## 6 Conclusions  
This paper introduces CeT, a predictive framework designed to forecast future traffic states at signalized intersections. The model employs a discrete spatial-temporal structure, reminiscent of the traditional cellular transmission model, which proves effective in utilizing traffic data from high-definition maps. This includes vehicle-state information, vehicle-type attributes, temporal attributes, and spatial attributes. CeT first transforms the vehicle-type attributes into word encodings and embeds them into vehicle-state information, enabling the model to learn the effects of different vehicle types on traffic predictions. Subsequently, CeT converts vehicle-state embeddings into the state of the cell space, encompassing entire lane segments. Next, CeT utilizes the previous cell space states to construct the input sequence and perform spatial-temporal embeddings to the sequence based on the previous temporal attributes and spatial attributes. Under the guidance of spatial-temporal embeddings, CeT learns the interdependencies and correlations within the input sequence, employing multi-head attention to forecast the number of vehicles in each lane segment across the forecasting horizon.  
We utilize the traffic data in pNEUMA to conduct two different signalized intersection scenarios to validate the effectiveness of CeT. Compared to the best predictive performance in baselines, the result indicates that CeT respectively improves MAE, RMSE, and ACC by 11.47%, 13.48%, and 4.36% in two-phase signalized intersections and 13.36%, 12.93%, and 4.78% in four-phase signalized intersections. Meanwhile, three independent ablation studies also confirm the advantages of CET over baseline models and the further contributions of vehicle-state embeddings, spatial-temporal embeddings, and spatial-temporal attention in enhancing predictive accuracy. Further, the studies on hyperparameters and model sensitivity also demonstrate the robustness of CeT.  
Future research primarily focuses on three main directions. Firstly, we aim to enhance the model architecture to more effectively capture complex dependencies and correlations, thereby improving predictive performance. Secondly, we plan to integrate CeT with the motion planner of AVs. By leveraging the results of traffic state evolution prediction, we will generate space-time corridors to facilitate reference trajectory planning for autonomous vehicles. Furthermore, we will apply CeT in urban infrastructure monitoring for real-time  
Figure 11: Analysis of learning rates and batch sizes on model convergence in Scenario 2.  
condition evaluation or energy consumption prediction to optimize efficiency due to its outstanding ability in discrete spatial-temporal data processing.  
Conceptualization, A.L. and Y.P.; Methodology, A.L.; Software, Z.X.; Validation, W.L.; Formal analysis, A.L.; Investigation, Z.X.; Data curation, W.L.; Writing--original draft, A.L.; Writing--review & editing, Y.P.; Supervision, Y.C.; Funding acquisition, Y.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key Science and Technology Projects in the Transportation Industry (2022-ZD6-116), and Technology Transfer Project of Beijing Municipal Engineering Design and Research Institute (40061000202417) and (40061000202503).  
Not applicable.  
The data presented in this study are openly available in pNEUMA at [[https://doi.org/10.1016/j.trc.2019.11.023](https://doi.org/10.1016/j.trc.2019.11.023)]([https://doi.org/10.1016/j.trc.2019.11.023](https://doi.org/10.1016/j.trc.2019.11.023)), reference number [8].  
The authors declare no conflicts of interest.