【摘要】 BP广泛用于短时交通流预测.为了降低大规模交通流的预测时间,已提出一些并行的BP方法,但在很多情况下其并行计算的效率仍有待提高.提出一个贪婪动态负载均衡(简称GC-DLB)算法,能够提高并行计算效率和降低预测时间,并在工作站网络(NOW)系统中对该算法进行了实现.与蝶形并行BP交通流预测方法(简称DP-BP)相比较,理论和实验结果说明了DP-BP方法结合GC-DLB算法可降低预测时间.更多还原

【Abstract】 The back propagation(BP) is wildly used in short-term traffic flow forecasting which requires the training set size be much larger than the network size.Although a number of parallel BP approaches have been proposed for reducing the forecasting time with a large samples of traffic flow data.However,still higher performance needs to be further delivered in many cases.An load balancing strategy based on greedy algorithm considering communication cost(GC-DLB) is proposed to improve parallel computing efficiency and reduce predicting time and GC-DLB algorithm is implemented in network-of-workstation(NOW) system.Comparing with the dish parallel BP approach(DP-BP),our results indicate that DP-BP approach combining with GC-DLB algorithm outperforms DP-BP approach itself,and can reduce forecasting time.更多还原