Abstract

Intelligent traffic control and guidance system is an effective way to solve urban traffic congestion, improve road capacity and guarantee drivers' travel safety, while short-term traffic flow prediction is the core of intelligent traffic control and guidance system. To investigate the long-term memory and the dynamic feature of short-time traffic flow time series, a hybrid model was proposed by integrating autoregressive fractionally integrated moving average (ARFIMA) model and nonlinear autoregressive (NAR) neural network model to predict short-time traffic flow, in which ARFIMA model can address the long-term memory of linear component and NAR neural network can accommodate the dynamic feature of nonlinear residual component. First, the ARFIMA model was employed to predict the linear component of traffic flow, and the results were compared with those of autoregressive integrated moving average (ARIMA) model. Next, the NAR neural network model was adopted to forecast the nonlinear residual components, and the weighted results were considered as the predicted flow of the hybrid model. The proposed hybrid model was validated by using the cross-sectional traffic flow data in California freeways obtained from the open-access PeMS database. The results showed that the ARFIMA model considering the long-term memory can effectively predict the short-term traffic flow, and the prediction accuracy of the hybrid model is better than that of the singular models. The findings provide an alternative for the short-term traffic flow prediction with lower error and higher accuracy.

摘要

智能交通控制与引导系统是解决城市交通拥堵、提高道路通行能力、保障驾驶员出行安全的有效途径，而短期交通流量预测是智能交通控制与引导系统的核心。为了研究短时交通流时间序列的长期记忆和动态特征，提出了一种融合自回归分数阶积分移动平均（ARFIMA）模型和非线性自回归（NAR）神经网络模型的混合模型来预测短时交通流，其中ARFIMA模型可以解决线性分量的长期记忆，NAR神经网络可以容纳非线性残差分量的动态特征。首先，采用 ARFIMA 模型预测交通流量的线性分量，并将结果与​​自回归积分移动平均（ARIMA）模型的结果进行比较。接下来，采用NAR神经网络模型对非线性残差分量进行预测，加权后的结果作为混合模型的预测流量。通过使用从开放访问 PeMS 数据库获得的加州高速公路横截面交通流量数据验证了所提出的混合模型。结果表明，考虑长期记忆的ARFIMA模型能够有效预测短期交通流，混合模型的预测精度优于单一模型。研究结果为短期交通流量预测提供了一种误差更低、准确性更高的替代方案。采用NAR神经网络模型对非线性残差分量进行预测，加权后的结果作为混合模型的预测流量。通过使用从开放访问 PeMS 数据库获得的加州高速公路横截面交通流量数据验证了所提出的混合模型。结果表明，考虑长期记忆的ARFIMA模型能够有效预测短期交通流，混合模型的预测精度优于单一模型。研究结果为短期交通流量预测提供了一种误差更低、准确性更高的替代方案。采用NAR神经网络模型对非线性残差分量进行预测，加权后的结果作为混合模型的预测流量。通过使用从开放访问 PeMS 数据库获得的加州高速公路横截面交通流量数据验证了所提出的混合模型。结果表明，考虑长期记忆的ARFIMA模型能够有效预测短期交通流，混合模型的预测精度优于单一模型。研究结果为短期交通流量预测提供了一种误差更低、准确性更高的替代方案。通过使用从开放访问 PeMS 数据库获得的加州高速公路横截面交通流量数据验证了所提出的混合模型。结果表明，考虑长期记忆的ARFIMA模型能够有效预测短期交通流，混合模型的预测精度优于单一模型。研究结果为短期交通流量预测提供了一种误差更低、准确性更高的替代方案。通过使用从开放访问 PeMS 数据库获得的加州高速公路横截面交通流量数据验证了所提出的混合模型。结果表明，考虑长期记忆的ARFIMA模型能够有效预测短期交通流，混合模型的预测精度优于单一模型。研究结果为短期交通流量预测提供了一种误差更低、准确性更高的替代方案。