摘要
目前高速公路拥堵状况发生频率越来越高,为给驾驶者提供便利的出行路径,减缓道路交通拥堵状态,以流量统计为基础,设计了基于改进遗传算法的高速公路交通拥堵状况预测模型。利用固定与移动检测技术采集流量、密度以及速度等宏观交通流量数据,针对冗余数据、缺失数据以及错误数据等异常类参数,采取不同的识别与处理方法,得到有效且完整的流量数据;利用反向传播神经网络与支持向量机回归网络改进遗传算法,建立两个子预测模型;通过加权处理两个模型权重构建混合预测模型,根据子预测模型拥堵预测偏差,结合最优权值组合策略修正混合预测模型的权值系数。实验结果表明,设计模型能划分目标高速公路的交通拥堵状况等级,可依据流量、速度以及占有率等数据预测拥堵状况,且模型预测精度较高,具有理想的预测有效性与准确性。
The frequency of highway congestion is increasing. In order to provide convenient travel path for drivers and slow down road traffic congestion, according to traffic statistics a highway traffic congestion prediction model based on improved genetic algorithm is designed. The fixed and mobile detection technology is used to collect macro traffic flow data such as flow, density and speed. Different identification and processing methods are adopted for abnormal parameters such as redundant data, missing data and error data to obtain effective and complete traffic flow data. The back propagation neural network and support vector machine regression network are used to improve the genetic algorithm. Two sub prediction models are established, and a hybrid prediction model is constructed by weighting the weights of the two models. According to the congestion prediction deviation of the sub prediction model, the weight coefficient of the hybrid prediction model is modified combined with the optimal weight combination strategy. The experimental results show that the design model can divide the traffic congestion level of the target expressway, and predict the congestion status according to the data of traffic flow, speed and occupancy rate, and the model has high prediction accuracy and ideal prediction effectiveness and accuracy.
作者
黄承锋
陈一铭
李元龙
HUANG Chengfeng;CHEN Yiming;LI Yuanlong(School of Economics and Management,Chongqing Jiaotong University,Chongqing 400074,China)
出处
《吉林大学学报（信息科学版）》
CAS
2022年第2期198-205,共8页
Journal of Jilin University（Information Science Edition）
基金
国家社会科学重点基金资助项目(16AGJ007)。
关键词
交通流量统计
高速公路交通
拥堵状况预测
改进遗传算法
支持向量机回归
反向传播神经网络
traffic flow statistics
highway traffic
congestion prediction
improved genetic algorithm
support vector machine regression
back propagation neural network
