{'fraction_non_alphanumeric': 0.028130671506352088, 'fraction_numerical': 0.0, 'mean_word_length': 5.267045454545454, '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}}
optimising Public Bus Transit Networks Using Deep Reinforcement Learning

Public Transportation Buses are an integral part of our cities, which relies heavily on optimal planning of routes. The quality of the routes directly influences the quality of service provided to passengers, in terms of coverage, directness, and in-vehicle travel time. In addition, it affects the profitability of the transportation system, since the network structure directly influences the operational costs. We propose a system which automates the planning of bus networks based on given demand. The system implements a paradigm, Deep Reinforcement Learning, which has not been used in past literature before for solving the well-documented multi-objective Transit Network Design and Frequency Setting Problem (TNDFSP). The problem involves finding a set of routes in an urban area, each with its own bus frequency. It is considered an NP-Hard combinatorial problem with a massive search space. Compared to state-of-the-art paradigms, our system produced very competitive results, outperforming state-of-the-art solutions.