UC Davis Institute of Transportation Studies

Many important decision making processes in transportation planning and engineering involve repetitive computation of network performance, measured by total network delay, throughput, network efficiency, etc. The computational complexity imposed by repetitive evaluation of these measures, especially under user equilibrium condition, is a serious obstacle for timely decision making regarding transportation networks. This study applies Associative Memory (AM) techniques, which are conceptually and computationally simple, to quick estimation of these performance measures. The results of the numerical experiments were encouraging and the relative error on an average was found to be less than two percent. Furthermore, the applicability of this approximation method to bilevel network problems is explored through a study on the network recovery problem (NRP), which seeks a quick and effective repairing strategy for disturbed networks following natural or human-induced disasters.