{'fraction_non_alphanumeric': 0.012145748987854251, 'fraction_numerical': 0.0, 'mean_word_length': 5.404145077720207, '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}}
Transportation data visualisation and mining for emergency management

Emergency control systems influence the design, development, and maintenance of public infrastructure in a significant manner. These systems help to restore the infrastructure and sometimes prevent damage in cases of crisis. The road transportation system is an important part of public infrastructure and is one of the critical assets of a nation. Recovering from emergency situations on the road has been one of the major concerns of the traffic management personnel. Many software systems have been developed using advanced technologies to help in the detection of, prevention of and recovery from road crisis. We have developed the CubeView visualisation system based on the concepts of data-visualisation and data mining. Visualisation of loop-detector traffic data can help in the identification of potentially important patterns embedded in the data. CubeView helps in summarising the major trends in traffic patterns for both historical and current data. This paper presents the idea of applying the concepts of data-visualisation and outlier detection to road traffic data using the CubeView system for emergency situation control and management planning.