{'fraction_non_alphanumeric': 0.021739130434782608, 'fraction_numerical': 0.0006038647342995169, 'mean_word_length': 5.601593625498008, '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}}
Analysis of Large-Scale Incidents on Urban Freeways

Traffic incidents on urban freeways are a major source of congestion and travel time uncertainty. In particular, large-scale incidents have longer durations and need more incident response resources. These incidents cause severe problems such as longer traffic queues, substantial delays, and secondary incidents. Large-scale incidents deserve more attention by practitioners and researchers. The objective of this study was to analyze large-scale incidents and explore their correlates and implications for traffic operations. An innovative analysis method based on a detailed incident data set from Hampton Roads, Virginia, was developed. This study defined large-scale incidents as having a minimum duration of 2 h, according to guidance from the Manual of Uniform Traffic Control Devices. The study discovered how the spatial and temporal patterns of large-scale incidents varied. Forty percent of the large-scale incidents blocked all lanes of traffic during some point in their duration. Rigorous statistical models were estimated to quantify associated key factors that included incident characteristics, roadway geometry, traffic flow, and operational responses. Results indicated that given large-scale incidents, their longer durations were associated with extreme events, for example, occurring in a work zone, the presence of curvature on the segment where the incident occurred, morning peak hours, and occurrence of secondary incidents. The new findings provide insights concerning the understanding of large-scale incidents and have certain implications for effective incident management.