Improving Traffic Safety and Efficiency by Adaptive Signal Control Based on Deep Reinforcement Learning PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Improving Traffic Safety and Efficiency by Adaptive Signal Control Based on Deep Reinforcement Learning PDF full book. Access full book title Improving Traffic Safety and Efficiency by Adaptive Signal Control Based on Deep Reinforcement Learning by Yaobang Gong. Download full books in PDF and EPUB format.
Author: Yaobang Gong
Publisher:
ISBN:
Category :
Languages : en
Pages : 126
Get Book Here
Book Description
As one of the most important Active Traffic Management strategies, Adaptive Traffic Signal Control (ATSC) helps improve traffic operation of signalized arterials and urban roads by adjusting the signal timing to accommodate real-time traffic conditions. Recently, with the rapid development of artificial intelligence, many researchers have employed deep reinforcement learning (DRL) algorithms to develop ATSCs. However, most of them are not practice-ready. The reasons are two-fold: first, they are not developed based on real-world traffic dynamics and most of them require the complete information of the entire traffic system. Second, their impact on traffic safety is always a concern by researchers and practitioners but remains unclear. Aiming at making the DRL-based ATSC more implementable, existing traffic detection systems on arterials were reviewed and investigated to provide high-quality data feeds to ATSCs. Specifically, a machine-learning frameworks were developed to improve the quality of and pedestrian and bicyclist’s count data. Then, to evaluate the effectiveness of DRL-based ATSC on the real-world traffic dynamics, a decentralized network-level ATSC using multi-agent DRL was developed and evaluated in a simulated real-world network. The evaluation results confirmed that the proposed ATSC outperforms the actuated traffic signals in the field in terms of travel time reduction. To address the potential safety issue of DRL based ATSC, an ATSC algorithm optimizing simultaneously both traffic efficiency and safety was proposed based on multi-objective DRL. The developed ATSC was tested in a simulated real-world intersection and it successfully improved traffic safety without deteriorating efficiency. In conclusion, the proposed ATSCs are capable of effectively controlling real-world traffic and benefiting both traffic efficiency and safety.
Author: Yaobang Gong
Publisher:
ISBN:
Category :
Languages : en
Pages : 126
Get Book Here
Book Description
As one of the most important Active Traffic Management strategies, Adaptive Traffic Signal Control (ATSC) helps improve traffic operation of signalized arterials and urban roads by adjusting the signal timing to accommodate real-time traffic conditions. Recently, with the rapid development of artificial intelligence, many researchers have employed deep reinforcement learning (DRL) algorithms to develop ATSCs. However, most of them are not practice-ready. The reasons are two-fold: first, they are not developed based on real-world traffic dynamics and most of them require the complete information of the entire traffic system. Second, their impact on traffic safety is always a concern by researchers and practitioners but remains unclear. Aiming at making the DRL-based ATSC more implementable, existing traffic detection systems on arterials were reviewed and investigated to provide high-quality data feeds to ATSCs. Specifically, a machine-learning frameworks were developed to improve the quality of and pedestrian and bicyclist’s count data. Then, to evaluate the effectiveness of DRL-based ATSC on the real-world traffic dynamics, a decentralized network-level ATSC using multi-agent DRL was developed and evaluated in a simulated real-world network. The evaluation results confirmed that the proposed ATSC outperforms the actuated traffic signals in the field in terms of travel time reduction. To address the potential safety issue of DRL based ATSC, an ATSC algorithm optimizing simultaneously both traffic efficiency and safety was proposed based on multi-objective DRL. The developed ATSC was tested in a simulated real-world intersection and it successfully improved traffic safety without deteriorating efficiency. In conclusion, the proposed ATSCs are capable of effectively controlling real-world traffic and benefiting both traffic efficiency and safety.
Author: Tianxin Li (M.S. in Engineering)
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
Traffic signal control is an essential aspect of urban mobility that significantly impacts the efficiency and safety of transportation networks. Traditional traffic signal control systems rely on fixed-time or actuated signal timings, which may not adapt to the dynamic traffic demands and congestion patterns. Therefore, researchers and practitioners have increasingly turned to reinforcement learning (RL) techniques as a promising approach to improve the performance of traffic signal control. This dissertation investigates the application of RL algorithms to traffic signal control, aiming to optimize traffic flow and reduce congestion. The study develops a simulation model of a signalized intersection and trains RL agents to learn how to adjust signal timings based on real-time traffic conditions. The RL agents are designed to learn from experience and adapt to changing traffic patterns, thereby improving the efficiency of traffic flow, even for scenarios in which traffic incidents occur in the network. In this dissertation, the potential benefits of using RL algorithms to optimize traffic signal control in scenarios with and without traffic incidents were explored. To achieve this, an incident generation module was developed using the open-source traffic signal performance simulation framework that relies on the SUMO software. This module includes emergency response vehicles to mimic the realistic impact of traffic incidents and generates incidents randomly in the network. By exposing the RL agent to this environment, it can learn from the experience and optimize traffic signal control to reduce system delay. The study began with a single intersection scenario, where the DQN algorithm was modeled to form the RL agent traffic signal controller. To improve the training process and model performance, experience replay and target network were implemented to solve the limitations of DQN. Hyperparameter tuning was conducted to find the best parameter combination for the training process, and the results showed that DQN outperformed other controllers in terms of the system-wise and intersection-wise queue distribution and vehicle delay. The study was then extended to a small corridor with 2 intersections and a grid network (2x2 intersection), and the incident generation module was used to expose the RL agent to different traffic scenarios. Again, hyperparameter tuning was conducted, and the DQN model outperformed other controllers in terms of reducing congestion and improving the system performance. The robustness of the DQN performance was also tested with different demands, and the microsimulation results showed that the DQN performance was consistent. Overall, this study highlights the potential of RL algorithms to optimize traffic signal control in scenarios with and without traffic incidents. The incident generation module developed in this study provides a realistic environment for the RL agent to learn and adapt, leading to improved system performance and reduced congestion. In addition, hyperparameter tuning is essential to lay down a solid foundation for the RL training process
Author: Tian Tan
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
Adaptive traffic signal control (ATSC) system serves a significant role for relieving urban traffic congestion. The system is capable of adjusting signal phases and timings of all traffic lights simultaneously according to real-time traffic sensor data, resulting in a better overall traffic management and an improved traffic condition on road. In recent years, deep reinforcement learning (DRL), one powerful paradigm in artificial intelligence (AI) for sequential decision-making, has drawn great attention from transportation researchers. The following three properties of DRL make it very attractive and ideal for the next generation ATSC system: (1) model-free: DRL reasons about the optimal control strategies directly from data without making additional assumptions on the underlying traffic distributions and traffic flows. Compared with traditional traffic optimization methods, DRL avoids the cumbersome formulation of traffic dynamics and modeling; (2) self-learning: DRL self-learns the signal control knowledge from traffic data with minimal human expertise; (3) simple data requirement: by using large nonlinear neural networks as function approximators, DRL has enough representation power to map directly from simple traffic measurements, e.g. queue length and waiting time, to signal control policies. This thesis focuses on building data-driven and adaptive controllers via deep reinforcement learning for large-scale traffic signal control systems. In particular, the thesis first proposes a hierarchical decentralized-to-centralized DRL framework for large-scale ATSC to better coordinate multiple signalized intersections in the traffic system. Second, the thesis introduces efficient DRL with efficient exploration for ATSC to greatly improve sample complexity of DRL algorithms, making them more suitable for real-world control systems. Furthermore, the thesis combines multi-agent system with efficient DRL to solve large-scale ATSC problems that have multiple intersections. Finally, the thesis presents several algorithmic extensions to handle complex topology and heterogeneous intersections in real-world traffic networks. To gauge the performance of the presented DRL algorithms, various experiments have been conducted and included in the thesis both on small-scale and on large-scale simulated traffic networks. The empirical results have demonstrated that the proposed DRL algorithms outperform both rule-based control policy and commonly-used off-the-shelf DRL algorithms by a significant margin. Moreover, the proposed efficient MARL algorithms have achieved the state-of-the-art performance with improved sample-complexity for large-scale ATSC.
Author: Dunhao Zhong
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
Vehicles have become an indispensable means of transportation to ensure people's travel and living materials. However, with the increasing number of vehicles, traffic congestion has become severe and caused a lot of social wealth loss. Therefore, improving the efficiency of transport management is one of the focuses of current academic circles. Among the research in transport management, traffic signal control (TSC) is an effective way to alleviate traffic congestion at signalized intersections. Existing works have successfully applied reinforcement learning (RL) techniques to achieve a higher TSC efficiency. However, previous work remains several challenges in RL-based TSC methods. First, existing studies used a single scaled reward to frame multiple objectives. Nevertheless, the single scaled reward has lower scalability to assess the controller's performance on different objectives, resulting in higher volatility on different traffic criteria. Second, adaptive traffic signal control provides dynamic traffic timing plans according to unforeseeable traffic conditions. Such characteristic prohibits applying the existing eco-driving strategies whose strategies are generated based on foreseeable and prefixed traffic timing plans. To address the challenges, in this thesis, we propose to design a new RL-TSC framework along with an eco-driving strategy to improve the TSC's efficiency on multiple objectives and further smooth the traffic flows. Moreover, to achieve effective management of the system-wide traffic flows, current researches tend to focus on the design of collaborative traffic signal control methods. However, the existing collaboration-based methods often ignore the impact of transmission delay for exchanging traffic flow information on the system. Inspired by the state-of-the-art max-pressure control in the traffic signal control area, we propose a new efficient RL-based cooperative TSC scheme by improving the reward and state representation based on the max-pressure control method and developing an agent that can address the data transmission delay issue by decreasing the discrepancy between the real-time and delayed traffic conditions. To evaluate the performance of our proposed work more accurately, in addition to the synthetic scenario, we also conducted an experiment based on the real-world traffic data recorded in the City of Toronto. We demonstrate that our method surpassed the performance of the previous traffic signal control methods through comprehensive experiments.
Author: Soheil Mohamad Alizadeh Shabestary
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
With perpetually increasing demand for transportation as a result of continued urbanization and population growth, it is essential to increase the existing transportation infrastructure. Optimizing traffic signals in real time, although is one of the primary tools to increase the efficiency of our urban transportation networks, is a difficult task, due to the non-linearity and stochasticity of the traffic system. Deriving a simple model of the intersection in order to design an appropriate adaptive controller is extremely challenging, and traffic signal control falls under the challenging category of sequential decision-making processes. One of the best approaches to resolving issues around adaptive traffic signal control is reinforcement learning (RL), which is model-free and suitable for sequential decision-making problems. Conventional discrete RL algorithms suffer from the curse of dimensionality, slow training, and lack of generalization. Therefore, we focus on developing continuous RL-based (CRL) traffic signal controller that addresses these issues. Also, we propose a more advanced deep RL-based (DRL) traffic signal controller that can handle high-dimensional sensory inputs from newer traffic sensors such as radars and the emerging technology of Connected Vehicles. DRL traffic signal controller directly operates with highly-detailed sensory information and eliminates the need for traffic experts to extract concise state features from the raw data (e.g., queue lengths), a process that is both case-specific and limiting. Furthermore, DRL extracts what it needs from the more detailed inputs automatically and improves control performance. Finally, we introduce two multimodal RL-based traffic signal controllers (MCRL and MiND) that simultaneously optimize the delay for both transit and regular traffic, as public transit is the more sustainable mode of transportation in busy cities and downtown cores. The proposed controllers are tested using Paramics traffic microsimulator, and the results show the superiority of both CRL and DRL over other state-of-practice and state-of-the-art traffic signal controllers. In addition to the advantages of MiND, such as its multimodal capabilities, significantly faster convergence, smaller model, and elimination of the feature extraction process, our experimental results show significant improvements in travel times for both transit and regular traffic at the intersection level compared to the base cases.
Author: Jiajie Hu
Publisher:
ISBN:
Category : Automated vehicles
Languages : en
Pages : 143
Get Book Here
Book Description
The connected vehicle is an emerging technology aimed at deploying and developing a fully connected transportation system which allows the vehicles to dynamically transmit messages between the vehicles (V2V), infrastructure (V2I), Cloud (V2C) and everything (V2X). The connected vehicles can provide an unprecedented amount of data even in the traffic network with a low market penetration rate, which can provide new solutions to transportation issues. This study focuses on micromodeling and quantitatively assessing the potential benefits of the connected vehicles on safety, mobility, and energy efficiency perspectives. In this dissertation, we proposed deep-learning based systems to solve different transportation problems under the environment of connected vehicles. The crash risk prediction system can identify crash-prone intersections and guide the deployment of safety measures to prevent potential crashes. The pothole detection system provides a cost-effective strategy to map the road conditions, which will be beneficial to road maintenance especially when municipal budgets are limited. The slippery condition surveillance system achieves real-time monitoring of pavement slippery conditions impacted by adverse weather and promotes cautious driving behaviors. The adaptive traffic signal control system provides an adaptive, efficient and optimized traffic signal control agent, which can reduce vehicle delay and emissions, improve mobility and energy efficiency. Overall, connected vehicle technology shows great potential in the field of transportation. The safety, mobility and energy efficiency will be further improved with the widespread deployment of connected vehicles and increase of market penetration rate, which is achievable in the near future.
Author: Zhengtao Ding
Publisher: Institution of Engineering and Technology
ISBN: 1849195749
Category : Technology & Engineering
Languages : en
Pages : 288
Get Book Here
Book Description
An adaptive system for linear systems with unknown parameters is a nonlinear system. The analysis of such adaptive systems requires similar techniques to analyse nonlinear systems. Therefore it is natural to treat adaptive control as a part of nonlinear control systems. Nonlinear and Adaptive Control Systems treats nonlinear control and adaptive controlin a unified framework, presenting the major results at a moderate mathematical level, suitable for MSc students and engineers with undergraduate degrees. Topics covered include introduction to nonlinear systems; state space models; describing functions forcommon nonlinear components; stability theory; feedback linearization; adaptive control; nonlinear observer design; backstepping design; disturbance rejection and output regulation; and control applications, including harmonic estimation and rejection inpower distribution systems, observer and control design for circadian rhythms, and discrete-time implementation of continuous-timenonlinear control laws.
Author: Lokesh Chandra Das
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
Traffic congestion is a serious problem in the USA that affects safety, economy, environments, and human lives. Autonomous vehicles (AVs) equipped with vehicle-to-everything (V2X) communication technology is emerging as a viable solution to mitigate traffic congestion. In this dissertation, we propose an advanced traffic control system for autonomous vehicles, utilizing machine learning techniques, to alleviate traffic congestion, and enhance traffic efficiency and safety. The proposed system consists of two key components: an intelligent adaptive cruise control system (ACC) and a cooperative lane-change system. To address the limitations of existing static model-based approaches, we introduce a novel AI-based ACC system that dynamically adjusts the ACC settings based on real-time traffic conditions. By adapting to changing situations, this system significantly improves traffic efficiency. However, we recognize that current intelligent ACC systems primarily focus on traffic flow enhancement, disregarding the influence of adaptive inter-vehicle gap adjustment on driving safety and comfort. To bridge this gap, we develop a Safety-Aware Intelligent ACC system, which effectively assesses driving safety by dynamically updating safety model parameters according to varying traffic conditions. This innovative approach ensures that driving safety and comfort are prioritized alongside traffic efficiency. Furthermore, we present a novel multi-agent reinforcement learning (MARL)-based intelligent lane-change system for autonomous vehicles. This system optimizes both local and global performance by incorporating a road-side unit (RSU) responsible for managing a specific road segment, as well as vehicle-to-everything (V2X) capabilities for the agents. This density-aware cooperative multi-agent framework enables efficient and safe lane changes, considering the overall traffic conditions and maximizing the benefits for all vehicles involved. Finally, we present a use case scenario of our proposed next-generation traffic control system by designing an intelligent adaptive motion control system for electric vehicles (EVs) which facilitates an EV to control its motion to align with the position where the electromagnetic strength is expected to be maximal to receive maximum charging efficiency. By combining the AI-based ACC system and the MARL-based intelligent lane-change system, our next-generation traffic control system for autonomous vehicles aims to revolutionize traffic management, offering improved efficiency and safety for autonomous vehicles on the roads of the future.
Author: G. Ranganathan
Publisher: Springer Nature
ISBN: 9811928401
Category : Technology & Engineering
Languages : en
Pages : 799
Get Book Here
Book Description
The book features original papers from International Conference on Pervasive Computing and Social Networking (ICPCSN 2022), organized by NSIT, Salem, India during 3 – 4 March 2022. It covers research works on conceptual, constructive, empirical, theoretical and practical implementations of pervasive computing and social networking methods for developing more novel ideas and innovations in the growing field of information and communication technologies.
Author: Thomas Leo McCluskey
Publisher: Birkhäuser
ISBN: 3319258087
Category : Computers
Languages : en
Pages : 303
Get Book Here
Book Description
The work on Autonomic Road Transport Support (ARTS) presented here aims at meeting the challenge of engineering autonomic behavior in Intelligent Transportation Systems (ITS) by fusing research from the disciplines of traffic engineering and autonomic computing. Ideas and techniques from leading edge artificial intelligence research have been adapted for ITS over the last 30 years. Examples include adaptive control embedded in real time traffic control systems, heuristic algorithms (e.g. in SAT-NAV systems), image processing and computer vision (e.g. in automated surveillance interpretation). Autonomic computing which is inspired from the biological example of the body’s autonomic nervous system is a more recent development. It allows for a more efficient management of heterogeneous distributed computing systems. In the area of computing, autonomic systems are endowed with a number of properties that are generally referred to as self-X properties, including self-configuration, self-healing, self-optimization, self-protection and more generally self-management. Some isolated examples of autonomic properties such as self-adaptation have found their way into ITS technology and have already proved beneficial. This edited volume provides a comprehensive introduction to Autonomic Road Transport Support (ARTS) and describes the development of ARTS systems. It starts out with the visions, opportunities and challenges, then presents the foundations of ARTS and the platforms and methods used and it closes with experiences from real-world applications and prototypes of emerging applications. This makes it suitable for researchers and practitioners in the fields of autonomic computing, traffic and transport management and engineering, AI, and software engineering. Graduate students will benefit from state-of-the-art description, the study of novel methods and the case studies provided.
