پیوندهای مفید
آمار
| تعداد نشریات | 21 |
| تعداد شمارهها | 671 |
| تعداد مقالات | 9,761 |
| تعداد مشاهده مقاله | 69,440,108 |
| تعداد دریافت فایل اصل مقاله | 48,598,850 |
Hierarchical federated learning model for traffic light management in future smart | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 14، دوره 14، شماره 12، اسفند 2023، صفحه 175-186 اصل مقاله (980.41 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2022.29398.4150 | ||
| نویسندگان | ||
| Alireza Soleimany1؛ Yousef Farhang* 2؛ Amin Babazadeh Sangar1 | ||
| 1Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran | ||
| 2Department of Computer Engineering, Khoy Branch, Islamic Azad University, Khoy, Iran | ||
| تاریخ دریافت: 01 مرداد 1401، تاریخ بازنگری: 19 آذر 1401، تاریخ پذیرش: 04 دی 1401 | ||
| چکیده | ||
| The present era is marked by rapid improvement and advances in technology. Nowadays inefficient traffic light management systems can make long delays and waste energy improving the efficiency of such complex systems to save energy and reduce air pollution in future smart cities. In this paper, we propose to take real-time traffic information from the surrounding environment. Such a process, which is called profilization constantly gathers and analyses information for vehicles and pedestrians throughout smart cities in order to fairly predict their actions and behaviours. We develop an efficient multi-level traffic light control system to schedule traffic signals’ duration based on a distributed profile database, which is generated by embedding sensors in streets, Vehicles and everywhere. We deploy pervasive deep learning models from the cloud to users (vehicles, bikes and pedestrians) to learn and control the traffic lights. In the cloud-level learning model, the maximum waiting time of different vehicles and pedestrians is calculated based on their profiles. The profilization process is a constant learning process throughout the whole city at the user level. Each vehicle deploys a separate learning model (decision-making) based on its average and maximum speed in a different area, waiting times at the intersections and possible trips and destinations. Such a multi-level deep learning model in the level of intersection and cloud aims to locally schedule the traffic with deadlines toward their destinations within a certain period. The results show that the proposed multi-level traffic light system can significantly improve the efficiency of the traffic system in future smart cities. | ||
| کلیدواژهها | ||
| Traffic light system؛ smart cities؛ deep learning | ||
| مراجع | ||
|
[1] M. Al-qutwani and X. Wang, Smart traffic lights over vehicular named data networking, Inf. 10 (2019)., no. 3. [2] K. Bonawitz, H. Eichner, W. Grieskamp, D. Huba, A. Ingerman, V. Ivanov, C. Kiddon, J. Konecny, S. Mazzocchi, H.B. McMahan and T. Van Overveldt, Towards federated learning at scale: System design, Proc. Machine Learn. Syst. 1 (2019), 374–388. [3] A. Boukerche and R.W. Coutinho, Crowd management The over-looked component of smart transportation systems, IEEE Commun. Mag. 57 (2019), no. 4, 48–53. [4] N. Casas, Deep deterministic policy gradient for urban traffic light control, arXiv preprint arXiv:1703.09035, (2017). [5] S. Chiu, Adaptive traffic signal control using fuzzy logic, Proc. Intel. Vehicles 92 Symp., IEEE, 1992, pp. 98–107. [6] B. De Schutter and B. De Moor, Optimal traffic light control for a single intersection, Eur. J. Control 4 (1998), no. 3, 260–276. [7] S. El-Tantawy, B. Abdulhai and H. Abdelgawad, Design of reinforcement learning parameters for seamless application of adaptive traffic signal control, J. Intel. Transport. Syst. 18 (2014), no. 3, 227–245. [8] W. Genders and S. Razavi, Using a deep reinforcement learning agent for traffic signal control, arXiv preprint arXiv:1611.01142, (2016). [9] H. Hosseinzadeh, Automated skin lesion division utilizing Gabor filters based on shark smell optimizing method, Evol. Syst. 11 (2020), no. 4, 589–598. [10] H. Hosseinzadeh and M. Sedaghat, Brain image clustering by wavelet energy and CBSSO optimization algorithm, J. Mind Med. Sci. 6 (2019), no. 1, 110–120. [11] M.B. Jensen, M.P. Philipsen, A. Mogelmose, T.B. Moeslund and M.M. Trivedi, Vision for looking at traffic lights: Issues, survey, and perspectives, IEEE Trans. Intel. Transport. Syst. 17 (2016), no. 7, 1800–1815. [12] J. Konecny, H.B. McMahan, F.X. Yu, P. Richtarik, A.T. Suresh and D. Bacon, Federated learning: Strategies for improving communication efficiency, arXiv preprint arXiv:1610.05492, 2016. [13] X. Liang, X. Du, G. Wang and Z. Han, A deep reinforcement learning network for traffic light cycle control, IEEE Trans. Vehicular Technol. 68 (2019), no. 2, 1243–1253. [14] X. Liang, X. Du, G. Wang and Z. Han, Deep reinforcement learning for traffic light control in vehicular networks, arXiv preprint arXiv:1803.11115, (2018). [15] S. Lima and L. Teran, Cognitive smart cities and deep learning: A classification framework, Sixth Int. Conf. eDemocracy eGovernment (ICEDEG), IEEE, 2019, pp. 180–187. [16] J. Lva, W. Tangb and H. Hosseinzadeh, Developed multiple-layer perceptron neural network based on developed search and rescue optimizer to predict iron ore price volatility: A case study, ISA Trans. 130 (2022), 420–432. [17] M. Mohammadi and A. Al-Fuqaha, Enabling cognitive smart cities using big data and machine learning: Approaches and challenges, IEEE Commun. Mag. 56 (2018), no. 2, 94–101. [18] M. Mohammadi, A. Al-Fuqaha, M. Guizani and J.-S. Oh, Semisupervised deep reinforcement learning in support of IoT and smart city services, IEEE Internet Things J. 5 (2017), no. 2, 624–635. [19] S.S. Mousavi, M. Schukat and E. Howley, Traffic light control using deep policy-gradient and value-function-based reinforcement learning, IET Intel. Transport Syst. 11 (2017), no. 7, 417–423. [20] F. Mutz, L.P. Veronese, T. Oliveira-Santos, E. De Aguiar, F.A.A. Cheein and A.F. De Souza, Large-scale mapping in complex field scenarios using an autonomous car, Expert Syst. Appl. 46 (2016), 439–462. [21] S. Parekh, N. Dhami, S. Patel and J. Undavia, Traffic signal automation through IoT by sensing and detecting traffic intensity through IR sensors, Inf. Commun. Technol. Intel. Syst. Volume 1. Springer Singapore, 2019, pp. 53–65. [22] A. Sharif, J. Li, M. Khalil, R. Kumar, M.I. Sharif and A. Sharif, Internet of things-smart traffic management system for smart cities using big data analytics, 14th Int. Comput. Conf. Wavelet Active Media Technol. Inf. process., IEEE, 2017, pp. 281–284. [23] B. Tang, Z. Chen, G. Hefferman, S. Pei, T. Wei, H. He and Q. Yang, Incorporating intelligence in fog computing for big data analysis in smart cities, IEEE Trans. Ind. Inf. 13 (2017), no. 5, 2140–2150. | ||
|
آمار تعداد مشاهده مقاله: 44,020 تعداد دریافت فایل اصل مقاله: 37,733 |
||