摘要
目前的交通车辆调度系统中,依靠大量的经验策略对交通远程图像观察后制定规则,存在数据采集混乱,调度主观性强,调度效率低下的问题。将物联网的思想引入到大型交通车辆调度中,首先,使用物联网中的RIFD技术改造信息采集模块,配合GPS技术对大型交通车辆实现自动远距离、实时的数据采集,保证调度策略中的数据来源的充足与准确;在软件设计中,引入一种模糊函数制定车辆调度优化的评估模型,再使用粒子群优化算法对调度策略求解,解决了传统交通调度非线性强,无精确模型评定的缺陷。实验表明,本文的交通车辆调度方法较传统的车辆调度效率提高15%,具有很强的实用价值。
The present transportation vehicle scheduling rely on artificial observation after making to traffic remote images, because the data acquisition, scheduling subjectivity is strong, a scheduling inefficiencies. In this paper, a largescale transportation vehicle scheduling system based on Internet of things technology design. RIFD in the first, the use of the Internet of things technology combined with GPS technology for large transport vehicles to realize automatic remote, real-time data collection, to ensure adequate and accurate data sources in the scheduling policy; Again, introducing a fuzzy function is a scheduling optimization evaluation model, using particle swarm optimization algorithm for scheduling policies, solve the traditional traffic scheduling strong nonlinear, without precise model of evaluation of defects. Experiments show that the transportation vehicle scheduling method than the traditional vehicles dispatching efficiency increased by 15%, has a strong practical value.
出处
《科技通报》
北大核心
2013年第10期189-191,共3页
Bulletin of Science and Technology
关键词
交通车辆调度
RIFD
模糊函数
粒子群优化
transportation vehicle scheduling
RIFD
fuzzy function
particle swarm optimization
