A Dynamic Spatio-Temporal Deep Learning Model for Lane-Level Traffic Prediction

Table 1

Summary of notations.

Symbol	Description
	Lane network
	Node set and edge set of in
	Traffic information of all lanes at timestamp
	Traffic information of lane at timestamp
	Data-driven adjacent matrix
	Distance-based adjacent matrix
	Traffic information similarity matrix
	Sequence length for input and predict
	A constant that controls the contribution of
	Degree matrix
	Update gate and reset gate in GRU
	Cell state and hidden state in GRU
	Learnable parameter matrices
	Feature fusion gate
	Learned spatial features
	Learned temporal features
	Fused spatio-temporal features