Link- and Path-Based Traffic Assignment Algorithms: Computational and Statistical Study

Abstract

The computational performance of five algorithms for the traffic assignment problem (TAP) is compared with that of mid- to large-scale randomly generated grid networks. The applied procedures include the Frank-Wolfe, PARTAN, gradient projection, restricted simplicial decomposition, and disaggregate simplicial decomposition algorithms. A statistical analysis is performed to determine the relative importance of various properties (network size, congestion level, solution accuracy, zone-node ratio) of the traffic assignment problem for the five selected algorithms. Regression models, which measure central processing unit time and number of iterations consumed by each algorithm using various factors and their combinations, are derived to provide a quantitative evaluation. Ultimately, the findings of this research will be useful in guiding transportation professionals to choose suitable solution algorithms and to predict the resulting algorithm performance in TAPs.