# Estimation of Lane-Level Traffic Flow Using a Deep Learning Technique
## 6 Conclusions and Future Work  
In recent years, deep learning techniques for optimizing object detection and tracking have developed rapidly and have been applied to human and vehicle traffic flows [17; 18]. To our knowledge, this study was the first to introduce virtual lines and hot zones to a deep learning object detection and tracking system for lane-specific vehicle counting and velocity estimation.  
Our contributions to traffic flow estimation are detailed in the third to fifth paragraph of the Discussion Section. Briefly, the contributions are as follows: the system proposed in this paper can provide detailed real-time information including the number of vehicles passing in each lane of the section of freeway, the vehicle types, and the velocities of these vehicles. Such information can be provided to the authorities for traffic monitoring and control, shared with individual vehicles (including self-driving cars) for navigation, and analyzed by official or academic institutions for traffic modeling and prediction. Since the system proposed in this paper can be simply implemented using video cameras set on the freeway, its efficiency, low cost, and high data quality are superior to ETC-based traffic monitoring systems for traffic counting and analysis.  
In the future, we will continue to improve the prediction accuracy, especially that of lane-specific counting. We will also study techniques concerning the macro-view of traffic flow, and we hope to develop an attention model that can automatically describe the complete traffic situation on a larger scale.  
The wireless sensor networks (WSNs) are widely applied, and security has become a major concern for WSN protocol designers [22]. All detection schemes are subject to cyber-security attacks. Main cyber vulnerabilities include internet exposition, interface/password breaches, update lack, low segregation, weakness of Cyber Physical System (CPS) protocols, weakness of CPS applications, and leak of sensitization [23]. Since our system is related to traffic monitoring and control, it might face some potential cybersecurity attacks. The attacks might occur during data transmission and database access, including interruption interception and modification. The data used in this paper were sourced from an authorized organization (MOTC, Taiwan), which we do not believe to be vulnerable. In the future, this issue will be further investigated.  
Conceptualization, J.-C.J. and C.-M.L.; methodology, J.-C.J. and C.-M.L.; software, C.-M.L.; data curation, C.-M.L.; writing--original draft preparation, C.-M.L.; writing--review and editing, J.-C.J.; supervision, J.-C.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Ministry of Science and Technology (MOST), Taiwan, under grant MOST 109-2218-E-006-032.  
Not Applicable.  
The data are available at [[https://drive.google.com/drive/folders/1](https://drive.google.com/drive/folders/1) Sf8 wmSIY5 CEodwnNNN2 Xzb6 wF_qZwcV2 Zusp=sharing]([https://drive.google.com/drive/folders/1](https://drive.google.com/drive/folders/1) Sf8 wmSIY5 CEodwnNNN2 Xzb6 wF_qZwcV2 Zusp=sharing).  
The authors declare no conflict of interest.