{'fraction_non_alphanumeric': 0.024390243902439025, 'fraction_numerical': 0.0, 'mean_word_length': 5.315384615384615, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Efficient Future Prediction using Neural Network in Vehicular Ad hoc Networks

Vehicular Ad-hoc Network (VANET) is a primary part of Intelligent Transportation System (ITS). VANETS provides mechanisms for vehicles to communicate with road side units (RSUs). This information represents past data and predicts upcoming events. The emergence of Neural Networks (NNs) and its variants can simulate learning model for future prediction. Utilizing vehicular network and recognition property of neural network, it is now possible to analyze data of real-time driving and road conditions to predict future traffic condition. This system can be used for traffic monitoring, accident prediction or road hazard detection. In this paper, a detailed survey is provided about use of neural networks for prediction purposes in VANETs.