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
(Assistant Professor, Department of Electrical Engineering, Bonab Branch, Islamic Azad University, Bonab, Iran)
Larissa Khodadadi
2
(Assistant Professor, Department of Electrical Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran)
روزبه دبیری
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
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