Abstract
Intelligent Vehicular Ad Hoc Networks which integrates deep learning techniques with modern vehicular communication networks can play a major role in prediction of vehicular traffic as well as efficient dissemination of critical information between vehicular nodes. An accurate traffic prediction mechanism is a key requirement for numerous applications of Intelligent Transportation System such as traffic management, accident prevention, route guidance and public safety. In this paper, a deep learning approach is proposed which is based on Convolutional Neural Network (CNN) combined with Temporal Convolutional Network (TCN), to predict the traffic patterns of vehicles. External factors like weather, weekend and holidays are considered along with internal factors such as location and time for analyzing their effect on vehicular traffic. Integration of CNN and TCN, captures spatio-temporal features, which are then merged with external factors to obtain a more accurate predicted traffic information. This predicted value is further disseminated within the vehicular network. Dataset of Indian cities is taken and converted to matrices of time vs space. Experimental results illustrate that our model outperforms other state-of-the-art techniques in regard to efficiency and accuracy.
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Bansal, N., Bali, R.S. (2024). Deep Learning Mechanism for Region Based Urban Traffic Flow Forecasting. In: Challa, R.K., et al. Artificial Intelligence of Things. ICAIoT 2023. Communications in Computer and Information Science, vol 1930. Springer, Cham. https://doi.org/10.1007/978-3-031-48781-1_27
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