Abstract
The conflict between limited road resources and rapid car ownership makes the traffic signal timing become a pivotal challenge. Emerging studies have been carried out on adaptive signal timing, but most of them still focus on the throughput of intersections, leaving safety and travel experience unconsidered. This paper proposes a time difference penalized traffic signal timing method by reinforcement learning technique to balance safety and throughput capacity in traffic control system. Firstly, a microcosmic state representation is proposed to integrate the dynamics of both traffic lights and road vehicles, including driver behaviors of lane changing, car-following, previous phase of traffic light and its duration. Secondly, an action space, including 8 signal phases, and a behavior-aware reward function are designed to resist the red-light overflow. Finally, a partial long short term memory (LSTM) network is trained to balance traffic efficiency and traveling experience. In the network training, a parallel sampling method is adopted to obtain experience from multiple environments to accelerate the training convergence in practical application. Experimental results show that the proposed method improves the intersection efficiency up to 14.28% compared to the fixed signal timing and 5.26% compared to DQN while getting rid of red-light overflow time.
Highlights
Today, the global car ownership in the world has exceeded one billion, which led to a series of serious problems, such as traffic congestion, environmental pollution, energy-wasting etc. [1], [2]
Intelligent traffic signal control is a measure to deal with space conflicts, which reduces environmental pollution and energy waste [5], and improves driving efficiency
EXPERIMENTAL ENVIRONMENT In this work, the microscopic traffic simulation software SUMO is employed to construct the experimental environment of traffic, and the Tensorflow is used to build the neural network for deep learning framework
Summary
The global car ownership in the world has exceeded one billion, which led to a series of serious problems, such as traffic congestion, environmental pollution, energy-wasting etc. [1], [2]. The limitation of road resources and the complexity of dynamical traffic flow make it hard to optimize signal timing to improve the capacity at intersection while keeping. Intelligent traffic signal control is a measure to deal with space conflicts, which reduces environmental pollution and energy waste [5], and improves driving efficiency. A short signal cycle is a barrier for improving the efficiency of intersections [13] It is a difficult trade-off between safety and capacity in an adaptive signal control system. Worse still, existing methods have not considered lane changing, which brings a negative effect on intersection efficiency [14] Aiming at these issues, we propose a novel intelligent signal timing method named Rep-DRQN to process signal control in this work.
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