The basic model was developed by combining ideas from the theory of Markov population processes with a new for the relationship between traffic flow and density, producing a stochastic version of a simple-continuum model. Kalman filtering was then applied to the basic model to develop algorithms for (1) estimating from loop detector counts the traffic density in freeway sections broken down by destination off-ramp, (2) predicting main-line and off-ramp traffic volumes from given on-ramp volumes and, (3) computing adaptive estimates of the freeway's origin destination matrix from loop detector counts. Monte Carlo simulation tests were used to evaluate three different methods for off-line estimation of model parameters, as well as to assess the accuracy of the density estimates and volume predictions. The results indicated that the estimation and prediction model tends to be robust with respect to the parameter estimation scheme, and that the model generates a reasonable characterization of estimation and prediction uncertainty. Limited tests with field data tended to confirm the simulation results, and to emphasize the importance of real-time estimation of freeway origin-destination matrices in generating accurate predictions.