{'fraction_non_alphanumeric': 0.015139442231075698, 'fraction_numerical': 0.0, 'mean_word_length': 5.126829268292683, '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}}
A Bee Colony Optimization Algorithm for the Long-Term Car Pooling Problem

Recently, the big number of vehicles on roadways and the increase in the rising use of private cars have made serious and significant traffic congestion problems in large cities around the world. Severe traffic congestion can have many detrimental effects, such as time loss, air pollution, increased fuel consumption and energy waste. Public transportation systems have the capacity to decrease traffic congestion and be an answer to this increasing transport demand. However, it cannot be the only solution. Another recommended solution for reducing the harmful factors leading to such problems is car pooling. It is a collective transportation system based on the idea that a person shares his private vehicle with one or more people that have the same travel destination. In this paper, a Bee Colony Optimization (BCO) metaheuristic is used to solve the Car Pooling Problem. The BCO model is based on the collective intelligence shown in bee foraging behavior. The proposed algorithm is experimentally tested on benchmark instances of different sizes. Computational results show the effectiveness of our proposed algorithm when compared to several state of the art algorithms.