Abstract
In daily life, parking vehicle is the most common issue arising due to the increase in a number of vehicles every year. Managing a parking system is quite a tough but important factor in minimizing the flow of traffic in cities. Research shows almost 30% of traffic is caused due to drivers watching for parking places. Many systems are installed to manage parking prediction but this system has less accuracy and cost of computation is high. The main intent of this proposed paper is to address these two major requirements and parking availability will be predicted during different time-periods like Weekday, Weekend, and Cultural events with peak hour attribute into consideration. In this paper, the model is designed such that the LSTM algorithm will be used to predict the parking space because it performs efficiently in case of time series prediction. The overall model works efficiently using LSTM for the project because the data-set contains sequential data of parking space information available for every single hour. Along with the prediction system, we are also developing a parking slot booking system, where the user of the app can book a parking slot well in advance. Also along with the prediction model, our system can give user live status of the parking slots available at a particular parking lot. The accuracy of parking prediction will be compared against the parking accuracy obtained using LSTM model.
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Acknowledgements
I want to stretch out my genuine gratitude to all who helped me for the undertaking work. I want to earnestly express gratitude toward Prof. Sachin Deshpande for their direction and steady supervision for giving crucial data with respect to the effort likewise, for their help in completing this task work. I want to offer my thanks towards my folks and individuals from Vidyalankar Institute of Technology for their thoughtful co-activity and support.
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Mishra, A., Deshpande, S. (2021). Deep Learning Based Parking Prediction Using LSTM Approach. In: Senjyu, T., Mahalle, P.N., Perumal, T., Joshi, A. (eds) Information and Communication Technology for Intelligent Systems. ICTIS 2020. Smart Innovation, Systems and Technologies, vol 196. Springer, Singapore. https://doi.org/10.1007/978-981-15-7062-9_59
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DOI: https://doi.org/10.1007/978-981-15-7062-9_59
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