{'fraction_non_alphanumeric': 0.014660493827160493, 'fraction_numerical': 0.0, 'mean_word_length': 5.826315789473684, '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}}
Evaluation of anticipatory stigmergy strategies for traffic management

In this paper, we propose and evaluate an anticipatory stigmergy model for traffic management. Recently, dynamic coordination methods are becoming possible using the more short-term traffic information that can be provided by probe-vehicle information or smart phones. Some approaches have been trying to handle short-term traffic information in which a stigmergy-based approach is employed as an indirect communication method for cooperation among distributed agents and for managing traffic congestion. One drawback of these approaches is that handling near-future congestion remains problematic because stigmergies are basically past information. We propose anticipatory stigmergy for sharing information on near-future traffic and allocating drivers adequately. In this model, all vehicles submit their near-future intention as anticipatory stigmergy to search again their routes. Because it might be ineffective that all drivers choose the recommended route by anticipatory stigmergy, we examine strategies to allocate drivers the recommended link based on the residual distance or the lost time of traffic congestion. Our preliminary results demonstrate that anticipatory stigmergy with allocation strategy works well.