# Urban Traffic State Sensing and Analysis Based on ETC Data: A Survey
## 5 ETC-Based Urban Traffic Management: Applications and Practices
### Traffic Prediction and Artificial Intelligence Technology  
Artificial intelligence technology plays an increasingly important role in ETC data-based traffic prediction, becoming a key technology for enhancing the intelligence levels of urban traffic management. [PERSON] et al. proposed highway traffic parameter prediction models based on combined deep learning models, constructing two deep learning time-series combination prediction models. Using ETC data from the Shanlin Highway in Gansu Province for short-term prediction validation, the results demonstrated that both models exhibited ideal data training and prediction capabilities in testing [33]. In traffic state estimation, [PERSON] and [PERSON] proposed boundary-aware observer-deep learning traffic state estimation methods that integrate partial differential equation observers with deep learning paradigms, estimating spatiotemporal traffic states from boundary sensing data [34]. [PERSON] et al. developed an improved support tensor machine method for traffic state subcategory classification based on ETC gantry data (ISTM). Compared with K-means clustering and SVM, ISTM demonstrated optimal values for both SumD and DBI indicators [35]. Regarding comprehensive reviews of deep learning algorithms, [PERSON] et al. conducted comprehensive reviews of deep learning algorithms in traffic prediction, covering different scenarios across various traffic datasets and reviewing 111 pioneering research works since the 1980s, including both deep learning and classical models. The research provided detailed introductions to the data sources used in transportation systems and in-depth investigations of theoretical foundations for popular deep learning algorithms and classical models in traffic prediction [36]. In explainable artificial intelligence, [PERSON] et al. proposed explainable traffic flow prediction methods based on large language models (xTP-LLMs), capturing complex time-series patterns and external factors by converting multi-modal traffic data into natural language descriptions. Experiments demonstrated that the xTP-LLM provides intuitive and reliable explanations for predictions while maintaining accuracy comparable to deep learning baselines [37]. [PERSON] et al. provided comprehensive reviews of the recent advances and emerging opportunities for artificial intelligence in traffic prediction, focusing on AI-based traffic prediction methods with a particular emphasis on multivariate traffic time-series modeling. The research covered various data types and resources, classified fundamental data preprocessing methods in traffic prediction contexts, subsequently summarized prediction methods and applications, provided solutions for major research challenges in traffic prediction, and discussed future research directions [38]. [PERSON] and [PERSON] reviewed traffic flow prediction methods in intelligent transportation system construction, classifying methods into three categories based on statistics, machine learning, and deep learning, concluding that deep learning methods achieved optimal overall effectiveness [39]. [PERSON] conducted traffic volume prediction based on deep learning models for Annual Average Daily Traffic (AADT) correction, managing data discontinuity through LSTM technology [40]. In video data processing, [PERSON] et al. developed high-precision deep learning models for estimating traffic characteristics from video data. Through deep learning video processing algorithms, they detected, tracked, and predicted highly disaggregated vehicle-based data such as trajectories and speeds, converting these data into aggregated traffic characteristics, including speed variance, average speed, and flow. Using LiDAR sensor observations as the ground truth, the results demonstrated that this technology estimates lane traffic volume with 97% accuracy, while computer vision technology can estimate individual vehicle-based speed calculations with 90-95% accuracy [41].