High throughput computing application to transport modeling

Optimization of road space allocation (RSA) from a network perspective is computationally challenging. Analogues to the Network Design Problem (NDP), RSA can be classified as a NP-hard problem. In large scale networks when the number of alternatives increases exponentially, there is a need for an efficient method to reduce the number of alternatives as well as a computational approach to reduce the computer execution time of the analysis. A heuristic algorithm based on Genetic Algorithm (GA) is proposed to efficiently select Transit Priority Alternatives (TPAs). In order to reduce the execution time, the GA is modified to implement two parallel processing techniques: A High Performance Computing (HPC) technique using Multi-threading (MT) and a High Throughput Computing (HTC) technique. The advantages and limitations of the MT and HTC techniques are discussed. Moreover, the proposed framework allows for a TPA to be analyzed by a commercial package which is a significant provision for large scale networks in practice.