3/23/2023 0 Comments Bus route file sumoYet, these models are costly and impractical, and there are surprisingly few published examples robustly validated with empirical data. Large-scale microsimulations are increasingly resourceful tools for analysing in detail citywide effects and alternative scenarios of our policy decisions, approximating the ideal of ‘urban digital twins’. These findings indicate a further potential to improve the quality of life in cities using positive counterintuitive effects of street repurposing and it is an opportunity for participatory and sustainable city-making beyond the ongoing public debate. Comparing existing plans designed by the City of Barcelona with variants of those, we find positive counterintuitive effects related to "Braess' Paradox", which result in the reduction of emissions (-8% of main pollutants) and traffic congestion (-14% of travel time) solely by closing some streets to motor vehicles. In our study, we evaluate a number of "what-if scenarios" of "city pruning" regarding traffic restrictions for Barcelona by means of realistic, agent-based computer simulations in order to identify their impact on travel performance and the environment. The experimental results show that the proposed DRL -based cooperative approach can converge in the parking space allocation problem involving a large AVP system and achieve greater improvement of global AVP efficiency than can the other parking methods.Ĭurrent trends in urban planning aim at the reduction of space for private vehicles to promote alternative mobility, more diverse activities on streets, and reduced pollution for healthier cities. After action embedding, the deep deterministic policy gradient (DDPG) is employed as the training algorithm. Next, because the current reinforcement learning methods are difficult to apply to parking space allocation involving large numbers of discrete actions, a cost-based method of parking allocation action embedding is proposed to embed the discrete parking actions in a continuous space, which the actor can generalize. Then, a reward shaping method oriented to the global objective is designed. First, the problem of parking space allocation is formulated as a Markov decision process (MDP). Therefore, in this study, a system-side deep reinforcement learning (DRL)-based cooperative approach is proposed to solve the parking space allocation problem in a large AVP environment. However, in an AVP system, the traditional vehicle-side greedy search strategy for available parking spaces is likely to achieve low global efficiency and poses a high risk of collision. Furthermore, our results indicate that the advantage of using A3C is not that visible in the single intersection scenario, but notable in the four and nine intersections scenario.Īutomated valet parking (AVP) is one of the most advanced technologies for improving parking efficiency and security. Results given in this study show that Asynchronous Advantage Actor-Critic (A3C) got a faster learning time and effective in gaining optimal result compared to Advantage Actor-Critic (A2C) using real-life-like traffic rules environments in SUMO with an open-source repository. This paper will discuss the experiments of Asynchronous Advantage Actor-Critic performance compared to stable baseline Advantage Actor-Critic performance using real-life-like traffic rules environments in SUMO with an open-source repository using the Indonesian traffic rules Environment. One of the algorithms that can be used is Asynchronous Advantage Actor-Critic (A3C). Reinforcement learning is a popular approach for intelligent traffic signal controller as it has advantages such as self learning without the need of supervision, goal oriented, real-time adaptation and curse of dimensionality management. This practice has a weakness in its inability to adapt to various traffic conditions. The traffic lights, especially in Indonesia, are conventionally giving the signals it always repeats the same cycle over time. To tackle this problem, many places in the world place a traffic light in most major or dense traffic intersections. Traffic congestion is one of the most common problems in line with the increase in population and economic activity.
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