经济驱动的自适应交通信号控制
Economic-Driven Adaptive Traffic Signal Control
摘要：
随着新兴的互联汽车技术和智能道路，对智能自适应交通信号控制的需求比以往任何时候都多。提出了一种新的经济驱动的自适应交通信号控制（eATSC）模型，该模型引入了经济学中定义的超控制变量--利率，用于信号交叉口的交通信号控制。eATSC使用连续复合函数，该函数捕获车辆总数和每辆车的累积等待时间，以计算不同方向的罚款。计算出的罚款增长的等待时间，并用于信号控制决策。每个交叉口被分配两个智能代理调整利率和信号灯长度为不同的方向，根据交通模式，分别。该问题被公式化为马尔可夫决策过程（MDP）问题，以减少冗余，并利用两个智能体的双重决斗深度Q网络（DDDQN）来解决这个问题。在最优策略下，智能体可以选择最优的利率和信号时间，以最小化交通拥堵的可能性。为了评估我们的方法的优越性，VISSIM仿真模型与经典的四腿信号交叉口。结果表明，所提出的模型是足够的，能够维持健康的交通流在交叉口。
英文摘要：
With the emerging connected-vehicle technologies and smart roads, the need for intelligent adaptive traffic signal controls is more than ever before. This paper proposes a novel Economic-driven Adaptive Traffic Signal Control (eATSC) model with a hyper control variable - interest rate defined in economics for traffic signal control at signalized intersections. The eATSC uses a continuous compounding function that captures both the total number of vehicles and the accumulated waiting time of each vehicle to compute penalties for different directions. The computed penalties grow with waiting time and is used for signal control decisions. Each intersection is assigned two intelligent agents adjusting interest rate and signal length for different directions according to the traffic patterns, respectively. The problem is formulated as a Markov Decision Process (MDP) problem to reduce congestions, and a two-agent Double Dueling Deep Q Network (DDDQN) is utilized to solve the problem. Under the optimal policy, the agents can select the optimal interest rates and signal time to minimize the likelihood of traffic congestion. To evaluate the superiority of our method, a VISSIM simulation model with classic four-leg signalized intersections is developed. The results indicate that the proposed model is adequately able to maintain healthy traffic flow at the intersection.
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