{'fraction_non_alphanumeric': 0.017982017982017984, 'fraction_numerical': 0.0, 'mean_word_length': 5.109756097560975, '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}}
Optimizing an agent-based traffic evacuation model using genetic algorithms

Computer simulations are commonly used to model emergencies and discover useful evacuation strategies. The top-down conceptual models typically used for such simulations do not account for differences in individual behavior and how they affect other individuals. To create a more realistic model, this study uses Agent-Based Modeling (ABM) to simulate the evacuation of an urban population in case of a chlorine spill. Since the agents (each a car and driver) in this model do not behave uniformly, and the initial traffic and spill locations are randomized, optimizing traffic lights is challenging. A commercial evolutionary optimizer controls execution of the simulator, seeking to optimize the control of traffic lights in order to minimize deaths and injuries. ABM for a traffic evacuation could prove useful in the real world, when the threat is at a known location such as a power plant or a specific railway segment.