论文标题
使用变异LSTM网络的短期交通流量预测
Short-Term Traffic Flow Prediction Using Variational LSTM Networks
论文作者
论文摘要
交通流量特征是该地区最关键的决策和交通警务因素之一。对交通流量的预测状态的认识在交通管理和交通信息部门中至关重要。这项研究的目的是通过使用基于智能运输系统领域的历史数据的深度学习技术提出一个预测流量模型。 2019年，从CalTrans性能测量系统（PEM）收集的历史数据。拟议的预测模型是一种短期长期记忆编码器，简短的VLSTM-E尝试与其他常规方法相比，试图准确地估算流量。 VLSTM-E可以通过考虑分布和缺失值来提供更可靠的短期流量流。
Traffic flow characteristics are one of the most critical decision-making and traffic policing factors in a region. Awareness of the predicted status of the traffic flow has prime importance in traffic management and traffic information divisions. The purpose of this research is to suggest a forecasting model for traffic flow by using deep learning techniques based on historical data in the Intelligent Transportation Systems area. The historical data collected from the Caltrans Performance Measurement Systems (PeMS) for six months in 2019. The proposed prediction model is a Variational Long Short-Term Memory Encoder in brief VLSTM-E try to estimate the flow accurately in contrast to other conventional methods. VLSTM-E can provide more reliable short-term traffic flow by considering the distribution and missing values.
