Short-term traffic flow prediction with PSR-XGBoostconsidering chaotic characteristics

To improve the level of active traffic management,a short-term traffic flow prediction model is proposed by combining phase space reconstruction(PSR)and extreme gradient boosting(XGBoost)algorithms.Firstly,the traditional data preprocessing method is improved.The new method uses hierarchical clustering to determine the traffic flow state and fills in missing and abnormal data according to different traffic flow states.Secondly,one-dimensional data are mapped into a multidimensional data matrix through PSR,and the time series complex network is used to verify the data reconstruction effect.Finally,the multidimensional data matrix is inputted into the XGBoost model to predict future traffic flow parameters.The experimental results show that the mean square error,average absolute error,and average absolute percentage error of the prediction results of the PSR-XGBoost model are 5.399%,1.632%,and 6.278%,respectively,and the required running time is 17.35 s.Compared with mathematical-statistical models and other machine learning models,the PSR-XGBoost model has clear advantages in multiple predictive indicators,proving its feasibility and superiority in short-term traffic flow prediction.

Incident Detection Method of Expressway Based on Traffic Flow Simulation Model

The expressway traffc incidents have the characteristics of high harmful, strong destructive and refractory.Incident detection can guarantee smooth operation of the expressway, reduce traffc congestion and avoid secondary accident by informing the accident, detection and treatment timely. In this paper, an incident detection method is proposed using the toll station data that takes into account the traffc ratio at the entrances and crossway in the network. The expressway traffc simulation model is improved and a simulation algorithm is established to describe the movement of the vehicles. A numerical example is experimented on the expressway network of Shandong province. The proposed method can effectively detect the expressway incidents, and dynamically estimate the traffc network states so as to provide advice for the highway management department.

Study on the Reduced Traffic Congestion Method Based on Dynamic Guidance Information

This paper studies how to generate the reasonable information of travelers' decision in real network. This problem is very complex because the travelers' decision is constrained by different human behavior. The network conditions can be predicted by using the advanced dynamic OD(Origin-Destination, OD) estimation techniques. Based on the improved mesoscopic traffic model, the predictable dynamic traffic guidance information can be obtained accurately.A consistency algorithm is designed to investigate the travelers' decision by simulating the dynamic response to guidance information. The simulation results show that the proposed method can provide the best guidance information. Further,a case study is conducted to verify the theoretical results and to draw managerial insights into the potential of dynamic guidance strategy in improving traffic performance.