Traffic simulationAdaptive traffic signal control (ATSC) is a promising technique to improve the efficiency of signalized intersections, especially in the era of connected vehicles (CVs) when real-time information on vehicle positions and trajectories is available. Numerous ATSC algorithms have been ...
Recent research reveals that reinforcement learning can potentially perform optimal decision-making compared to traditional methods like Adaptive Traffic Signal Control (ATSC). With the development of knowledge through trial and error, the Deep Reinforcement Learning (DRL) technique shows its feasibility for...
Adaptive Traffic Signal Control : Exploring Reward Definition For Reinforcement Learning Shiratori, "Adaptive traffic signal control: Deep reinforcement learning algorithm with experience replay and target network," arXiv preprint arXiv:1705.02755, May 2017.J. Gao, Y. Shen, J. Liu, M. Ito, and N...
Presents a distributed approach to traffic signal control, where the signal timing parameters are a given intersection are adjusted as functions of the local traffic condition and of the signal timing parameters at adjacent intersections. The signal timing at an intersection is defined by three paramet...
Traffic signal control systemsTransportation departmentsUrban transportationIn the current Regional Transportation Plan (RTP) for Southern California, the Southern California Association of Governments (SCAG) identifies dedicated truck lanes as a means to more efficiently keep goods movement flowing smoothly, ...
www.cfluid.com|基于2个网页 3. 自适应交通控制 文献[28] 将自适应公交调度(adaptive transit operation)策略和自适应交通控制(adaptive signal control)策略同时进行应用研究。 www.docin.com|基于 1 个网页
Reinforcement learning (RL) has been the object of investigation of many recent papers as a promising approach to control such a stochastic environment. The goal of this paper is to analyze the feasibility of RL, particularly the use of Q-learning algorithm for adaptive traffic signal control in...
Smart cities are characterized by their use of intelligent transportation systems (ITS), which utilize advanced traffic signal control methods to achieve effective and efficient traffic operations. Recently, due to significant progress in artificial intelligence, research has focused on machine learning-base...
The transportation demand is rapidly growing in metropolises, resulting in chronic traffic congestions in dense downtown areas. Adaptive traffic signal control as the principle part of intelligent transportation systems has a primary role to effectively reduce traffic congestion by making a real-time ada...
Many traffic control systems, such as RHODES (Head et al., 1992) and UTOPIA (Mauro and Di Taranto, 1989), incorporate network signal control at the strategic level of a hierarchical control structure, to take into account the influence of travellers’ route choice behaviour. From a network ...