Design of Traffic Electronic Information Signal Acquisition System Based on Internet of Things Technology and Artificial Intelligence.

Hongling Wang

doi:10.9781/ijimai.2024.08.002

Authors

Hongling Wang Harbin University

DOI:

https://doi.org/10.9781/ijimai.2024.08.002

Keywords:

Artificial Intelligence, Internet of things, Signal Acquisition System, Traffic Electronic Information

Abstract

This study aims to devise a traffic electronic information signal acquisition system employing Internet of Things and artificial intelligence technologies, offering a novel approach to address prevailing challenges related to traffic congestion and safety. Initially, the hardware circuit for the high-speed signal acquisition control core is developed, leveraging Field-Programmable Gate Array technology. This facilitates wireless monitoring of signal acquisition. Subsequently, a comprehensive time signal acquisition system is formulated, encompassing modules for communication, acquisition, storage, adaptive measurement, and signal analysis. The geomagnetic acquisition module within this system is utilized for collecting geomagnetic signals, which are then translated into switch signals indicating the presence or absence of vehicles. These signals are subsequently transmitted to the geomagnetic signal processor. Experimental results pertaining to the signal acquisition system reveal a notable peak storage speed of 200KB/s, considering the utilization of one million sampling points. Across a series of tests, the maximum relative error of the obtained results ranges from 2.2% to 2.7%, underscoring the consistency and reliability of the measurements. In comparison to existing testing devices, the system exhibits heightened accuracy in test results, rendering it more apt for traffic signal acquisition applications. In conclusion, this study accomplishes the collection and dissemination of diverse traffic information, furnishing robust support for traffic control and ensuring safe operations.

Downloads

Download data is not yet available.

References

B. Jan, H. Farman, M. Khan, M. Talha, & I. U. Din, “Designing a smart transportation system: An internet of things and big data approach,” IEEE Wireless Communications, vol. 26, no. 4, pp. 73-79, 2019.

F. Al-Turjman, J. P. Lemayian., “Intelligence, security, and vehicular sensor networks in internet of things (IoT)-enabled smart-cities: An overview,” Computers & Electrical Engineering, vol. 87, p. 106776, 2020.

M. Babar, F. Arif., “Real-time data processing scheme using big data analytics in internet of things based smart transportation environment,” Journal of Ambient Intelligence and Humanized Computing, vol. 10, no. 10, pp. 4167-4177, 2019.

Y. Zhang, H. Huang, L. X. Yang, Y. Xiang, & M. Li, “Serious challenges and potential solutions for the industrial Internet of Things with edge intelligence,” IEEE Network, vol. 33, no. 5, pp. 41-45, 2019.

D. Jiang, “The construction of smart city information system based on the Internet of Things and cloud computing,” Computer Communications, vol. 150, pp. 158-166, 2020.

A. Ahmad, S. Din, A. Paul, G. Jeon, M. Aloqaily, & M. Ahmad, “Realtime route planning and data dissemination for urban scenarios using the Internet of Things,” IEEE Wireless Communications, vol. 26, no. 6, pp. 50-55, 2019.

Y. Ding, M. Jin, S. Li, & D. Feng, “Smart logistics based on the internet of things technology: an overview,” International Journal of Logistics Research and Applications, vol. 24, no. 4, pp. 323-345, 2021.

J. Yang, J. Zhang, H. Wang, “Urban traffic control in software defined internet of things via a multi-agent deep reinforcement learning approach,” IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 6, pp. 3742-3754, 2020.

R. W. Liu, J. Nie, S. Garg, Z. Xiong, Y. Zhang, & M. S. Hossain, “Datadriven trajectory quality improvement for promoting intelligent vessel traffic services in 6G-enabled maritime IoT systems,” IEEE Internet of Things Journal, vol. 8, no. 7, pp. 5374-5385, 2020.

H. Tran-Dang, N. Krommenacker, P. Charpentier, & D. S. Kim, “The Internet of Things for logistics: Perspectives, application review, and challenges,” IETE Technical Review, vol. 39, no. 1, pp. 93-121, 2022.

D. Tabernik, D. Skočaj, “Deep learning for large-scale traffic-sign detection and recognition,” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 4, pp. 1427-1440, 2019.

D. Temel, M. H. Chen, G. AlRegib, “Traffic sign detection under challenging conditions: A deeper look into performance variations and spectral characteristics,” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 9, pp. 3663-3673, 2019.

F. Zhu, Y. Lv, Y. Chen, X. Wang, G. Xiong, & F. Y. Wang, “Parallel transportation systems: Toward IoT-enabled smart urban traffic control and management,” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 10, pp. 4063-4071, 2019.

M. Akhtar, S. Moridpour, “A review of traffic congestion prediction using artificial intelligence,” Journal of Advanced Transportation, vol. 2021, pp. 1-18, 2021.

R. Abduljabbar, H. Dia, S. Liyanage, & S. A. Bagloee, “Applications of artificial intelligence in transport: An overview,” Sustainability, vol. 11, no. 1, p. 189, 2019.

J. Wu, X. Wang, Y. Dang, & Z. Lv, “Digital twins and artificial intelligence in transportation infrastructure: Classification, application, and future research directions,” Computers and Electrical Engineering, vol. 101, p. 107983, 2022.

Z. Wang, Y. Ma, “Detection and recognition of stationary vehicles and seat belts in intelligent Internet of Things traffic management system,” Neural Computing and Applications, pp. 1-10, 2022.

D. Ushakov, E. Dudukalov, E. Kozlova, & K. Shatila, “The Internet of Things impact on smart public transportation,” Transportation Research Procedia, vol. 63, pp. 2392-2400, 2022.

S. Muthuramalingam, A. Bharathi, S. Rakesh Kumar, N. Gayathri, R. Sathiyaraj, & B. Balamurugan, “IoT based intelligent transportation system (IoT-ITS) for global perspective: A case study,” Internet of Things and Big Data Analytics for Smart Generation, 2019, pp. 279-300.

A. Avramović, D. Sluga, D. Tabernik, D. Skočaj, V. Stojnić, & N. Ilc, “Neural-network-based traffic sign detection and recognition in highdefinition images using region focusing and parallelization,” IEEE Access, vol. 8, pp. 189855-189868, 2020.

M. Koresh, J. Deva. “Computer vision based traffic sign sensing for smart transport,” Journal of Innovative Image Processing (JIIP), vol. 26, no. 4, pp. 11-19, 2019.

D. De Silva, S. Sierla, D. Alahakoon, E. Osipov, X. Yu, & V. Vyatkin, “Toward intelligent industrial informatics: A review of current developments and future directions of artificial intelligence in industrial applications,” IEEE Industrial Electronics Magazine, vol. 14, no. 2, pp. 57-72, 2020.

S. Xu, Y. Qian, R. Q. Hu, “Data-driven edge intelligence for robust network anomaly detection,” IEEE Transactions on Network Science and Engineering, vol. 7, no. 3, pp. 1481-1492, 2019.

S. D. Mou, Z. X. Xiong, Y. Y. Tian, “Intelligent traffic control system based on cloud computing and big data mining,” IEEE Transactions on Industrial Informatics, vol. 15, no. 12, pp. 6583-6592, 2019.

H. Guan, W. Yan, Y. Yu, L. Zhong, & D. Li, “Robust traffic-sign detection and classification using mobile LiDAR data with digital images,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11, no. 5, pp. 1715-1724, 2018.

Y. Tian, J. Gelernter, X. Wang, J. Li, & Y. Yu, “Traffic sign detection using a multi-scale recurrent attention network,” IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 12, pp. 4466-4475, 2019.

W. Li, Z. Su, R. Li, K. Zhang, & Y. Wang, “Blockchain-based data security for artificial intelligence applications in 6G networks,” IEEE Network, vol. 34, no. 6, pp. 31-37, 2020.

Z. Liang, J. Shao, D. Zhang, & L. Gao, “Traffic sign detection and recognition based on pyramidal convolutional networks,” Neural Computing and Applications, vol. 32, no. 11, pp. 6533-6543, 2020.

A. Boukerche, Y. Tao, P. Sun., “Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems,” Computer Networks, vol. 182, p. 107484, 2020.

X. Tao, Y. Peng, F. Zhao, P. Zhao, & Y. Wang, “A parallel algorithm for network traffic anomaly detection based on Isolation Forest,” International Journal of Distributed Sensor Networks, vol. 14, no. 11, p. 1550147718814471, 2018.

Y. Ma, Z. Wang, H. Yang, & L. Yang, “Artificial intelligence applications in the development of autonomous vehicles: a survey,” IEEE/CAA Journal of Automatica Sinica, vol. 7, no. 2, pp. 315-329, 2020.

R. A. Gonzalez, R. E. Ferro, D. Liberona, “Government and governance in intelligent cities, smart transportation study case in Bogotá Colombia,” Ain Shams Engineering Journal, vol. 11, no. 1, pp. 25-34, 2020.

W. Shi, M. Zhang, R. Zhang, S. Chen, & Z. Zhan, “Change detection based on artificial intelligence: State-of-the-art and challenges,” Remote Sensing, vol. 12, no. 10, p. 1688, 2020.

J. Yang, C. Wang, B. Jiang, & Q. Meng, “Visual perception enabled industry intelligence: state of the art, challenges and prospects,” IEEE Transactions on Industrial Informatics, vol. 17, no. 3, pp. 2204-2219, 2020.

Z. Tong, F. Ye, M. Yan, H. Liu, & S. Basodi, “A survey on algorithms for intelligent computing and smart city applications,” Big Data Mining and Analytics, vol. 4, no. 3, pp. 155-172, 2021.

D. Impedovo, G. Pirlo. “Artificial intelligence applications to smart city and smart enterprise,” Applied Sciences, vol. 10, no. 8, p. 2944, 2020.

Y. Hou, Q. Li, C. Zhang, G. Lu, Z. Ye, Y. Chen, L. B. Wang, & D. Cao, “The state-of-the-art review on applications of intrusive sensing, image processing techniques, and machine learning methods in pavement monitoring and analysis,” Engineering, vol. 7, no. 6, pp. 845-856, 2021.

S. Latif, Z. e Huma, S. S. Jamal, F. Ahmed, J. Ahmad, A. Zahid, K. Dashtipour, M. U. Aftab, M. Ahmad, & Q. H. Abbasi, “Intrusion detection framework for the internet of things using a dense random neural network,” IEEE Transactions on Industrial Informatics, vol. 18, no. 9, pp. 6435-6444, 2021.

S. Mishra, D. Bhattacharya, A. Gupta, “Congestion adaptive traffic light control and notification architecture using google maps APIs,” Data, vol. 3, no. 4, p. 67, 2018.

J. Jin, X. Ma, “A multi-objective agent-based control approach with application in intelligent traffic signal system,” IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 10, pp. 3900-3912, 2019.

G. Chen, J. Zhang, “Applying Artificial Intelligence and Deep Belief Network to predict traffic congestion evacuation performance in smart cities,” Applied Soft Computing, vol. 121, p. 108692, 2022.

E. Ranyal, A. Sadhu, K. Jain, “Road condition monitoring using smart sensing and artificial intelligence: A review,” Sensors, vol. 22, no. 8, p. 3044, 2022.