Advanced Traffic Signal Control Algorithms Phase II PDF Download

Author: Wei-Bin Zhang
Publisher:
ISBN:
Category : Adaptive control systems
Languages : en
Pages : 93

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Author: Alexander Skabardonis
Publisher:
ISBN:
Category : Adaptive control systems
Languages : en
Pages : 130

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Author: Andreas F. Molisch
Publisher:
ISBN:
Category : Adaptive control systems
Languages : en
Pages : 69

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Author: Jonathan Corey
Publisher:
ISBN:
Category :
Languages : en
Pages : 192

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Book Description
With over 300,000 traffic signals in the United States, it is important to everyone that those traffic signals operate optimally. Unfortunately, according to the Institute of Transportation Engineers over 75% of traffic signal control systems are in need of retiming or upgrade. Agencies and practitioners responsible for these signals face significant budgeting and procedural challenges to maintain and upgrade their systems. Transportation professionals have traditionally lacked accessible and effective tools to identify when and where the greatest benefits may be generated through retiming and system feature selection. They have also lacked methods and tools to identify, select and defend choices of new traffic signal control systems. This is especially true for adaptive traffic signal control systems which are generally more expensive and whose adaptive algorithms are proprietary, invalidating many traditional analysis methods. To address these challenges, a new theoretical framework including queuing and traffic signal control models has been developed in this study to predict the impacts of signal control technology on a given corridor. This framework has been implemented in the STAR Lab Toolkit for Analysis of Traffic and Intersection Control Systems (STATICS) that uses an underlying queuing model interacting with simulated traffic signal control logic to develop traffic measures of effectiveness under different traffic signal control strategies and settings. The STATICS toolkit has been employed by the Oregon Department of Transportation and several other transportation agencies to analyze their corridors and select advanced traffic signal control systems. Furthermore, a new cost-effective adaptive traffic signal control system called the Swarm-Intelligence Based Adaptive Signal System (SIBASS) is proposed to address situations where optimum optimization strategies change with traffic conditions. Compared to the existing adaptive signal control systems, SIBASS carries an important advantage that makes it robust under communication difficulties. It operates at the individual intersection level in a flat hierarchy that does not use a central controller. Instead, each intersection self-assigns a role based on current traffic conditions and the current roles of neighboring intersections. Each role uses different optimization goals, allowing SIBASS to change intersection optimization criteria based on the current role chosen by that intersection. By designing cooperative features into SIBASS it is possible to create corridor coordination and optimization. This is accomplished using the characteristics of the swarm rather than external imposition to create order. SIBASS is evaluated via simulation under varied traffic conditions. SIBASS consistently outperformed the existing systems tested in this study. On average, SIBASS reduced system average per vehicle delay by approximately 3.5 seconds and system average queue lengths by 20 feet in the tested scenarios. New approaches to tailoring traffic signal control optimization strategies to current traffic conditions and desired operational goals are enabled by SIBASS. Combined, STATICS and SIBASS offer a solid basis upon which to build future tools and methods to analyze traffic signal control systems. Future STATICS analytical modules may include estimating environmental performance and costs as well as improvements to pedestrian modeling and mobility analysis. Environmental and pedestrian considerations also present opportunities for improvement of SIBASS. New optimization roles can be created for SIBASS to address environmental and pedestrian optimization issues.

Author: J. L. Kay
Publisher:
ISBN:
Category : Traffic flow
Languages : en
Pages : 56

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Author: J. L. Kay
Publisher:
ISBN:
Category : Traffic flow
Languages : en
Pages : 64

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This report is a handbook for locating detectors for advanced traffic control strategies. A discussion of criteria is presented and procedures for locating detectors to provide required surveillance data are described. The procedures relate to locating detectors at critical intersections, assessing which link in the network requires detectorization, and locating detectors within the link. Both latitudinal and longitudinal placement within the link are discussed. This handbook is a supplement to Final Report (FHWA No. 75-92) for the detector locating project.

Author: Byungkyu Park
Publisher:
ISBN:
Category : Stochastic programming
Languages : en
Pages : 42

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Book Description
Traffic congestion has greatly affected not only the nation's economy and environment but also every citizen's quality of life. A recent study shows that every American traveler spent an extra 38 hours and 26 gallons of fuel per year due to traffic congestion during the peak period. Of this congestion, 10% is attributable to improper operations of traffic signals. Surprisingly, more than a half of all signalized intersections in the United States needs to be re-optimized immediately to maintain peak efficiency. Even though many traffic signal control systems have been upgraded from pre-timed controllers to actuated and adaptive controllers, the traffic signal optimization software has not been kept current. For example, existing commercial traffic signal timing optimization programs including SYNCHRO and TRANSYT-7F do not optimize advanced controller settings available in the modern traffic controllers including minimum green time, extension time, and detector settings. This is in part because existing programs are based on macroscopic simulation tools that do not explicitly consider individual vehicular movements. To overcome such a shortcoming, a stochastic optimization method (SOM) was proposed and successfully applied to a signalized corridor in Northern Virginia. This study presents enhancements made in the SOM and case study results from an arterial network consisting of 16 signalized intersections. The proposed method employs a distributed computing environment (DCE) for faster computation time and uses a shuffled frog-leaping algorithm (SFLA) for better optimization. The case study results showed that the proposed enhanced SOM method, called SFLASOM, improved the total network travel times over field settings by 3.5% for Mid-Day and 2.1% for PM-Peak. In addition, corridor travel times were improved by 2.3% to 17.9% over field settings. However, when the new SOM timing plan was compared to the new field timing plan implemented in July 2008, the improvements were marginal, showing slightly over 2% reductions in individual vehicular delay.

Author: Zahrah Amini
Publisher:
ISBN:
Category : Adaptive control systems
Languages : en
Pages : 37

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Author: Ruimin Li
Publisher:
ISBN: 9781839530265
Category : Electronic books
Languages : en
Pages : 327

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Author: Guoyuan Wu
Publisher:
ISBN:
Category :
Languages : en
Pages : 284

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Book Description
With the growth of population and increase of travelling requirements in metropolitan areas, public transit has been recognized as a promising remedy and is playing an ever more important role in sustainable transportation systems. However, the development of the public transit system has not received enough attention until the recent emergence of Bus Rapid Transit (BRT). In the conventional public transit system, little to no communication passes between transit vehicles and the roadside infrastructure, such as traffic signals and loop detectors. But now, thanks to advancements in automatic vehicle location (AVL) systems and wireless communication, real-time and high-resolution information of the movement of transit vehicles has become available, which may potentially facilitate the development of more advanced traffic control and management systems. This dissertation introduces a novel adaptive traffic signal control system, which utilizes the real-time location information of transit vehicles. By predicting the movement of the transit vehicle based on continuous detection of the vehicle motion by the on-board AVL system and estimating the measures of effectiveness (MOE) of other motor vehicles based on the surveillance of traffic conditions, optimal signal timings can be obtained by solving the proposed traffic signal optimization models. Both numerical analysis and simulation tests demonstrate that the proposed system improves a transit vehicle's operation as well as minimizes its negative impacts on other motor vehicles in the traffic system. In summary, there are three major contributions of this dissertation: a) development of a novel AVL-based adaptive traffic signal control system; b) modeling of the associated traffic signal timing optimization problem, which is the key component of the proposed system; c) applications of the proposed system to two real world cases. After presenting background knowledge on two major types of transit operations, i.e., preemption and priority, traffic signal control and AVL systems, the architecture of the proposed adaptive signal control system and the associated algorithm are presented. The proposed system includes a data-base, fleet equipped with surveillance system, traffic signal controllers, a transit movement predictor, a traffic signal timing optimizer and a request server. The mixed integer quadratic programming (MIQP) and nonlinear programming (NP) are used to formulate signal timing optimization problems. Then the proposed system and algorithm are applied to two real-world case studies. The first case study concerns the SPRINTER rail transit service. The proposed adaptive signal control (ASC) system is developed to relieve the traffic congestion and to clear the accumulated vehicle queues at the isolated signal around the grade crossing, based on the location information on SPRINTER from PATH-developed cellular GPS trackers. The second case study involves the San Diego trolley system. With the information provided by the AVL system, the proposed ASC system predicts the arrival times of the instrumented trolley at signals and provides the corresponding optimal signal timings to improve the schedule adherence, thus reducing the delays at intersections and enhancing the trip reliability for the trolley travelling along a signalized corridor in the downtown area under the priority operation. The negative impact (e.g., delay increase) on other traffic is minimized simultaneously. Both numerical analysis and simulation tests in the microscopic environment are conducted using the PARAMICS software to validate the proposed system for the aforementioned applications. The results present a promising future for further field operational testing.