Abstract
As traffic congestion in cities becomes serious, intelligent traffic signal control has been actively studied. Deep Q-Network (DQN), a representative deep reinforcement learning algorithm, is applied to various domains from fully-observable game environment to traffic signal control. Due to the effective performance of DQN, deep reinforcement learning has improved speeds and various DQN extensions have been introduced. However, most traffic signal control researches were performed at a single intersection, and because of the use of virtual simulators, there are limitations that do not take into account variables that affect actual traffic conditions. In this paper, we propose a cooperative traffic signal control with traffic flow prediction (TFP-CTSC) for a multi-intersection. A traffic flow prediction model predicts future traffic state and considers the variables that affect actual traffic conditions. In addition, for cooperative traffic signal control in multi-intersection, each intersection is modeled as an agent, and each agent is trained to take best action by receiving traffic states from the road environment. To deal with multi-intersection efficiently, agents share their traffic information with other adjacent intersections. In the experiment, TFP-CTSC is compared with existing traffic signal control algorithms in a 4 × 4 intersection environment. We verify our traffic flow prediction and cooperative method.
Highlights
Traffic congestion has become a serious problem in most cities
To evaluate traffic flow prediction (TFP)-cooperative traffic signal control (CTSC), we compare our model with an existing reinforcement learning algorithm including Deep Q-Network (DQN) and Q-learning algorithm
It can be seen that TFP-CTSC model outperforms the other model
Summary
Traffic congestion has become a serious problem in most cities. Since the capacity of the road is limited, it is difficult to handle the increasing traffic flow. Traffic signal control [1,2,3]. Traffic flow prediction [4,5,6] methods are the most effective solution to mitigate traffic congestion. Traffic signal control has the advantage of being able to cope immediately with the amount of traffic flow occurring at the intersection level [7]. Traffic flow prediction can cover larger regions based on historical traffic data [8]. The adaptive traffic signal control [9,10] is one of the most effective ways to improve the road environment. Adaptive traffic signal control takes the action of switching the traffic phase or adjusting the traffic signal length
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