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"TransformerLight: A Novel Sequence Modeling Based Traffic Signaling Me" by Qiang Wu, Mingyuan Li et al.

Traffic signal control (TSC) is still one of the most significant and challenging research problems in the transportation field. Reinforcement learning (RL) has achieved great success in TSC but suffers from critically high learning costs in practical applications due to the excessive trial-and-error learning process. Offline RL is a promising method to reduce learning costs whereas the data distribution shift issue is still up in the air. To this end, in this paper, we formulate TSC as a sequence modeling problem with a sequence of Markov decision process described by states, actions, and rewards from the traffic environment. A novel framework, namely TransformerLight, is introduced, which does not aim to fit into value functions by averaging all possible returns, but produces the best possible actions using a gated Transformer. Additionally, the learning process of TransformerLight is much more stable by replacing the residual connections with gated transformer blocks due to a dynami ....

Dynamic Programming , Reinforcement Learning , Sequence Modeling , Traffic Signal Control ,

"Expression might be enough: representing pressure and demand for reinf" by Liang Zhang, Qiang Wu et al.

Many studies confirmed that a proper traffic state representation is more important than complex algorithms for the classical traffic signal control (TSC) problem. In this paper, we (1) present a novel, flexible and efficient method, namely advanced max pressure (Advanced-MP), taking both running and queuing vehicles into consideration to decide whether to change current signal phase; (2) inventively design the traffic movement representation with the efficient pressure and effective running vehicles from Advanced-MP, namely advanced traffic state (ATS); and (3) develop a reinforcement learning (RL) based algorithm template, called Advanced-XLight, by combining ATS with the latest RL approaches, and generate two RL algorithms, namely”Advanced-MPLight” and”Advanced-CoLight” from Advanced-XLight. Comprehensive experiments on multiple real-world datasets show that: (1) the Advanced-MP outperforms baseline methods, and it is also efficient and reliable for deployment; and (2) Advan ....

Advanced Max Pressure , Dvanced Xlight , Reinforcement Learning , Traffic Signal Control , Raffic State Representation ,

"Multi-agent deep reinforcement learning for traffic signal control wit" by Wei Wei, Qiang Wu et al.

Traffic signal control is an essential and chal-lenging real-world problem, which aims to alleviate traffic congestion by coordinating vehicles' movements at road in-tersections. Deep reinforcement learning (DRL) combines deep neural networks (DNNs) with a framework of reinforcement learning, which is a promising method for adaptive traffic signal control in complex urban traffic networks. Now, multi-agent deep reinforcement learning (MARL) has the potential to deal with traffic signal control at a large scale. However, current traffic signal control systems still rely heavily on simplified rule- based methods in practice. In this paper, we propose: (1) a MARL algorithm based on Nash Equilibrium and DRL, namely Nash Asynchronous Advantage Actor-Critic (Nash-A3C); (2) an urban simulation environment (SENV) to be essentially close to the real-world scenarios. We apply our method in SENV, obtaining better performance than benchmark traffic signal control methods by 22.1%, which prove ....

Gnash Equilibrium , Nash Asynchronous Advantage Actor Critic , Gnash Equilibrium , Ash A3c , Simulation Environment , Traffic Signal Control ,

Derq And AM Signal Partner To Distribute Industry-Leading AI Video Analytics Technology To Advance Smart Infrastructure And Improve Road Safety

Derq, a Detroit- and Dubai-based start-up, announced today the company has partnered with AM Signal, a leader in the distribution of traffic. ....

United States , United Arab Emirates , New Mexico , Karl Jeanbart , Mesa Convention Center , Eight Western States , Intelligent Transportation Systems , Traffic Signal Control , Western United , Zac Ward , Vice President , Annual Conference , Techstars Mobility , Top Road Safety Innovator , Vision Zero ,

The V2X (Vehicle-To-Everything) Communications Ecosystem:

The V2X (Vehicle-To-Everything) Communications Ecosystem:
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