Short-term traffic flow prediction is of great significance for road construction and operation. Aiming at the possible problems of missing data or insufficient sample data, the short-term traffic flow prediction of parallel road sections is studied using the traffic flow data collected in Shandong Province and the transfer learning idea. Firstly, the spatiotemporal correlation of the traffic flow of the parallel road section is analyzed, and the correlation coefficient is calculated; Secondly, the long-term and short-term memory network model is used to predict the section flow of expressway; Finally, the model based transfer learning is used to predict the cross section flow of parallel roads. The prediction results show that the average absolute error, the mean square error, the root mean square error and the average absolute percentage error of the transfer learning based prediction algorithm decrease by 2.56%, 3.59%, 1.81% and 4.41% respectively compared with the direct prediction of the short-term memory network model. In addition, the prediction model based on transfer learning can significantly improve the prediction efficiency, and can provide feasible methods for traffic flow prediction in areas lacking equipment collection or data.
The short-term traffic flow prediction is of practical significance for the operation and management of the expressway. Combining the traffic flow data collected from the expressway network in Shandong Province, the short-term traffic flow prediction of expressway sections is studied to provide support for the establishment of vehicles travel path selection model. Firstly, the spatio-temporal correlation of traffic flow is analyzed and the correlation coefficients are calculated. Secondly, the summation autoregression and average long-term and short-term memory models based on time series, and regression prediction model based on spatial correlation are selected to predict the traffic flow at the section. Finally, the weighted least squares method is used for the spatio-temporal data integration prediction. The prediction results show that the prediction accuracy of the spatio-temporal traffic flow data fusion algorithm is higher than that of the single prediction model. The average absolute percentage error of the data fusion algorithm is reduced to 8.127%, and the average absolute error and root mean square error are lower than those of the single prediction model. The combined prediction model improves the prediction accuracy.
For the purpose of alleviating highway traffic congestion caused by truck flows, in this paper, based on the highway networking toll data with high coverage and accuracy, vehicle trajectories are constructed, traffic volume is tallied for congestion identification, and then a truck flow traceability analysis method is proposed. The results of the research show that there are different degrees of congestion in the seven freight corridors of the highway network in Shandong Province, and the main sources and destinations of truck flows on the congested sections are located in cities such as Jinan, Zibo and Weifang, as well as some regions outside Shandong Province, indicating that freight transport between major cities in Shandong Province and cross-provincial freight transport are the causes of highway congestion. The study also reveals the departure time distribution pattern of truck flows on congested sections, which can provide a reference for adjusting the highway truck passage time to reduce the impact of truck flow on highway traffic.
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