강화학습을 활용한 고속도로 가변제한속도 및 램프미터링 전략 개발

학위논문(석사) -- 서울대학교대학원 : 공과대학 건설환경공학부, 2022.2. 김동규.Recently, to resolve societal problems caused by traffic congestion, traffic control strategies have been developed to operate freeways efficiently. The representative strategies to effectively manage freeway flow are variable speed limit (VSL) control and the coordinated ramp metering (RM) strategy. This paper aims to develop a dynamic VSL and RM control algorithm to obtain efficient traffic flow on freeways using deep reinforcement learning (DRL). The traffic control strategies applying the deep deterministic policy gradient (DDPG) algorithm are tested through traffic simulation in the freeway section with multiple VSL and RM controls. The results show that implementing the strategy alleviates the congestion in the on-ramp section and shifts to the overall sections. For most cases, the VSL or RM strategy improves the overall flow rates by reducing the density and improving the average speed of the vehicles. However, VSL or RM control may not be appropriate, particularly at the high level of traffic flow. It is required to introduce the selective application of the integrated control strategies according to the level of traffic flow. It is found that the integrated strategy can be used when including the relationship between each state detector in multiple VSL sections and lanes by applying the adjacency matrix in the neural network layer. The result of this study implies the effectiveness of DRL-based VSL and the RM strategy and the importance of the spatial correlation between the state detectors.최근에는 교통혼잡으로 인한 사회적 문제를 해결하기 위해 고속도로를 효율적으로 운영하기 위한 교통통제 전략이 다양하게 개발되고 있다. 고속도로 교통류를 효과적으로 관리하기 위한 대표적인 전략으로는 차로별 제한속도를 다르게 적용하는 가변 속도 제한(VSL) 제어와 진입 램프에서 신호를 통해 차량을 통제하는 램프 미터링(RM) 전략 등이 있다. 본 연구의 목표는 심층 강화 학습(deep reinforcement learning)을 활용하여 고속도로의 효율적인 교통 흐름을 얻기 위해 동적 VSL 및 RM 제어 알고리즘을 개발하는 것이다. 고속도로의 여러 VSL과 RM 구간에서 시뮬레이션을 통해 심층 강화학습 알고리즘 중 하나인 deep deterministic policy gradient (DDPG) 알고리즘을 적용한 교통류 제어 전략을 검증한다. 실험 결과, 강화학습 기반 VSL 또는 RM 전략을 적용하는 것이 램프 진입로 구간의 혼잡을 완화하고 나아가 전체 구간의 혼잡을 줄이는 것으로 나타났다. 대부분의 경우 VSL이나 RM 전략은 본선과 진입로 구간의 밀도를 줄이고 차량의 평균 통행 속도를 증가시켜 전체 교통 흐름을 향상시킨다. VSL 또는 RM 전략들은 높은 수준의 교통류에서 적절하지 않을 수 있어 교통류 수준에 따른 전략의 선택적 도입이 필요하다. 또한 검지기간 지리적 거리와 관련한 인접 행렬을 포함하는 graph neural network layer이 여러 지점 검지기의 공간적 상관 관계를 감지하는 데 이용될 수 있다. 본 연구의 결과는 강화학습 기반 VSL과 RM 전략 도입의 필요성과 지점 검지기 간의 공간적 상관관계의 중요성을 반영하는 전략 도입의 효과를 시사한다.Chapter 1. Introduction 1 Chapter 2. Literature Review 4 Chapter 3. Methods 8 3.1. Study Area and the Collection of Data 8 3.2. Simulation Framework 11 3.3. Trip Generation and Route Choice 13 3.4. Deep Deterministic Policy Gradient (DDPG) Algorithm 14 3.5. Graph Convolution Network (GCN) Layer 17 3.6. RL Formulation 18 Chapter 4. Results 20 4.1. VSL and RM 20 4.2. Efficiency according to the flow rate 28 4.3. Effectiveness of the GCN Layer 33 Chapter 5. Conclusion 34 Bibliography 37 Abstract in Korean 44석

Vision-Based Lane-Changing Behavior Detection Using Deep Residual Neural Network

Accurate lane localization and lane change detection are crucial in advanced driver assistance systems and autonomous driving systems for safer and more efficient trajectory planning. Conventional localization devices such as Global Positioning System only provide road-level resolution for car navigation, which is incompetent to assist in lane-level decision making. The state of art technique for lane localization is to use Light Detection and Ranging sensors to correct the global localization error and achieve centimeter-level accuracy, but the real-time implementation and popularization for LiDAR is still limited by its computational burden and current cost. As a cost-effective alternative, vision-based lane change detection has been highly regarded for affordable autonomous vehicles to support lane-level localization. A deep learning-based computer vision system is developed to detect the lane change behavior using the images captured by a front-view camera mounted on the vehicle and data from the inertial measurement unit for highway driving. Testing results on real-world driving data have shown that the proposed method is robust with real-time working ability and could achieve around 87% lane change detection accuracy. Compared to the average human reaction to visual stimuli, the proposed computer vision system works 9 times faster, which makes it capable of helping make life-saving decisions in time

The State-of-the-art of Coordinated Ramp Control with Mixed Traffic Conditions

Ramp metering, a traditional traffic control strategy for conventional vehicles, has been widely deployed around the world since the 1960s. On the other hand, the last decade has witnessed significant advances in connected and automated vehicle (CAV) technology and its great potential for improving safety, mobility and environmental sustainability. Therefore, a large amount of research has been conducted on cooperative ramp merging for CAVs only. However, it is expected that the phase of mixed traffic, namely the coexistence of both human-driven vehicles and CAVs, would last for a long time. Since there is little research on the system-wide ramp control with mixed traffic conditions, the paper aims to close this gap by proposing an innovative system architecture and reviewing the state-of-the-art studies on the key components of the proposed system. These components include traffic state estimation, ramp metering, driving behavior modeling, and coordination of CAVs. All reviewed literature plot an extensive landscape for the proposed system-wide coordinated ramp control with mixed traffic conditions.Comment: 8 pages, 1 figure, IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE - ITSC 201

MARVEL: Multi-Agent Reinforcement-Learning for Large-Scale Variable Speed Limits

Variable speed limit (VSL) control is a promising traffic management strategy for enhancing safety and mobility. This work introduces MARVEL, a multi-agent reinforcement learning (MARL) framework for implementing large-scale VSL control on freeway corridors using only commonly available data. The agents learn through a reward structure that incorporates adaptability to traffic conditions, safety, and mobility; enabling coordination among the agents. The proposed framework scales to cover corridors with many gantries thanks to a parameter sharing among all VSL agents. The agents are trained in a microsimulation environment based on a short freeway stretch with 8 gantries spanning 7 miles and tested with 34 gantries spanning 17 miles of I-24 near Nashville, TN. MARVEL improves traffic safety by 63.4% compared to the no control scenario and enhances traffic mobility by 14.6% compared to a state-of-the-practice algorithm that has been deployed on I-24. An explainability analysis is undertaken to explore the learned policy under different traffic conditions and the results provide insights into the decision-making process of agents. Finally, we test the policy learned from the simulation-based experiments on real input data from I-24 to illustrate the potential deployment capability of the learned policy

Deep Reinforcement Learning Approach for Lagrangian Control: Improving Freeway Bottleneck Throughput Via Variable Speed Limit

Connected vehicles (CVs) will enable new applications to improve traffic flow. The focus of this dissertation is to investigate how reinforcement learning (RL) control for the variable speed limit (VSL) through CVs can be generalized to improve traffic flow at different freeway bottlenecks. Three different bottlenecks are investigated: A sag curve, where the gradient changes from negative to positive values causes a reduction in the roadway capacity and congestion; a lane reduction, where three lanes merge to two lanes and cause congestion, and finally, an on-ramp, where increase in demand on a multilane freeway causes capacity drop. An RL algorithm is developed and implemented in a simulation environment for controlling a VSL in the upstream to manipulate the inflow of vehicles to the bottleneck on a freeway to minimize delays and increase the throughput. CVs are assumed to receive VSL messages through Infrastructure-to-Vehicle (I2V) communications technologies. Asynchronous Advantage Actor-Critic (A3C) algorithms are developed for each bottleneck to determine optimal VSL policies. Through these RL control algorithms, the speed of CVs are manipulated in the upstream of the bottleneck to avoid or minimize congestion. Various market penetration rates for CVs are considered in the simulations. It is demonstrated that the RL algorithm is able to adapt to stochastic arrivals of CVs and achieve significant improvements even at low market penetration rates of CVs, and the RL algorithm is able to find solution for all three bottlenecks. The results also show that the RL-based solutions outperform feedback-control-based solutions

Imagination-augmented reinforcement learning framework for variable speed limit control

Variable Speed Limit (VSL) is a commonly applied active traffic management measure for urban motorways. In recent years, model-based and model-free approaches have been extensively adopted to solve VSL optimization problems. However, the success of model-based VSL relies heavily on the nature of the environmental model adopted (e.g., traffic flow model). Implicit environment models may result in inappropriate control actions. Although model-free approaches are able to directly map raw measurements to control actions without a need for an environment model, they usually require large amounts of training data. In order to address these issues, we propose an Imagination-Augmented Agent (I2A) for VSL control. The I2A consists an imagination path and a model-free path, which work together to generate appropriate control actions. The simulation results show that the proposed I2A agent outperforms other tested Reinforcement Learning (RL) agents in terms of Total Time Spent and bottleneck volume