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1 |
Multi-Objective reinforcement learning approach for improving safety at intersections with adaptive traffic signal control
رویکرد یادگیری تقویت چند هدفه برای بهبود ایمنی در تقاطع ها با کنترل سیگنال ترافیک تطبیقی-2020 Adaptive traffic signal control (ATSC) systems improve traffic efficiency, but their impacts on traffic safety vary
among different implementations. To improve the traffic safety pro-actively, this study proposes a safety-oriented
ATSC algorithm to optimize traffic efficiency and safety simultaneously. A multi-objective deep reinforcement
learning framework is utilized as the backend algorithm. The proposed algorithm was trained and
evaluated on a simulated isolated intersection built based on real-world traffic data. A real-time crash prediction
model was calibrated to provide the safety measure. The performance of the algorithm was evaluated by the realworld
signal timing provided by the local jurisdiction. The results showed that the algorithm improves both
traffic efficiency and safety compared with the benchmark. A control policy analysis of the proposed ATSC
revealed that the abstracted control rules could help the traditional signal controllers to improve traffic safety,
which might be beneficial if the infrastructure is not ready to adopt ATSCs. A hybrid controller is also proposed
to provide further traffic safety improvement if necessary. To the best of the authors’ knowledge, the proposed
algorithm is the first successful attempt in developing adaptive traffic signal system optimizing traffic safety. Keywords: Traffic safety | Adaptive Signal control | Multi-objective reinforcement learning | Deep learning |
مقاله انگلیسی |
2 |
Decentralized network level adaptive signal control by multi-agent deep reinforcement learning
کنترل سیگنال تطبیقی سطح شبکه غیر متمرکز با یادگیری تقویت عمیق چند عاملی-2019 Adaptive traffic signal control systems are deployed to accommodate real-time traffic conditions. Yet travel demand
and behavior of the individual vehicles might be overseen by their model-based control algorithms and aggregated
input data. Recent development of artificial intelligence, especially the success of deep learning, makes it possible to
utilize information of individual vehicles to control the traffic signals. Several pioneering studies developed modelfree
control algorithms using deep reinforcement learning. However, those studies are limited to isolated intersections
and their effectiveness was only evaluated in ideal simulated traffic conditions by hypothetical benchmarks. To fill the
gap, this study proposes a network-level decentralized adaptive signal control algorithmusing one of the famous deep
reinforcement methods, double dueling deep Q network in the multi-agent reinforcement learning framework. The
proposed algorithm was evaluated by the real-world coordinated actuated signals in a simulated suburban traffic corridor
which emulates the real-field traffic condition. The evaluation results showed that the proposed deepreinforcement-
learning-based algorithm outperforms the benchmark. It is able to reduce 10.27% of the travel time
and 46.46% of the total delay. Keywords: Deep reinforcement learning | Multi-agent reinforcement learning | Adaptive signal control |
مقاله انگلیسی |