Abstract: The disclosed embodiments illustrate a method and a system for controlling KPI parameters of a transportation system. The method includes extracting historical commuting characteristics of one or more commuters, from a database server over a communication network. The method further includes generating a predictive model based on the extracted historical commuting characteristics. The method further includes generating a service schedule of one or more transportation services of the transportation system. The service schedule of the one or more transportation services may be generated by use of the generated predictive model, based on defined criteria of the transportation system. The method further includes controlling a KPI parameter of the transportation system to attain a desired KPI parameter of the KPI parameter, based on the generated service schedule, when the one or more transportation services are deployed at one or more time stamps.

Abstract: The disclosed embodiments illustrate a method and a system for controlling KPI parameters of a transportation system. The method includes extracting historical commuting characteristics of one or more commuters, from a database server over a communication network. The method further includes generating a predictive model based on the extracted historical commuting characteristics. The method further includes generating a service schedule of one or more transportation services of the transportation system. The service schedule of the one or more transportation services may be generated by use of the generated predictive model, based on defined criteria of the transportation system. The method further includes controlling a KPI parameter of the transportation system to attain a desired KPI parameter of the KPI parameter, based on the generated service schedule, when the one or more transportation services are deployed at one or more time stamps.

Abstract: LASSO constraints can lead to a Gaussian mixture copula model that is more robust, better conditioned, and more reflective of the actual clusters in the training data. These qualities of the GMCM have been shown with data obtained from: digital images of fine needle aspirates of breast tissue for detecting cancer; email for detecting spam; two dimensional terrain data for detecting hills and valleys; and video sequences of hand movements to detect gestures. Using training data, a GMCM estimate can be produced and iteratively refined to maximize a penalized log likelihood estimate until sequential iterations are within a threshold value of one another. The GMCM estimate can then be used to classify further samples. The LASSO constraints help keep the analysis tractibe such that useful results can be found and used while the result is still useful.