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Deep reinforcement learning traffic signal control method combined with state prediction

A reinforcement learning and traffic signal technology, which is applied in the traffic control system of road vehicles, traffic signal control, traffic control system, etc., can solve the problems of limited control effect, achieve the effect of easy prediction, improve traffic efficiency, and reduce the amount of data

Pending Publication Date: 2022-01-21
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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AI Technical Summary

Problems solved by technology

However, for the complex and changeable traffic flow in the actual scene, the optimal control strategy can only be obtained by integrating the current, historical and future states
[0004]Real traffic flow data has the characteristics of sudden change, real-time, periodicity, etc., and is a typical time series data. At present, the signal control methods based on DRL only use the current traffic flow State-based decision-making, limited control effect

Method used

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  • Deep reinforcement learning traffic signal control method combined with state prediction
  • Deep reinforcement learning traffic signal control method combined with state prediction
  • Deep reinforcement learning traffic signal control method combined with state prediction

Examples

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Embodiment Construction

[0021] Such as figure 1 As shown, a deep reinforcement learning traffic signal control method combined with state prediction includes the following steps:

[0022] Step 1: Use SUMO modeling to generate an intersection model. The intersection is a two-way 6-lane lane with a length of 500m. Along the driving direction of the vehicle, the left lane is a left-turn lane, the middle lane is a straight lane, and the right lane is a straight lane plus a right-turn lane. Traffic flow data includes vehicle generation methods, simulation duration, number of vehicles, and driving trajectories. The generation of vehicles in the present invention obeys Weibull distribution, can simulate the situation of high and low traffic peaks in real life, has engineering application value, and its probability density function is:

[0023]

[0024] where λ is the scale parameter set to 1, and a is the shape parameter set to 2. The duration of a simulation round is 2 hours, and the number of vehicle...

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Abstract

The invention discloses a deep reinforcement learning traffic signal control method combined with state prediction. The method comprises the following steps: (1) modeling road network environment and traffic flow data; (2) selecting a deep reinforcement learning algorithm and designing three elements; (3) predicting a future traffic state; (4) training the model; and (5) carrying out experiment for test. The waiting time of vehicles can be shortened, and the passing efficiency of a road network is improved.

Description

technical field [0001] The invention relates to the technical field of intelligent traffic signal control, in particular to a deep reinforcement learning traffic signal control method combined with state prediction. Background technique [0002] With the improvement of living standards, the number of car ownership continues to increase, and the problem of urban traffic congestion is also becoming more and more serious. Traffic signal control is the most direct and cheapest way to improve road traffic efficiency and alleviate traffic congestion. Traditional signal control methods mainly include fixed timing control, sensory control and adaptive control. SCATS (Sydney Coordinated Adaptive Traffic System) and SCOOT (Split Cycle Offset Optimizing Technique) are currently widely used adaptive traffic signal control systems, which use simplified traffic models to solve optimal signal control strategies; however, the establishment of simplified models relies on a large number of B...

Claims

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Application Information

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IPC IPC(8): G08G1/08G08G1/081G06N20/00
CPCG08G1/08G08G1/081G06N20/00Y02T10/40
Inventor 周大可唐慕尧杨欣
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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