Route Optimization Using Radial Interpolated Quantum Finite Automata for Intelligent Transportation
Keywords:
Artificial Intelligence, Internet of Transportation things, Intelligent Transportation Systems, Radial Basis, Interpolated Neural Network, Quantum Finite AutomataAbstract
Internet of Things (IoT) is novel pattern which is giving immeasurable services. Several applications integrate physical objects by internet as well as transmit data gathered over network without human interference. Artificial Intelligence (AI) is the potentiality of a machine to carry out analytical consequences like, assessing, inferring, learning and problem-solving that humans are competent of carrying out at ease. Nevertheless, there still remains scope to IoT in transportation (i.e., Internet of Transportation things) where AI benefits from Intelligent Transportation Systems (ITS). This is owing to the reason that with the immense growth in population has resulted in increase in demand for vehicles. This has inspired several researchers to come up with numerous features by designing pertinent applications to make transportation smart by optimization vehicle route, ensuring smooth and smart parking, therefore paving ways and mechanisms in reducing congestions and accidents. In this work to circumvent traffic jams as well as solve congestion problems, Radial Basis Interpolated Neural Network and Quantum Finite Automata (RBINN-QFA) route optimization technique is applied to identify optimal route. Sensors positioned on each of the junctions are used to collect data at different time. A Radial Basis Interpolated Neural Network (RBI-NN) algorithm is first applied to identify the arrival time of vehicles for data allocation. Next, Quantum Finite Automata (QFA) is applied for group routing so which traffic congestion is reduced. Grouping here are formed based on the number of vehicles and junction. The Quantum Finite Automata algorithm assigned routes to vehicles to evade congestion and therefore ensuring optimized vehicle routing. Simulation outcomes represent that RBINN-QFA method give high routing accuracy as well as minimum route detection time with improved sensitivity and specificity. The finding put forwards an abstract development for performing machine learning techniques in gathering ITS-oriented data as well as smart city optimal routing.
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References
Yang Cui, Dongfei Lei, “Optimizing Internet of Things-Based Intelligent Transportation System’s Information Acquisition Using Deep Learning”, IEEE Access, Feb 2023 [Faster Region-based Convolutional Neural Network (Faster R-CNN)]
Preeti Rani, Rohit Sharma, “Intelligent transportation system for internet of vehicles based vehicular networks for smart cities”, Computers and Electrical Engineering, Elsevier, Jan 2023 [Tree-based Machine and Ensemble Learning]
Bowen Du, Hao Peng, Senzhang Wang, Md Zakirul Alam Bhuiyan, Lihong Wang, Qiran Gong, Lin Liu, and Jing Li, “Deep Irregular Convolutional Residual LSTM for Urban Traffic Passenger Flows Predict”, IEEE Transactions on Intelligent Transportation Systems, Oct 2020
Keping Yu, Long Lin, Mamoun Alazab, Liang Tan, and Bo Gu, “Deep Learning-Based Traffic Safety Solution for a Mixture of Autonomous and Manual Vehicles in a 5G-Enabled Intelligent Transportation System”, IEEE Transactions on Intelligent Transportation Systems, May 2021
Ling Zhao, Yujiao Song, Chao Zhang, Yu Liu , Pu Wang , Member, IEEE, Tao Lin, Min Deng, and Haifeng Li, “T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction”, IEEE Transactions on Intelligent Transportation Systems, Oct 2020
Lakshmi Shankar Iyer, “AI enabled applications towards intelligent transportation”, Transportation Engineering, Elsevier, Jul 2021
Agachai Sumalee, HungWai Ho, “Smarter and more connected: Future intelligent transportation system”, IATSS Research, Elsevier, Jun 2018
Ritesh Dhanare, Kapil Kumar Nagwanshi, and Sunita Varma, “A Study to Enhance the Route Optimization Algorithm for the Internet of Vehicle”, Wireless Communications and Mobile Computing, Wiley, Apr 2022
AndréBroekman, Petrus Johannes Gräbe, Wynand J.vdM. Steyn, “Real-time traffic quantization using a mini edge artificial intelligence platform”, Transportation Engineering, Elsevier, Mar 2021
Qian Yu, Yuanguo Wang, Xiaogang Jiang , Bailu Zhao, Xiuling Zhang, Xiaobei Wang, and Qingqing Liu, “Optimization of Vehicle Transportation Route Based on IoT”, Mathematical Problems in Engineering, Hindawi, Sep 2021
Daisuke Inoue, Akihisa Okada, Tadayoshi Matsumori, Kazuyuki Aihara, & Hiroaki Yoshida, “Traffic signal optimization on a square lattice with quantum annealing”, Scientific Reports, Oct 2021
Zhengang Guo, Yingfeng Zhang, Jingxiang Lv, Yang Liu, and Ying Liu, “An Online Learning Collaborative Method for Traffic Forecasting and Routing Optimization”, IEEE Transactions on Intelligent Transportation Systems, May 2020
Yongsheng Cao, “Online Routing and Charging Schedule of Electric Vehicles With Uninterrupted Charging Rates”, IEEE Access, Sep 2022
Ennio Gambi, Linda Senigagliesi, Andrea Barbaresi, Matteo Mellini, Adelmo De Santis, “A WKNN-based approach for NB-IoT sensors localization”, Digital Communications and Networks, Elsevier, Apr 2022
Baofeng Ji, Mingkun Zhang, Ling Xing, Xiaoli Li, Chunguo Li, Congzheng Han, Hong Wen, “Research on optimal intelligent routing algorithm for IoV with machine learning and smart contract”, Digital Communications and Networks, Elsevier, Jun 2022
Ajay Kumar Dogra1, Jagdeep Kaur2, “Moving towards smart transportation with machine learning and Internet of Things (IoT): a review”, Journal of Smart Environments and Green Computing, Nov 2022
Ritesh Dhanare, Kapil Kumar Nagwanshi, and Sunita Varma, “A Study to Enhance the Route Optimization Algorithm for the Internet of Vehicle”, Wireless Communications and Mobile Computing, Wiley, Apr 2022
Ritesh Dhanare, Kapil Kumar Nagwanshi, and Sunita Varma, “A Study to Enhance the Route Optimization Algorithm for the Internet of Vehicle”, Wireless Communications and Mobile Computing, Wiley, Apr 2022
Mohammadhossein Ghahramani, MengChu Zhou, Anna Molter, and Francesco Pilla, “IoT-Based Route Recommendation for an Intelligent Waste Management System”, IEEE Internet of Things Journal, Vol. 9, No. 14, Jul 15, 2022
Sankar Sennan, Somula Ramasubbareddy, Sathiyabhama Balasubramaniyam, Anand Nayyar, Chaker Abdelaziz Kerrache, Muhammad Bilal, “MADCR: Mobility Aware Dynamic Clustering-Based Routing Protocol in Internet of Vehicles”, China Communications, July 2021
P. Satyanarayana, T. Sushma, M. Arun, V. S. Raiu Talari, S. Gopalakrishnan and V. G. Krishnan, "Enhancement of Energy Efficiency and Network Lifetime Using Modified Cluster Based Routing in Wireless Sensor Networks," 2023 International Conference on Intelligent Systems for Communication, IoT and Security (ICISCoIS), Coimbatore, India, 2023, pp. 127-132, doi: 10.1109/ICISCoIS56541.2023.10100580.
Raj, R., & Sahoo, D. S. S. . (2021). Detection of Botnet Using Deep Learning Architecture Using Chrome 23 Pattern with IOT. Research Journal of Computer Systems and Engineering, 2(2), 38:44. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/31
Patil, R. A. ., & Patil, P. D. . (2023). Skewed Evolving Data Streams Classification with Actionable Knowledge Extraction using Data Approximation and Adaptive Classification Framework. International Journal on Recent and Innovation Trends in Computing and Communication, 11(1), 38–52. https://doi.org/10.17762/ijritcc.v11i1.5985
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