## 5 Conclusions and Discussion  
We propose a framework for full-sample vehicle trajectory reconstruction based on roadside sensing data. The framework includes multiple tasks, deep learning-based video data processing, multi-target tracking, vehicle re-identification, and missing trajectory completion. YOLOv5 and DeepSORT algorithms are applied to extract vehicle trajectories. A vehicle re-identification algorithm is then designed to splice the vehicle trajectories in different video data, where lane and visual features are fused to reduce the adverse effect on the accuracy caused by the simultaneous appearance of similar vehicles on the expressway. The LSTM model based on graph attention is then developed to complete the missing vehicle trajectory. Comprehensive experiments are conducted using roadside sensing data from the Donghai Bridge, Shanghai, China. The recognition accuracy mAP of our vehicle re-identification algorithm with lane features reaches 73.46%, 1.5 percentage points higher than the original visual-based vehicle re-identification algorithm. Meanwhile, the experimental results show that the bidirectional LSTM vehicle trajectory reconstruction model based on graph attention outperforms other models that do not consider interactions between vehicles. The experimental results show that our framework can effectively reconstruct continuous vehicle trajectories via roadside sensing data.  
Future studies could follow several directions. Firstly, we could continue to improve the multi-camera cross-region tracking algorithm, use more advanced neural networks, or introduce traffic operation rules to improve the accuracy of vehicle re-identification. Secondly, the model of vehicle trajectory completion could be improved. We hope that the vehicle trajectory predicted by the model will follow the space-time law of vehicle operation and that there will be no wrongly predicted trajectory collisions caused by algorithm errors. Thirdly, since the quality of video data is strongly affected by natural factors, such as weather and light, more types of sensor data could be fused to improve the accuracy of the vehicle trajectories. Finally, when the framework is used in different traffic scenarios, by collecting new data sets and transfer learning, we can make the framework suitable for the traffic scenarios, such as urban areas and rural areas.  
Figure 9: Evaluation of the vehicle trajectory completion effect of the models on the Donghai Bridge dataset: (**a**) evaluation of vehicle trajectory completion with different numbers of missing frames; (**b**) evaluation of vehicle trajectory completion with different numbers of missing vehicles.  
**Author Contributions:** Conceptualization, C.Z.; methodology, G.S., Z.Z. and C.Z.; software, Z.Z. and A.S.; validation, C.Z. and X.L.; formal analysis, C.Z.; investigation, Z.Z.; data curation, C.Z., F.S. and L.Y.; writing--original draft preparation, G.S. and Z.Z.; writing--review and editing, C.Z., A.S. and X.L.; visualization, Z.Z.; funding acquisition, C.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China under Grant 2022 YEF0604900, and in part by the National Natural Science Foundation of China under Grant 52102383, and in part by the China Postdoctoral Science Foundation under Grant 2021M692428.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** All authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.