| description abstract | It has been recognized that significant travel time estimation errors may be introduced using low-resolution GPS-based floating car trajectory data traditionally. Very few studies have been conducted to concentrate on spatial–temporal relationship identification among travel time measurements. In this study, an attention-based spatial–temporal graph convolutional networks on low-resolution data (AGCN-LR) approach was proposed to estimate more accurate travel time in urban traffic roadway networks using low-resolution GPS-based data measured by floating cars. Specifically, three models were developed in this AGCN-LR approach. Hour, day, and week were used to model the dynamic relationship among spatial–temporal traffic flow attributes, respectively. The same structures were adopted for these three models. Two spatial-temporal block (ST-block) models and one temporal convolutional model were included. Furthermore, one spatial graph convolutional model and one temporal attention mechanism model were embedded in a ST-block. AGCN-LR not only improved the efficiency and accuracy of travel time estimation through the framework optimization training process in a spectrum convolution network but also combined the three temporal components. The final estimation value was formed afterward. Experimental tests were conducted using the real data set from low-resolution floating car data in Harbin, China, in 2017. Results indicated that AGCN-LR outperforms the other state-of-the-art algorithms by reducing estimation mean absolute error (MAE) by about 50 s when it captured the relationship among dynamic spatial and temporal data from the data set. The AGCN-LR approach demonstrated great potential to become one of the important urban network-wide traffic management tools using low-resolution floating car data. | |
