IoT Based Smart Parking System Using Deep Long Short Memory Network

Abstract

Traffic congestion is one of the most notable urban transport problems, as it causes high energy consumption and air pollution. Unavailability of free parking spaces is one of the major reasons for traffic jams. Congestion and parking are interrelated because searching for a free parking spot creates additional delays and increase local circulation. In the center of large cities, 10% of the traffic circulation is due to cruising, as drivers nearly spend 20 min searching for free parking space. Therefore, it is necessary to develop a parking space availability prediction system that can inform the drivers in advance about the location-wise, day-wise, and hour-wise occupancy of parking lots. In this paper, we proposed a framework based on a deep long short term memory network to predict the availability of parking space with the integration of Internet of Things (IoT), cloud technology, and sensor networks. We use the Birmingham parking sensors dataset to evaluate the performance of deep long short term memory networks. Three types of experiments are performed to predict the availability of free parking space which is based on location, days of a week, and working hours of a day. The experimental results show that the proposed model outperforms the state-of-the-art prediction models.

RMU-Net: A Novel Residual Mobile U-Net Model for Brain Tumor Segmentation from MR Images

“…The outcome of the network performs well as compared to previous approaches. The deep LSTM used by Ghulam Ali et al [16] and the IoT sensors integration helps detect the availability of available parking slots. Birmingham parking dataset was used for the evaluation of the model.…”

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

“…Solutions to this problem have been formulated both as a regression problem as well as a classification one, both utilizing imaging and/or other occupancy sensing modalities. Regression solutions [205][206][207] are typically used to predict a parking lots occupancy levels in the future whereas classification systems [208][209][210] involve guiding drivers according to the shortest distance as well as used for user localization purposes within such lots. In addition to cloud based approaches, edge computing systems for smart parking have also been devised as suggested in [211,212] who deploy CNNs on edge devices for occupancy detection and user localization, respectively.…”

“…A summary of IoT based AI for Smart Transport has been given in Table 12. [207] LR [206] Homogeneous (RFID data from cars) DNN+ CNN [208] Classification-Different positions based on beacons installed Homogeneous (Radio frequency signal strength) DT [209] Classification [217] Regression-Arrival time prediction RF [220] Classification-Localization, as on platform or train Homogeneous (Wi-Fi signal parameters) Transport management (Traffic flow) NB [213] Cloud Classification-Different traffic states Homogeneous (GPS data, current and historical) RF [214] Heterogeneous (Weather, Road data) RNN (LSTM) [216] Regression-Traffic flow prediction Homogeneous (Traffic flow data [vehicle speed count etc.]) RNN (LSTM) [221] SAE + RNN (LSTM) [215] Transport management (Traffic Accident detection) RF [218] Classification-Accident or not Homogeneous (Velocity, Position)…”

Integration of Discrete Wavelet Transform, DBSCAN, and Classifiers for Efficient Content Based Image Retrieval

“…The color is the wavelength-dependent approach and is said to be a core feature of images as it cannot be altered based on the orientation and size of the object. It has been widely used for image retrieval and recognition of objects [1,27,28].…”