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Dynamic prediction of traffic conditions using streaming data and Bayesian approach

Posted on:2018-12-27Degree:M.A.ScType:Thesis
University:Ecole Polytechnique, Montreal (Canada)Candidate:Kianpour, MohammadFull Text:PDF
GTID:2442390002995691Subject:Civil engineering
Abstract/Summary:
Streaming data can be defined by its remarkable volume, generation of speed, richness of information, and diversity of information. Today, large volumes of data, produced by many new sources, can be classified as this type of data. Domains such as transportation and traffic engineering can benefit from these datasets. Using streaming data to create new methods of travel behaviour modelling can be helpful in three ways. First of all, it can reduce the required time and cost of collecting sufficient data from conventional methods. Secondly, it can increase the accuracy level of proposed models and implemented simulations. Finally, it can reduce dependency on traditional cross-sectional data.
Keywords/Search Tags:Data
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