Abstract
Cities are evolving into smart cities, and most of its day-to-day activities are automated. One such important activity is the city vehicle parking mechanism. We propose a smart parking system which helps drivers to choose a parking slot based on historical data showing various trends, the distance vector from the location of the vehicle to the parking area and insights from the Google Traffic API. We also attempt to predict the car park occupancy on a certain day of the week at a certain time of the day using a machine learning algorithm. In contrast to the existing systems where a vehicle lies in waiting at a parking area if it is full, since the driver targets only a single car park, our system could suggest alternate car parking areas in advance based on various factors if the targeted car park is already full. This results in increased convenience and reduction in waiting time for drivers looking for a parking slot.
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Sumini, M.V., Mulerikkal, J., Ramkumar, P.B., Tharakan, P. (2020). Fuzzy Concepts and Machine Learning Algorithms for Car Park Occupancy and Route Prediction. In: Bansal, J., Gupta, M., Sharma, H., Agarwal, B. (eds) Communication and Intelligent Systems. ICCIS 2019. Lecture Notes in Networks and Systems, vol 120. Springer, Singapore. https://doi.org/10.1007/978-981-15-3325-9_3
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DOI: https://doi.org/10.1007/978-981-15-3325-9_3
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