Possibility of the Economic Prediction Model based on the Smart Algorithm of the Smart City
Subject Areas : محاسبات نرم در علوم مهندسی
mahsa khodadadi
1
,
Larissa Khodadadi
2
,
روزبه دبیری
3
1 - Assistant Professor, Department of Electrical Engineering, Bonab Branch, Islamic Azad University, Bonab, Iran
2 - Assistant Professor, Department of Electrical Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
3 - Associate Professor, Department of Civil Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
Keywords: Smart city, Deep learning, Traffic management, Economic prediction model,
Abstract :
Smart cities make better use of space and have less traffic, cleaner air and more efficient city services and improve people's quality of life. The large number of vehicles that are constantly moving through congested areas in smart cities complicates the availability of a public parking space. This creates challenges for both traffic and residents. With such a large population, road congestion is a serious challenge. It wastes vital resources like fuel, money and most importantly time. Finding a suitable place to park is one of the reasons for traffic jams on highways. This paper proposes an economic forecasting model based on deep learning for long-term economic growth in smart cities. Traffic management is vital for cities in that it ensures that people can move freely around the city. Many cars trying to reach congested areas in smart cities make it difficult to find a public parking lot. This issue is inconvenient for both drivers and residents. A number of traffic management authorities have implemented an artificial neural network to solve this problem, and modern car systems have come with smart parking solutions. The experimental result of the economic forecasting model based on deep learning improves traffic estimation, accurate prediction of traffic flow, traffic management and intelligent parking compared to existing methods
1-Chen, G., and Zhang, J., (2022), “Applying Artificial Intelligence and Deep Belief Network to predict traffic congestion evacuation performance in smart cities”, Applied Soft Computing, 121, Article ID 108692.#
2- Liu, C., and Ke, L., (2022), “Cloud assisted Internet of things intelligent transportation system and the traffic control system in the smart city”, Journal of Control and Decision, pp. 1–14.#
3- Al-Sayed, A., Al-Shammari, F., Alshutayri, A., Aljojo, N., Aldhahri, E., and Abouola, O., (2022), “The Smart City-Line in Saudi Arabia: Issue and Challenges”, Postmodern Openings, 13, pp. 15–37.#
4- Medina-Salgado, B., S´anchez-DelaCruz, E., Pozos-Parra, P., and Sierra, J. E., (2022), “Urban traffic flow prediction techniques: A review”, Sustainable Computing: Informatics and Systems, 35, Article ID 100739.#
5- Naveed, Q. N., Alqahtani, H., Khan, R. U., Almakdi, S., Alshehri, M., and Abdul Rasheed, M. A., (2022), “An Intelligent Traffic Surveillance System Using Integrated Wireless Sensor Network and Improved Phase Timing Optimization”, Sensors, 22, 9, p. 3333.#
6- Bellini, P., Nesi, P., and Pantaleo, G., (2022), “IoT-enabled smart cities: a review of concepts, frameworks and key technologies”, Applied Sciences, 12, 3, p. 1607.#
7- Raj, E. F. I., Appadurai, M., Darwin, S., and Rani, E. F. I., (2022), “Internet of things (IoT) for sustainable smart cities”, in Internet of 9ings, pp. 163–188, CRC Press, florida FL USA.#
8- Shin, S., Sohn, K., Park, D., and Choi, J. S., (2022), “Special issue on smart cities and its applications”, ETRI Journal, 44, 2, pp. 179–182.#
9- Yuvaraj, N., Praghash, K., Raja, R. A., and Karthikeyan, T., (2022), “An investigation of garbage disposal electric vehicles (GDEVs) integrated with deep neural networking (DNN) and intelligent transportation system (ITS) in smart city management system (SCMS)”, Wireless Personal Communications, 123, 2, pp. 1733–1752.#
10- Paul, A., and Mitra, S., (2022), “Exploring reward efficacy in traffic management using deep reinforcement learning in intelligent transportation system”, ETRI Journal, 44, 2, pp. 194–207.#
11-Naveed, Q. N., Alqahtani, H., Khan, R. U., Almakdi, S., Alshehri, M., and Abdul Rasheed, M. A., (2022), “An intelligent traffic surveillance system using integrated wireless sensor network and improved phase timing optimization”, Sensors, 22, 9, p. 3333.#
12-Rawat, B., Bist, A. S., Apriani, D., Permadi, N. I., and Nabila, E. A., (2022), “AI based drones for security concerns in smart cities”, APTISI Transactions on Management (ATM), 7, 2, pp. 125–130.#
13- Ouallane, A. A., Bahnasse, A., Bakali, A., and Talea, M., (2022), “Overview of road traffic management solutions based on IoT and AI”, Procedia Computer Science, 198, pp. 518–523.#
14- Tomar, I., Sreedevi, I., and Pandey, N., (2022), “State-of-Art review of traffic light synchronization for intelligent vehicles: current status, challenges, and emerging trends”, Electronics, 11, 3, p. 465.
15- Nie, X., Peng, J., Wu, Y., Gupta, B. B., and El-Latif, A. A. A., (2022), “Real-time traffic speed estimation for smart cities with spatial temporal data: a gated graph attention network approach”, Big Data Research, 28, Article ID 100313.#
16- Alsaawy, Y., Alkhodre, A., Abi Sen, A., Alshanqiti, A., Bhat, B. A., and Bahbouh, N. M., (2022), “A comprehensive and effective framework for traffic congestion problem based on the integration of IoT and data analytics”, Applied Sciences, 12, 4, p. 2043.#
17- Li, S., Gao, Y., Ba, T., and Zhao, W., (2022), “Energy-saving methods for urban travel and public transportation in smart cities”, Journal of Interconnection Networks, Article ID 2146006.#
18- Gamel, S. A., Saleh, A. I., and Ali, H. A., (2022), “A fog-based Traffic Light Management Strategy (TLMS) based on fuzzy inference engine”, Neural Computing & Applications, 34, 3, pp. 2187–2205.#
19- Abdel-Basset, M., Eldrandaly, K. A., Shawky, L. A., Elhoseny, M., and AbdelAziz, N. M., (2022), “Hybrid computational intelligence algorithm for autonomous handling of COVID- 19 pandemic emergency in smart cities”, Sustainable Cities and Society, 76, Article ID 103430.#
20- Nguyen, D. D., Rohacs, J., and Rohacs, D., (2021), “Autonomous flight trajectory control system for drones in smart city traffic management”, ISPRS International Journal of Geo-Information, 10, 5, p.338.#
21- Fahim, A., Hasan, M., and Chowdhury, M. A., (2021), “Smart parking systems: comprehensive review based on various aspects”, Heliyon, 7, 5, Article ID e07050.#
22- Khatri, S., Vachhani, H., Shah, S., (2021), “Machine learning models and techniques for VANET based traffic management: implementation issues and challenges”, Peer-to-Peer Networking and Applications, 14, 3, pp. 1778–1805.#
23- Raskar, C., and Nema, S., (2021), “Modified fuzzy-based smart barricade movement for traffic management system”, Wireless Personal Communications, 116, 4, pp. 3351–3370.#
24-Saha, A., Chowdhury, C., Jana, M., and Biswas, S., (2021), “IoT sensor data analysis and fusion applying machine learning and metaheuristic approaches”, Enabling AI Applications in Data Science, Springer, Cham, pp. 441–469.#
25- https://www.kaggle.com/datasets/utathya/smart-city-trafficpatterns. #
26- https://www.kaggle.com/datasets/mbornoe/lisa-traffic-lightdataset. #
27- https://www.kaggle.com/datasets/aryashah2k/highwaytraffic-videos-dataset. #
28- https://www.kaggle.com/datasets/fedesoriano/trafficprediction-dataset.#