# Urban Traffic State Sensing and Analysis Based on ETC Data: A Survey
## 7 Conclusions and Discussion
### Research Prospects  
Looking toward the future, ETC data-based urban traffic state sensing and analysis technologies will embrace broader development prospects. The arrival of the ETC 2.0 era marks a comprehensive transformation of this technology from simple toll collection tools to comprehensive traffic management platforms, possessing enhanced sensing capabilities, computing capabilities, and service capabilities.  
Against the backdrop of smart city construction, ETC will become crucial infrastructure for urban transportation digital transformation, deeply integrating with other urban management systems to collectively construct urban transportation systems. The deep fusion of emerging technologies, including vehicle-road networking, edge computing, and digital twins with ETC systems, will further promote the intelligent and refined development of urban traffic management.  
Under the guidance of sustainable transportation development strategies, ETC-based refined traffic management facilitates improved traffic efficiency and reduced congestion and emissions, representing an important technical pathway for achieving sustainable transportation development. With the advancement of carbon peak and carbon neutrality goals, the application potential of ETC technology in green transportation and low-carbon travel will be further explored and released.  
ETC data-based urban traffic state sensing and analysis technologies will achieve greater progress in theoretical innovation, technological breakthroughs, and application expansion, playing increasingly important roles in constructing safe, efficient, green, and intelligent modern urban transportation systems.  
Y.W.: Conceptualization, Methodology, Writing--Original Draft, Investigation, Writing--Review and Editing. R.L.: Conceptualization, Methodology, Supervision, Writing--Original Draft, Writing--Review and Editing. X.Y.: Conceptualization, Methodology, Supervision, Writing--Review and Editing. All authors have read and agreed to the published version of the manuscript.  
This study is supported by the China Postdoctoral Science Foundation, Cooperative Optimization on Right-of-Way at Signalized Intersections in Heterogeneous traffic Environment (2022M712410). National Natural Science Foundation of China (General Program), Research on Basic Problem of Vehicle-Infrastructure Cooperative Traffic Control for Special Vehicles (52472350). Guangxi Major Science and Technology Special Subproject, Reutilization of Pinglu Canal Cross-Line Bridges and Optimization of Traffic Organization (2023 AA14006).  
Not applicable.  
All authors are grateful for the resources provided by Intelligent Transportation System Research Center of Tongji University.  
The authors declare that there is no conflict of interest regarding the publication of this paper.  
The following abbreviations are used in this manuscript:  
AADT Annual Average Daily Traffic  
AI Artificial Intelligence  
ARM Advanced RISC Machine  
AVI Automatic Vehicle Identification  
DBI Davies-Bouldin Index  
DP-SH Dynamic Programming with Shooting Heuristic  
DSRC Dedicated Short-Range Communication  
ERP Electronic Road Pricing  
ETC Electronic Toll Collection  
FCD Floating Car Data  
GNSS Global Navigation Satellite System  
GPS Global Positioning System  
GRU Gated Recurrent Unit  
IoT Internet of Things  
ISTM Improved Support Tensor Machine  
ITS Intelligent Transportation System  
LiDAR Light Detection and Ranging  
LightGBM Light Gradient Boosting Machine  
LSTM Long Short-Term Memory  
MaaS Mobility-as-a-Service  
MEC Mobile Edge Computing  
MTC Manual Toll Collection  
Multi-SBRL Multiple Slope-Based Linear Regression  
OBU On-Board Unit  
OD Origin-Destination