# Lane-Level Traffic Flow Prediction with Heterogeneous Data and Dynamic Graphs
## 1 Introduction
The lane-level traffic prediction method has attracted the attention of researchers mainly because it can be applied to a variety of complex dynamic prediction scenarios. Based on the improvement of the existing prediction method, it could play a huge role in the future in applications such as unmanned driving, lane-level positioning [17], and cloud video surveillance traffic detection [18]. In order to focus on the temporal and spatial characteristics of traffic flow, [PERSON] et al. [19] used an entropy-based gray correlation method to analyze the correlation between lanes and an LSTM-GRU model to derive the spatial-temporal information matrix which is iterated to output the speed prediction. [PERSON] et al. [20] proposed an innovative two-stream multi-channel convolutional neural network (TMCNN) model, based on the view that different lanes at an intersection can be equated with diverse channels in an image. By introducing a theory of lane-changing behavior, [PERSON] et al. [21] established a model for lane-changing  
Figure 1: Different traffic conditions of lanes in morning and evening peaks. and its implementation based on a deep belief network (DBN) and LSTM. In addition, [PERSON] et al. [22] and [PERSON] et al. [23] utilized a convolutional LSTM network (ConvITM) model to process temporal-spatial information. All the above authors carried out in-depth studies on the temporal and spatial characteristics of traffic flow, but excessive emphasis on these characteristics will inevitably lead to the neglect of other heterogeneous data; for example, some time-varying characteristics are not taken into account. Consequently, in our work, considering static and dynamic road network structures, we propose a heterogeneous graph convolution model that takes advantage of the road occupancy rate, minimum visibility, and other data to minimize the deviations in prediction results caused by a single data source. Inspired by some of the latest research on dynamic graphs [24, 25], we propose generating dynamic graphs in an internal graph convolution network to determine the correlation between lanes. Since the impact of weather conditions and road occupancy on flow prediction cannot be ignored, we fuse other heterogeneous data in an external data fusion network. In addition, in order to mine the time-dependence of traffic flow, we deploy a codec network with a temporal mechanism. In summary, regarding lane-level prediction, this study mainly demonstrates innovation and progress in the following three respects:  
* We propose a heterogeneous graph convolution model based on dynamic graph generation. In order to discover the spatial dependence of dynamic graphs, a graph convolutional network (GCN) is introduced into this model, and a temporal attention mechanism is applied to further study the time-dependence. * We analyze other heterogeneous data to obtain more potential information that has not been previously considered. That is, by incorporating multivariate data such as occupancy and minimum visibility into heterogeneous data fusion networks, we can reduce the loss of accuracy. * We conduct extensive experiments on two real-world datasets, and the results show that our proposed model can increase the precision of lane-level traffic state prediction.