Personalized Situation Adaptive Human-Vehicles-Interaction (HVI) Prediction in COVID-19 Context

Abstract

The purposes are to investigate the personalized situation adaptive Human-Computer Interaction (HCI) in the COVID-19 context, achieve accurate predictions for HCI in different urban transportation situations, and solve the urban intelligent transportation problems. Problems of Human-Vehicles-Interaction (HVI) in context awareness are analyzed. Historical traffic flow in three different situations, including novice user situation, mid user situation, and expert user situation, are taken as the data sources. The HVI data are preprocessed afterward. Next, Dilated Convolution (DC) and Long-Short Term Memory (LSTM) are integrated (DC-LSTM) to build an HVI model based on situation adaptive. The proposed model is simulated to analyze its performance. Simulation experiments suggest that the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) of the proposed model are 4.64%, 5.34%, and 7.82%, respectively. Although these three metrics increase under the mid user and expert user situations, the proposed model can still provide a higher accuracy than LTSM, Convolutional Neural Network (CNN), Simple Recurrent Network (SRN), Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP). Besides, the prediction velocity can maintain about 60 Frame-Per-Second (FPS) under all three user situations. Regarding the path guidance performance, the proposed model can suppress the traffic congestion and dredge the congested sections effectively. Hence, the HVI model based on situational adaptation constructed has high prediction accuracy and traffic congestion evacuation performance, which can provide an experimental basis for the later intelligent transportation field and improving situational self-adaptability.