Connected Vehicle Based Traffic Signal Optimization PDF Download

Author: Xuegang Ban
Publisher:
ISBN:
Category : Electronic traffic controls
Languages : en
Pages : 47

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Author: Wan Li
Publisher:
ISBN:
Category : Intelligent transportation systems
Languages : en
Pages : 145

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The advent and deployment of Connected vehicle (CV) and Vehicle-to-everything (V2X) communications offer the potential to significantly improve the efficiency of traffic signal control systems. The knowledge of vehicle trajectories in the network allows for optimal signal setting and significant improvements in network performance compared to existing traffic signal control systems. This research aims to develop a framework, including modeling techniques, algorithms, and testing strategies, for urban traffic signal optimization with CVs. The objective is to improve the safety, mobility, and sustainability of all vehicles in the study areas utilizing CV data, i.e., real time information on vehicles' locations and speeds, as well as communications to the signal control systems. The proposed framework is able to optimize traffic signal timing for a single intersection and along a corridor under various market penetration of CVs. Under full penetration rate of CVs, the signal timing optimization and coordination problems are first formulated in a centralized scheme as a mixed-integer nonlinear programing (MINLP). Due to the complexity of the model, the problem is decomposed into two levels: an intersection level to optimize phase durations using dynamic programing (DP) and a corridor level to optimize the offsets of all intersections. Under medium-to-high penetration rates of CVs, Kalman filter methods are applied to estimate trajectories of unequipped vehicles given the available trajectories of CVs. The estimated trajectories combined with CV trajectories are utilized in the trajectory-based signal timing optimization process. Under relatively low penetration rates of CVs, a Deep Intersection Spatial Temporal Network (DISTN) is developed to predict short-term movement-based traffic volumes. The predicted volumes are used in a volume-based adaptive signal control method to calculate signal timing parameters. Comprehensive testing and validation of the proposed methods are conducted in traffic simulation and with real world CV (probe vehicle) data. The testing tasks aim to validate that the developed methods are computationally manageable and have the potential to be implemented in CV-based traffic signal applications in the real world.

Author: Ehsan Bagheri
Publisher:
ISBN:
Category : Intelligent transportation systems
Languages : en
Pages : 168

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As traffic on urban road network increases, congestion and delays are becoming more severe. At grade intersections form capacity bottlenecks in urban road networks because at these locations, capacity must be shared by competing traffic movements. Traffic signals are the most common method by which the right of way is dynamically allocated to conflicting movements. A range of traffic signal control strategies exist including fixed time control, actuated control, and adaptive traffic signal control (ATSC). ATSC relies on traffic sensors to estimate inputs such as traffic demands, queue lengths, etc. and then dynamically adjusts signal timings with the objective to minimize delays and stops at the intersection. Despite, the advantages of these ATSC systems, one of the barriers limiting greater use of these systems is the large number of traffic sensors required to provide the essential information for their signal timing optimization methodologies. A recently introduced technology called connected vehicles will make vehicles capable of providing detailed information such as their position, speed, acceleration rate, etc. in real-time using a wireless technology. The deployment of connected vehicle technology would provide the opportunity to introduce new traffic control strategies or to enhance the existing one. Some work has been done to-date to develop new ATSC systems on the basis of the data provided by connected vehicles which are mainly designed on the assumption that all vehicles on the network are equipped with the connected vehicle technology. The goals of such systems are to: 1) provide better performance at signalized intersections using enhanced algorithms based on richer data provided by the connected vehicles; and 2) reduce (or eliminate) the need for fixed point detectors/sensors in order to reduce deployment and maintenance costs. However, no work has been done to investigate how connected vehicle data can improve the performance of ATSC systems that are currently deployed and that operate using data from traditional detectors. Moreover, achieving a 100% market penetration of connected vehicles may take more than 30 years (even if the technology is mandated on new vehicles). Therefore, it is necessary to provide a solution that is capable of improving the performance of signalized intersections during this transition period using connected vehicle data even at low market penetration rates. This research examines the use of connected vehicle data as the only data source at different market penetration rates aiming to provide the required inputs for conventional adaptive signal control systems. The thesis proposes various methodologies to: 1) estimate queues at signalized intersections; 2) dynamically estimate the saturation flow rate required for optimizing the timings of traffic signals at intersections; and 3) estimate the free flow speed on arterials for the purpose of optimizing offsets between traffic signals. This thesis has resulted in the following findings: 1. Connected vehicle data can be used to estimate the queue length at signalized intersections especially for the purpose of estimating the saturation flow rate. The vehicles' length information provided by connected vehicles can be used to enhance the queue estimation when the traffic composition changes on a network. 2. The proposed methodology for estimating the saturation flow rate is able to estimate temporally varying saturation flow rates in response to changing network conditions, including lane blockages and queue spillback that limit discharge rates, and do so with an acceptable range of errors even at low level of market penetration of connected vehicles. The evaluation of the method for a range of traffic Level of Service (LOS) shows that the maximum observed mean absolute relative error (6.2%) occurs at LOS F and when only 10% of vehicles in the traffic stream are connected vehicles. 3. The proposed method for estimating the Free Flow Speed (FFS) on arterial roads can provide estimations close to the known ground truth and can respond to changes in the FFS. The results also show that the maximum absolute error of approximately 4.7 km/h in the estimated FFS was observed at 10% market penetration rate of connected vehicles. 4. The results of an evaluation of an adaptive signal control system based on connected vehicle data in a microsimulation environment show that the adaptive signal control system is able to adjust timings of signals at intersections in response to changes in the saturation flow rate and free flow speed estimated from connected vehicle data using the proposed methodologies. The comparison of the adaptive signal control system against a fixed time control at 20% and 100% CV market penetration rates shows improvements in average vehicular delay and average number of stops at both market penetration rates and though improvements are larger for 100% CV LMP, approximately 70% of these improvements are achieved at 20% CV LMP.

Author: Zhitong Huang
Publisher:
ISBN:
Category :
Languages : en
Pages : 173

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The dissertation presents a connected vehicle based traffic signal control model (CVTSCM) for signalized arterials. The model addresses different levels of traffic congestion starting with the initial deployment of connected vehicle technologies focusing on two modules created in CVTSCM. For near/under-saturated intersections, an arterial-level traffic progression optimization model (ALTPOM) is being proposed. ALTPOM improves traffic progression by optimizing offsets for an entire signalized arterial simultaneously. To optimize these offsets, splits of coordinated intersections are first adjusted to balance predicted upcoming demands of all approaches at individual intersections. An open source traffic simulator was selected to implement and evaluate the performance of ALTPOM. The case studies’ field signal timing plans were coordinated and optimized using TRANSYT-7F as the benchmark. ALTPOM was implemented with connected vehicles penetration rates at 25% and 50%, ALTPOM significantly outperforms TRANSYT-7F with at least 26.0% reduction of control delay (sec/vehicle) and a 4.4% increase of throughput for both directions of major and minor streets. This technique differs from traditional traffic coordination which prioritizes major street traffic, and thereby generally results in degrading performance on minor streets. ALTPOM also provides smooth traffic progression for the coordinated direction with little impact on the opposite direction. The performance of ALTPOM improves as the penetration rate of connected vehicles increases. For saturated/oversaturated conditions, two queue length management based Active Traffic Management (ATM) strategies are proposed, analytically investigated, and experimentally validated. The first strategy distributes as much green time as possible for approaches with higher saturation discharge rate in order to reduce delay. For the second approach, green times are allocated to balance queue lengths of major and minor streets preventing queue spillback or gridlock. Both strategies were formulated initially using uniform arrival and departure, and then validated using field vehicle trajectory data. After validation of the modules, the effectiveness of CVTSCM is proven. Then, conclusions and recommendations for future researches are presented at the end.

Author: Xiao Liang
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Traffic signals help to maintain order in urban traffic networks and reduce vehicle conflicts by dynamically assigning right-of-way to different vehicle movements. However, by temporarily stopping vehicle movements at regular intervals, traffic signals are a major source of urban congestion and cause increased vehicle delay, fuel consumption, and environmental pollution. Connected and Autonomous Vehicle technology may be utilized to optimize traffic operations at signalized intersections, since connected vehicles have the ability to communicate with the surrounding infrastructure and autonomous vehicles can follow the instructions from the signal or a central control system. Connected vehicle information received by a signal controller can be used to help adjust signal timings to tailor to the specific dynamic vehicle demand. Information about the signal timing plan can then be communicated back to the vehicles so that they can adjust their speeds/trajectories to further improve traffic operations. Based on a thorough literature review of existing studies in the area of signal control utilizing information from connected and autonomous vehicles, three research gaps are found: 1) application are limited to unrealistic intersection configurations; 2) methods are limited to a single mode; or, 3) methods only optimize the average value of measure of effectiveness while ignoring the distribution among vehicles. As a part of this dissertation, several methods will be proposed to increase computational efficiency of an existing CAV-based joint signal timing and vehicle trajectory optimization algorithm so that it can be applied to more realistic intersection settings without adding computational burden. Doing so requires the creation of new methods to accommodate features like multiple lanes on each approach, more than two approaches and turning maneuvers. Methods to incorporate human-driven cooperative vehicles and pedestrians are also proposed and tested. A more equitable traffic signal control method is also designed.

Author: Lei Zhang
Publisher:
ISBN:
Category :
Languages : en
Pages : 172

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This dissertation discusses network wide signal control strategies base on connected vehicle technology. Traffic congestion on arterials has become one of the largest threats to economic competitiveness, livability, safety, and long-term environmental sustainability in the United States. In addition, arterials usually experience more blockage than freeways, specifically in terms of intersection congestion. There is no doubt that emerging technologies provide unequaled opportunities to revolutionize “retiming” and mitigate traffic congestion. Connected vehicle technology provides unparalleled safety benefits and holds promise in terms of alleviating both traffic congestion and the environmental impacts of future transportation systems. The objective of this research is to improve the mobility, safety and environmental effects at signalized arterials with connected vehicles. The proposed solution of this dissertation is to formulate traffic signal control models for signalized arterials based on connected vehicle technology. The models optimize offset, split, and cycle length to minimize total queue delay in all directions of coordinated intersections. Then, the models are implemented in a centralized system—including closed-loop systems—first, before expanding the results to distributed systems. The benefits of the models are realized at the infant stage of connected vehicle deployment when the penetration rate of connected vehicles is around 10%. Furthermore, the benefits incentivize the growth of the penetration rate for drivers. In addition, this dissertation contains a performance evaluation in traffic delay, volume throughput, fuel consumption, emission, and safety by providing a case study of coordinated signalized intersections. The case study results show the solution of this dissertation could adapt early deployment of connected vehicle technology and apply to future connected vehicle technology development.

Author: Can Zhang
Publisher:
ISBN:
Category : Edge computing
Languages : en
Pages : 106

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This thesis develops and analyzes centralized and decentralized network-level traffic signal control system under in a connected vehicle (CV) environment with mobile edge computing (MEC). The goal is to provide a framework of decentralized signal control (DSC) system especially for real-time control and large-scale traffic network. Short-term origin-destination (OD) demand is used as an input given that the technological paradigm assumed is within the CV environment, unlike most previous works that look at network control but in a current technological paradigm. Considering short-term OD demand as inputs, a queue-based dynamic traffic assignment (DTA) model is proposed to predict traffic dynamics in traffic networks with signal control. Although DTA has been an effective tool to describe traffic dynamics for traffic optimization, and many researchers have considered traffic signal control in their models, signal timings have been simplified without considering complex, but realistic, phase sequence and duration restrictions. This work formulates traffic signal timing as a component of the link performance function with three control variables: cycle length, phase split, and offset. In addition, both user-optimal (UO) and system-optimal (SO) DTA problems are solved within a single corridor network. Finally, this thesis provides a simulation-based framework of both centralized and decentralized signal control to solve the network-level traffic signal control optimization problem. For the centralized system, this work solves the issue of optimal control using a three-step naïve method. Because the optimization of large-scale network traffic signals is a Nondeterministic Polynomial Time (NP)-complete problem, the centralized system is further decomposed into a decentralized system where the network is divided into subnetworks. - Each subnetwork has its own agent that optimizes signals within the subnetwork. The proposed control systems are applied to a set of test scenarios constructed using different demand levels in different grid networks. This work also investigates the impact of network decomposition strategy on the signal control system performance. Results show that network decomposition with smaller subnetworks results in less Computational Time (CT), but also increased Average Travel Time (ATT) and Total Travel Delay (TTD). This thesis contributes to the literature by a queue-based DTA model for traffic network with real traffic signal timing plan, a simulation-based framework of DSC system within the MEC-enabled CV environment, and a scalable and extendable decomposition method for a DSC system.

Author:
Publisher:
ISBN:
Category : Electronic book
Languages : en
Pages : 0

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"TRB's Transportation Research Record: Journal of the Transportation Research Board, No. 2356 contains 14 papers that review an intelligent dilemma-zone protection system for a high-speed rural intersection; adaptive signal control in Germany; queue length under connected vehicle technology; a coordinated optimization model for signal timings of full continuous flow intersections; and, transit priority strategies for multiple routes under headway-based operations. This TRR also explores multiregime adaptive signal control for congested urban roadway networks; metered entry volume on an oversaturated network with dynamic signal timing; arterial queue spillback detection and signal control based on connected vehicle technology; a coordinated optimization model for transit priority control under arterial progression; and, a dynamic programming approach for arterial signal optimization. In addition, this issue examines self-organizing control logic for oversaturated arterials; microwave radar vehicle detectors at a signalized intersection under adverse weather conditions; a performance diagnosis tool for arterial traffic signals; and, a statistical study of the impact of adaptive traffic signal control on traffic and transit performance."--Online abstract.

Author: Satish V. Ukkusuri
Publisher: Springer Science & Business Media
ISBN: 1461462436
Category : Business & Economics
Languages : en
Pages : 322

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Book Description
This edited book focuses on recent developments in Dynamic Network Modeling, including aspects of route guidance and traffic control as they relate to transportation systems and other complex infrastructure networks. Dynamic Network Modeling is generally understood to be the mathematical modeling of time-varying vehicular flows on networks in a fashion that is consistent with established traffic flow theory and travel demand theory. Dynamic Network Modeling as a field has grown over the last thirty years, with contributions from various scholars all over the field. The basic problem which many scholars in this area have focused on is related to the analysis and prediction of traffic flows satisfying notions of equilibrium when flows are changing over time. In addition, recent research has also focused on integrating dynamic equilibrium with traffic control and other mechanism designs such as congestion pricing and network design. Recently, advances in sensor deployment, availability of GPS-enabled vehicular data and social media data have rapidly contributed to better understanding and estimating the traffic network states and have contributed to new research problems which advance previous models in dynamic modeling. A recent National Science Foundation workshop on “Dynamic Route Guidance and Traffic Control” was organized in June 2010 at Rutgers University by Prof. Kaan Ozbay, Prof. Satish Ukkusuri , Prof. Hani Nassif, and Professor Pushkin Kachroo. This workshop brought together experts in this area from universities, industry and federal/state agencies to present recent findings in this area. Various topics were presented at the workshop including dynamic traffic assignment, traffic flow modeling, network control, complex systems, mobile sensor deployment, intelligent traffic systems and data collection issues. This book is motivated by the research presented at this workshop and the discussions that followed.

Author: U.s. Department of Transportation
Publisher: CreateSpace
ISBN: 9781508557173
Category : Transportation
Languages : en
Pages : 286

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This report serves as a comprehensive guide to traffic signal timing and documents the tasks completed in association with its development. The focus of this document is on traffic signal control principles, practices, and procedures. It describes the relationship between traffic signal timing and transportation policy and addresses maintenance and operations of traffic signals. It represents a synthesis of traffic signal timing concepts and their application and focuses on the use of detection, related timing parameters, and resulting effects to users at the intersection. It discusses advanced topics briefly to raise awareness related to their use and application. The purpose of the Signal Timing Manual is to provide direction and guidance to managers, supervisors, and practitioners based on sound practice to proactively and comprehensively improve signal timing. The outcome of properly training staff and proactively operating and maintaining traffic signals is signal timing that reduces congestion and fuel consumption ultimately improving our quality of life and the air we breathe. This manual provides an easy-to-use concise, practical and modular guide on signal timing. The elements of signal timing from policy and funding considerations to timing plan development, assessment, and maintenance are covered in the manual. The manual is the culmination of research into practices across North America and serves as a reference for a range of practitioners, from those involved in the day to day management, operation and maintenance of traffic signals to those that plan, design, operate and maintain these systems.