Author: Lars SjöstedtPublisher:
ISBN:
Category :
Languages : sv
Pages : 52
Book Description
A framework for a traffic signal control system in a city
Author: Lars SjöstedtSmart City
Author: James HardyA Two Stage Interval-valued Neutrosophic Soft Set Traffic Signal Control Model for Four Way Isolated signalized Intersections
Author: Endalkachew Teshome AyelePublisher: Infinite Study
ISBN:
Category : Mathematics
Languages : en
Pages : 32
Book Description
One of the major problems of both developed and developing countries is traffic congestion in urban road transportation systems. Some of the adverse consequences of traffic congestion are loss of productive time, delay in transportation,increase in transportation cost,excess fuel consumption, safety of people,increase in air pollution level and disruption of day-to-day activities. Researches have shown that among others, traditional traffic control system is one of the main reasons for traffic congestion at traffic junctions. Most countries through out the world use pre-timed / fixed cycle time traffic control systems. But these traffic control systems do not give an optimal signal time setting as they do not take into account the time dependent heavy traffic conditions at the junctions. They merely use a predetermined sequence or order for both signal phase change and time setting. Some times this also leads to more congestion at the junctions. As an improvement of fixed time traffic control method, fuzzy logic traffic control model was developed which takes into account the current traffic conditions at the junctions and works based on fuzzy logic principle under imprecise and uncertain conditions. But as a real life situation,in addition to uncertainty and impreciseness there is also indeterminacy in traffic signal control constraints which fuzzy logic can not handle. The aim of this research is to develop a new traffic signal control model that can solve the limitations of fixed time signal control and fuzzy logic signal control using a flexible approach based on interval-valued neutrosophic soft set and its decision making technique, specially developed for this purpose.We have developed an algorithm for controlling both phase change and green time extension / termination as warranted by the traffic conditions prevailing at any time.
Reinforcement Learning-Based Traffic Signal Control for Signalized Intersections
Author: Dunhao ZhongPublisher:
ISBN:
Category :
Languages : en
Pages :
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.
Digital-computer-controlled Traffic Signal System for a Small City
Author: Morton I. WeinbergPublisher:
ISBN:
Category : Computers
Languages : en
Pages : 102
Book Description
Research was conducted to apply state-of-the-art computer technology to traffic signal control, with emphasis being placed on applications for the small or medium-size city. The most comprehensive analysis of control logic to date coupled with a synthesis of the hardware required for a real-time closed-loop digital-computer-controlled traffic signal system is presented. Utilizing the city of White Plains, N.Y. as a model, a typical computer signal system is specified, equipment is selected, and costs are estimated for its 116 signalized intersections.
Global Practices on Road Traffic Signal Control
Author: Keshuang TangPublisher: Elsevier
ISBN: 0128153032
Category : Law
Languages : en
Pages : 345
Book Description
Global Practices on Road Traffic Signal Control is a valuable reference on the current state-of-the-art of road traffic signal control around the world. The book provides a detailed description of the common principles of road traffic signal control using a well-defined and consistent format that examines their application in countries and regions across the globe. This important resource considers the differences and special considerations across countries, providing useful insights into selecting control strategies for signal timing at intersections and pedestrian crosswalks. The book's authors also include success stories for coping with increasing traffic-related problems, examining both constraints and the reasons behind them. Presents a comprehensive reference on country-by-country practices on road traffic signal control Compiles and compares approaches across countries Covers theories and common principles Examines the most current systems and their implementation
Traffic Signal Control Equipment
Author: Peter J. YauchPublisher: Transportation Research Board
ISBN: 9780309049177
Category : Electronic traffic controls
Languages : en
Pages : 60
Book Description
This synthesis will be of interest to traffic engineers and others interested in the capabilities of currently available equipment for traffic signal control. Information is provided on functions and operations of controller assemblies, displays, detectors, communications, and computerized system masters. Traffic engineers need to know the functional capabilities of the various types of signal control equipment in order to select appropriate equipment for a specific application. This report of the Transportation Research Board describes the functions of each type of equipment and how it works, and gives advantages, disadvantages, and limitations.
Traffic Control Systems Handbook
Author: Adaptive Traffic Signal Control System (ACS-LITE) for Wolf Road, New York
Author: Xuegang BanPublisher:
ISBN:
Category : Traffic engineering
Languages : en
Pages : 81
Book Description
Adaptive Control Software Lite (ACS-Lite) is a traffic signal timing optimization system that dynamically adjusts traffic signal timings to meet current traffic demands. The purpose of this research project was to deploy and evaluate the ACS-Lite adaptive traffic control system on a congested urban corridor in New York State (NYS). In this case, the Wolf Road Corridor in Albany, New York, was chosen. The primary goal was to document the experiences and key lessons learned from the deployment and evaluation regarding how an adaptive control system can be deployed, the advantages and disadvantages of the system, and whether it is suitable for use in other corridors in NYS. The results of the project showed that for heavily congested corridors adaptive control can improve flow within its own system, but may cause extra delays at the boundaries where there are interactions with other traffic control systems. Therefore, a more comprehensive control/management framework may be needed in some cases. The specific ACS-Lite software also needed to be upgraded and improved in order to work for the selected corridor, which caused delays to this project.
Data-driven Adaptive Traffic Signal Control Via Deep Reinforcement Learning
Author: Tian TanPublisher:
ISBN:
Category :
Languages : en
Pages :
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.