Truck Volume Estimation via Linear Regression Under Limited Data

This paper employs linear regression algorithms in order to train models under the presence of limited training data. Usually in transportation applications, these models are built via Ordinary Least Squares and Stepwise Regression, which perform poorly under limited data. The algorithms presented in this paper have been extensively used in other scientific fields for problems with similar conditions and seem to partially or fully remedy this problem and its consequences. Four different algorithms are presented and several models are built. The models are used for truck volume prediction on highway sections in New Jersey, and results are compared to Stepwise Linear regression models

Truck Volume Estimation via Linear Regression Under Limited Data

This paper employs linear regression algorithms in order to train models under the presence of limited training data. Usually in transportation applications, these models are built via Ordinary Least Squares and Stepwise Regression, which perform poorly under limited data. The algorithms presented in this paper have been extensively used in other scientific fields for problems with similar conditions and seem to partially or fully remedy this problem and its consequences. Four different algorithms are presented and several models are built. The models are used for truck volume prediction on highway sections in New Jersey, and results are compared to Stepwise Linear regression models

Truck Volume Estimation via Linear Regression Under Limited Data

This paper employs linear regression algorithms in order to train models under the presence of limited training data. Usually in transportation applications, these models are built via Ordinary Least Squares and Stepwise Regression, which perform poorly under limited data. The algorithms presented in this paper have been extensively used in other scientific fields for problems with similar conditions and seem to partially or fully remedy this problem and its consequences. Four different algorithms are presented and several models are built. The models are used for truck volume prediction on highway sections in New Jersey, and results are compared to Stepwise Linear regression models

A bilevel model for transit vehicle allocation

Ovaj rad predstavlja formuliranje i rješenje projektantskih problema za dvorazinske tranzitne mreže u intermodalnom mrežnom okruženju. Niža razina dvorazinskog problema sastoji se od kombiniranog izbora i raspoređivanja intermodalne mrežne ravnoteže s asimetričnim interakcijskim povezivanjem i promjenjivim tranzitnim frekvencijama. Gornja razina je problem maksimizacije broja putnika koji koriste određeni oblik javnog prijevoza s brojem tranzitnih vozila dodijeljenih prema tranzitnim pravcima kao projektnih varijabli. Kao rješenje za dvorazinski problem predlaže se algoritam temeljen na osjetljivosti.The formulation and resolution of a bi-level transit network design problem in an intermodal network environment is presented in the paper. The lower level of this bi-level problem is a combined mode-choice/assignment intermodal network equilibrium with asymmetric link interactions and variable transit frequencies. The upper level is a transit ridership maximization problem, with the number of transit vehicles allocated toward transit routes as a design variable. A sensitivity based algorithm is proposed for resolution of the bi-level problem

Truck Volume Estimation via Linear Regression Under Limited Data

This paper employs linear regression algorithms in order to train models under the presence of limited training data. Usually in transportation applications, these models are built via Ordinary Least Squares and Stepwise Regression, which perform poorly under limited data. The algorithms presented in this paper have been extensively used in other scientific fields for problems with similar conditions and seem to partially or fully remedy this problem and its consequences. Four different algorithms are presented and several models are built. The models are used for truck volume prediction on highway sections in New Jersey, and results are compared to Stepwise Linear regression models