{'fraction_non_alphanumeric': 0.011980267794221282, 'fraction_numerical': 0.004228329809725159, 'mean_word_length': 5.042553191489362, '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 and Preventing Fatal Crashes

A conservative estimate of crash reports of highways would suggest that more than 90% crashes could be attributed to human errors, rest factors are either environmental or are uncertain. Collisions especially at the intersections have hit it off to an alarming rate and hence there is a dire need for a medium that helps preventing fatal crashes. This article includes a study based on a combination of datasets plainly the sensors located at the intersections, the sensor data and the traffic incidents reported around the city. These datasets are eventually converged together for traffic analysis for the city of Austin in Texas. We have proposed a VANET based system for collision prediction and prevention. The proposed model employs two algorithms, a live source of data to detect vehicles approaching an intersection. The model then calculates the sensors in the proximity of this intersection and then based on differential equations of motion, predicts whether a collision may occur. Another algorithm makes use of a weighted approach to balance collision probability and then tested to make the same prediction. The first algorithm delivered an accuracy of 62% while the other one following a weighted approach delivered better accuracy of over 75%. The solution is a simplified approach that shows how real time reported data can be used to predict future collisions.