# Lane-Level Traffic Flow Prediction with Heterogeneous Data and Dynamic Graphs
## 1 Introduction  
As a significant part of smart city construction, an intelligent transportation system (ITS) acts as a powerful tool to enhance the quality of city transportation. Benefiting from the rapid development of the Internet of Things [1], the ITS can provide key information to aid in managing traffic through the connection between traffic sensors and the Internet. For instance, traffic police can inspect illegal parking and allocate traffic resources more efficiently with the deployment of automatic license plate recognition (LPR) infrastructure [2] or identify potentially illegal or dangerous driving behaviors by assessing driving style [3]. Moreover, with the vigorous development of mobile applications and intelligent vehicle terminals, every driver can easily obtain information about traffic congestion location predictions [4] and recommended driving routes. In addition, the traditional service industry relying on the road network has also been benefited in a variety of ways, such as the birth of crowdsourced logistics in the express industry, which could coordinate taxi passengerand express delivery services [5], and the location planning of business districts in the urban layout [6]. Therefore, ITS benefits not only traffic managers but also all participants including drivers, pedestrians, and so on. For the purpose of strengthening the foundation of ITSs, more researchers have funneled study into the field of traffic flow prediction. Based on different methods, traffic flow prediction analyzes and generalizes the traffic characteristics of both common and special areas (e.g., schools and hospitals) in both general and special periods (e.g., during congestion or after accidents) and generates accurate predictions of future traffic states [7]. Current traffic prediction methods fall into two types: model-driven methods and data-driven methods [8; 9; 10]. However, the former methods have the disadvantage of being unable to handle cases affected by special factors or events such as weather conditions and traffic accidents. Thus, in order to take special situations into account, current studies mainly adopt data-driven deep learning methods, which are capable of analyzing multi-dimensional data. [PERSON] et al. [11] modeled time series with a recurrent neural network (RNN). However, the RNN has the defects of gradient disappearance and long-term dependence, and therefore [PERSON] et al. [12] used a long short-term memory (LSTM) method to conduct a predictive analysis of the data collected from microwave sensors. By making use of a gate recurrent unit (GRU), an effective variant of LSTM, [PERSON] et al. [13] improved the speed forecast effectiveness. The above three studies only focused on either the temporal dependence or spatial dependence; they did not consider both comprehensively. Although [PERSON] et al. [14] focused on temporal-spatial correlation, and the proposed spatial-temporal dynamic network (STDN) model combined the dynamic change in spatial correlation with the periodicity of time correlation, [PERSON]'s study had the same disadvantage as the previous research, i.e., lanes at an intersection were considered as a whole and their differences were not distinguished [15]. At different intersections and in different flow directions, owing to the high mobility of vehicles, lanes often show diverse traffic patterns [15; 16]. As shown in Figure 1, in the morning, represented by red arrows, the traffic flow directed from right to left has a larger scale, and the opposite is true in the evening peak. In view of the ever-increasing development of smart cities, traffic flow prediction technology should detect dynamic changes in lanes more quickly and efficiently, with multiple trajectory data. Accordingly, from a data-driven perspective, a technology of traffic prediction at the lane level emerges and becomes one of the most vital parts of an ITS.