Cong Wu

As an essential parameter to represent vehicle following characteristics, distance headway (DHW) plays an essential role in microtraffic flow simulation, traffic control, and traffic safety alarm. However, due to the randomness, nonlinearity, and correlation of DHW data, constructing DHW prediction models is difficult. Moreover, few studies have considered the time correlation between the historical DHW and the target DHW. To solve the above problems, a DHW prediction model is proposed in this paper by integrating entropy-based grey relation analysis (EB-GRA) and temporal convolutional network (TCN), named as EB-GRA-TCN model. In the model, the EB-GRA is adopted to calculate the correlation between the target DHW and historical DHW sequences, and the DHW data with high correlation are dynamically selected as the optimal input of the DHW prediction model. Then, the TCN algorithm is used to train the DHW prediction model. The TCN architecture integrates the advantages of recurrent neural network (RNN) and convolutional neural network (CNN), which could fully use the previous DHW information. In the experiment, the DHW data from Hefei Expressway are utilized for training the EB-GRA-TCN model. The prediction results showed that the average root mean square error (RMSE) and mean absolute error (MAE) of the proposed model were 0.115 and 0.090, respectively, in the 5, 10, and 15 predicted steps. Compared with the autoregressive integrated moving average (ARIMA), TCN, RNN, and long short-term memory (LSTM) models, the EB-GRA-TCN model achieved the best prediction accuracy. The results indicated that the EB-GRA-TCN model obtained good predictive performance and could provide support for road traffic control and traffic safety warming.

The traffic state of the bus line is the information basis for the bus company to make bus dis-patch and travel time prediction. However, the bus GPS data is severely sparse in time and space coverage of traffic state, due to the long data sampling time interval and low bus departure frequency. Because of ignoring the severe sparseness of the bus data, the existing traffic state methods cannot reconstruct the traf-fic state accurately. To deal with this problem, a new traffic state estimation method for the bus line, named GANBS, is proposed. First, an improved generative adversarial network (GAN-I) is used to generate rea-sonable bus data. GAN-I aims to find the probability space of the data distribution under sparse sampling. And to reduce the size of the latent space of data, the traffic knowledge is introduced as prior information layers. Then, a traffic adaptive bilateral smoothing method (BS) is used to map discrete bus data into the continuous traffic state. The BS convolves data with a bilateral kernel, which multiplies the local action kernel with a mask of traffic state similarity. Therefore, the BS can maintain transitions between different traffic patterns while separating noise from traffic state. Finally, a set of numerical experiments are per-formed on the real bus data set in Changchun. The results show that the GAN-I can accurately reproduce the traffic state when the missing rate of data exceeds 50%. And the BS can eliminate the noise better com-pared with other methods.

Lane-Changing Decision (LCD) behavior is complex, dangerous, and varied by the Driver’s Psychology (DP) and Driving Style (DS). For lacking the consideration of DP and DS, the existing LCD models cannot predict the LCD process varying with the drivers and traffic conditions accurately. To deal with the problems, a new LCD model coupling DP and DS, named DP&DS-LCD, is put forward. In the model, a psychological field model is constructed to represent the DP effect on the scene vehicles. And then, K-means and K-Nearest Neighbor (KNN) algorithm are respectively adopted in learning and recognition phases to recognize the current driving style pattern. Finally, based on the DP and DS, the multi-Grained Cascade Forest (gcForest) algorithm is applied to predict the LCD behavior. In experiments, DP&DS-LCD is compared with other three LCD models by using the opening I-80 database from Next Generation Simulation project (NGSIM). And the results showed that the DP&DS-LCD model achieved the best performance. Therefore, the DP&DS-LCD model is effective and could provide support for the decision of autonomous vehicles by predicting the surrounding vehicles’ Lane-Changing (LC) behavior.