{'fraction_non_alphanumeric': 0.012458471760797342, 'fraction_numerical': 0.0, 'mean_word_length': 5.276041666666667, '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}}
Predicting Driving Conditions at Mountain Crossings Using Deep Learning

Predicting driving conditions is a complex endeavour, both in terms of defining a metric that reliably describes driving conditions as well as for quantifying and predicting anticipated future conditions. In this paper, we introduce the concept of using measured vehicle speeds as a ground truth for driving conditions at specific locations at mountain crossings, and present a prediction model that estimates future expected driving conditions using deep neural networks. Our network inputs consist of time series measurements from weather stations and vehicle speeds together with weather predictions from national forecasts. Applying multi-step hybrid Convolution and Long Short-Term Memory networks, we predict vehicle speeds six hours into the future to be used as an indicator for the expected driving conditions. The model is trained on historical data and then applied to real-time weather and vehicle data to set up a live system that can be used as a decision-making tool for road entrepreneurs to help them assess whether or not extraordinary measures, such as convoy driving or road closure, may need to be implemented.