No abstract

No abstract

No abstract

Word count: 28 4448 words text+ 29 188 words abstract+ 30 580 words references+ 31 5 tables/figures ×250 words (each) = 6466 words 32 ABSTRACT 1Recent methods in the literature to parallelize the traffic assignment problem consider partitioning 2 a network into subnetworks to reduce the computation time for large-scale networks. In this arti-3 cle, we seek a partitioning method that generates subnetworks minimizing the computation time 4 of a decomposition approach for solving the traffic assignment (DSTAP). We aim to minimize the 5 number of boundary nodes, the inter-flow between subnetworks, and the computation time when 6 the traffic assignment problem is solved in parallel on the subnetworks. We test two different meth-7 ods for partitioning. The first is an agglomerative clustering algorithm heuristic that decomposes 8 a network with the objectives of minimizing subnetwork boundary nodes. The second is a flow 9 weighted spectral clustering algorithm that uses the normalized graph Laplacian to partition the 10 network. 11We assess the performance of both algorithms on different test networks. The results indi-12 cate that the agglomerative heuristic generates subnetworks with a low number of boundary nodes, 13 which reduces the per iteration computation time of DSTAP. However, the partitions generated may 14 be heavily imbalanced leading to significantly higher computation time when the subnetworks are 15 solved in parallel separately at a certain DSTAP iteration. For the Austin network partitioned into 16 4 subnetworks, the agglomerative heuristic requires 3.5 times more computational time to solve 17 the subnetworks in parallel. We also show that the spectral partitioning method is superior in terms 18 of minimizing the inter-flow between subnetworks. This leads to a faster convergence rate of the 19 DSTAP algorithm. 20

No abstract