Short-Term Traffic Flow Prediciton Based on Parallel Quasi-Newton Neural Network

摘要：

Identifying and predicting the situation of traffic flow play an important role in traveler information broadcast and real-time traffic control. In this paper, a short-term traffic flow prediction model based on the parallel selfscaling quasi Newton (SSPQN) neural network is presented. In this method, a set of parallel search directions are generated at the beginning of each iteration. Each of these directions is selectively chosen from a representative class of quasi-Newtun (QN) methods. Inexact line searches are then carried out to estimate the minimum point along each search direction. Experimental and analytical results demonstrate the feasibility of applying SSPQN to traffic flow prediction and prove that it can better satisfy real-time demand of traffic flow prediction.

关键词： Traffic flow prediction quasi-Newton (QN) methods computing parallelism neural network

作者: Guozhen Tan Huimin Shi Fan Wang Chao Deng

作者单位: Department of Computer Science and Engineering, Dalian University of Technology Dalian 116024, China

会议类型: 国际会议

会议名称: 2009 International Conference on Measuring Technology and Mechatronics Automation(ICMTMA 2009)(2009年检测技术与机电自动化国际会议)

会议地点: 张家界

会议语种:英文

页码: 305-308

在线出版日期: 2009-04-11（万方平台首次上网日期，不代表论文的发表时间）