摘要
城市道路速度预测有助于引导驾驶人选择较为畅通的路径,减少等待时间,提高出行效率.城市交通状况受到多种因素影响,考虑多种交通流特征数据与天气数据,建立基于长短期记忆(long shortterm memory,LSTM)循环神经网络的道路行程速度预测组合模型.选取中国西安市南二环附近区域的滴滴出行浮动车数据,通过提取数据集的交通流特征(速度、流量、加速度和停车次数)和天气特征(温度、湿度、天气和风速)对道路行程速度进行预测.结果表明,与未加入外部特征的LSTM模型、误差逆传播(back propagation,BP)算法神经网络及支持向量回归(support vector regression,SVR)模型相比,融合多特征数据的组合模型平均绝对误差、均方误差和决定系数分别为2.695、13.838和0.771,置信区间为(-1.235,1.795),均优于其他模型,具有更高的精度和稳定性.
Urban road speed prediction is helpful to guide drivers to choose unimpeded routes,reduce waiting time and improve travel efficiency.Urban traffic conditions are affected by many factors.Based on the consideration of various traffic flow characteristic data and weather data,a combined model of road travel speed prediction based on long short-term memory(LSTM)cyclic neural network is established.The Didi floating car data in the area around the South Second Ring Road of Xi’an city are selected to predict the road travel speed by extracting the traffic flow characteristics(speed,flow,acceleration and stopping times)and weather characteristics(temperature,humidity,weather and wind speed)of the data set.The results show that compared with LSTM model,BP neural network model and SVR model without external features,the mean absolute error,mean square error and determination coefficient of the combined model with multi-feature data are 2.695,13.838 and 0.771,and the confidence interval of(-1.235,1.795)is better than other models.The combined model has higher accuracy and stability.
作者
霍嘉男
成卫
李冰
HUO Jianan;CHENG Wei;LI Bing(Faculty of Transportation Engineering,Kunming University of Science and Technology,Kunming 650504,Yunnan Province,P.R.China)
出处
《深圳大学学报（理工版）》
CAS
CSCD
北大核心
2023年第2期195-202,共8页
Journal of Shenzhen University(Science and Engineering)
基金
国家自然科学基金资助项目(52002161)
云南省基础研究计划资助项目(202101AU070026)
昆明理工大学人培基金资助项目(KKZ3202002039)。
关键词
智能交通
多特征数据
时间序列分析
速度预测
长短期记忆神经网络
深度学习
intelligent transportation
multi-feature data
time series analysis
speed prediction
long short-term memory(LSTM)neural network
deep learning
作者简介
霍嘉男(1996-),昆明理工大学硕士研究生.研究方向:交通信息工程及控制.E-mail:kmust-hjn@163.com;Corresponding author:Lecture LI Bing,E-mail:bing.li@kust.edu.cn。
