Objective The aim of this study was to assess conventional two-dimensional (2D)

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Summary In the development of smart cities, the intelligent transportation system (ITS) plays a major role. The dynamic and chaotic nature of the traffic information makes the accurate forecasting of traffic flow as a challengeable one in ITS. The volume of traffic data increases dramatically. We enter the epoch of big data. Hence, a 1deep architecture is necessary to process, analyze, and inference such a large volume of data. To develop a better traffic flow forecasting model, we proposed an attention‐based convolution neural network long short‐term memory (CNN‐LSTM), a multistep prediction model. The proposed scheme uses the spatial and time‐based details of the traffic data, which are extracted using CNN and LSTM networks to improve the model accuracy. The attention‐based model helps to identify the near term traffic details such as speed that is very important for predicting the future value of flow. The results show that our attention‐based CNN‐LSTM prediction model provides better accuracy in terms of prediction during weekdays and weekend days in the case of peak and nonpeak hours also. We used data from the largest traffic data set the California Department of Transportation (Caltrans) for our prediction work.

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An NRZ OOK visible light communication system is designed and simulated, using a white LED of wavelength 450 nm and a Photodetector. Ambient light noise source such as fluorescent lamp generated by conventional ballast is one of the important sources of degradation which affects the performance of the communication system. By using rectangular optical filter before the photodiode at the receiver, it reduces the ambient noise, improves the BER and clear eye diagram is achieved. Simulation of VLC system is done by using optisystem software. In this paper, performance analysis of optical to electrical power conversion, BER, Threshold, eye opening is done with respect to distance between the transmitter and receiver.