Traffic State Estimation of Bus Line With Sparse Sampled Data

Abstract

The traffic state of the bus line is the information basis for the bus company to make bus dispatch 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 traffic state accurately. To deal with this problem, a new traffic state estimation method for the bus line, named GAN_BS, is proposed. First, an improved generative adversarial network (GAN-I) is used to generate reasonable 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 performed 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 compared with other methods.

Keywords

• Traffic state estimation
• generative adversarial network
• bus line
• bilateral smoothing