{'fraction_non_alphanumeric': 0.01634320735444331, 'fraction_numerical': 0.0020429009193054137, 'mean_word_length': 5.282051282051282, '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}}
Road Traffic Prediction Using Artificial Neural Networks

The tremendous growth of the transportation systems and the increased number of vehicles during the last decades has created a significant problem in urban areas, that of traffic congestion. Traffic congestion increases the fuel consumption, causes air pollution and costs many hours per year to the drivers. In the current paper, a novel system targeted to predict the road traffic, using intelligent agents, is proposed. The accurate prediction of traffic will enable the road operators to proactively take appropriate measures, such as changing the traffic light strategy to alleviate the congestion problem. For the prediction process of the intelligent agents, artificial neural networks are employed in order to estimate the vehicles’ speed on the road as an indicator of the traffic congestion. The results showed that the proposed system provides high accuracy with a mean absolute percentage error of about 6.2%.