Enhanced Traffic Signal Operation Using Connected Vehicle Data 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 Enhanced Traffic Signal Operation Using Connected Vehicle Data PDF full book. Access full book title Enhanced Traffic Signal Operation Using Connected Vehicle Data by Ehsan Bagheri. Download full books in PDF and EPUB format.
Author: Ehsan Bagheri Publisher: ISBN: Category : Intelligent transportation systems Languages : en Pages : 168
Book Description
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: Ehsan Bagheri Publisher: ISBN: Category : Intelligent transportation systems Languages : en Pages : 168
Book Description
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: Shrikant Fulari Publisher: ISBN: Category : Adaptive control systems Languages : en Pages : 34
Book Description
For traffic signal control, Time of Day (TOD) mode of operations is widely deployed in practice for selecting a signal timing plan. However, TOD mode in not effective in adapting to variations in traffic conditions, such as special events and holidays, incidents, etc. Several research studies have reported the potential of Traffic Responsive Control operation or Traffic Responsive Plan Selection (TRPS) in reducing delays and the number of stops. For successful implementation of TRPS, accurate traffic state estimation is essential. The current study in this direction investigates a methodology for traffic state estimation for a corridor in Morgantown, WV, by using system detector data and connected vehicles (CV) data. Data from CVs form the basis to estimate queue lengths at signalized intersection approaches. While using data from multiple sources, a single measure in terms of three plan selection parameter was obtained, based on which discriminant functions were developed to classify the observations into states. Based on kmeans clustering, similar traffic states were grouped together and a new set of states were suggested in place of the original states for which up to 93% classification accuracy was obtained. Overall, it was demonstrated that queue length data can be a valuable source of information for traffic state estimation that is needed for implementing the TRPS framework.
Author: MD Abu Sufian Talukder Publisher: ISBN: Category : Electronic dissertations Languages : en Pages :
Book Description
High-quality, reliable, and robust data is key to better understanding performance and improvement needs for transportation infrastructure. Predominantly, transportation systems performance has been evaluated using infrastructure-based data, which is often limited by high costs, small sample size, and potential inaccuracy. With recent advancements in technology, previously unobtainable large high-fidelity data, such as probe data and connected vehicle (CV) data, can now be utilized to address many challenges related to transportation systems. This dissertation investigates various research and practical oriented applications for such emerging transportation data sources. The first part of this dissertation develops a novel methodology for characterizing mobility of transportation networks. Using probe vehicle travel times, a route-based travel time reliability metric is proposed for assessing and comparing transportation system's performance from one geographic area to another. The second part of this dissertation uses CV-technology to develop methodology for improving operational efficiency at a signalized intersection. Two innovative traffic signal control algorithms are established to demonstrate real-time delay optimization for both connected and non-connected vehicles. The third part of this dissertation extends the use of CV-technology to facilitate prioritized freight movement in a signalized corridor. An estimated time of arrival (ETA)-based priority logic is developed, and the proposed priority system is deployed along US-82 in Northport and Tuscaloosa, Alabama. Finally, this dissertation explores the application of emerging transportation data collection technologies to characterize and evaluate transportation systems performance. The techniques presented in this dissertation will be helpful to transportation agencies, planners, and practitioners to assess existing performance and need for future transportation infrastructure.
Author: Norris Novat Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
Traffic incidents are unforeseen events known to affect traffic flow because they reduce the capacity of an arterial corridor segment and normally generate a temporary bottleneck. Identification of retiming requirements to enhance traffic signal operations when an incident occurs depends on operations-oriented traffic signal performance measurements when effective and real-time traffic signal performance metrics are employed at traffic control centers, delays, fuel use, and air pollution may all be decreased. The majority of currently available traffic signal performance evaluations are based on high-resolution traffic signal controller event data, which gives data on an intersection-by-intersection basis but requires a substantial upfront expenditure. The necessary detecting and communication equipment also involves costly and periodic maintenance. Additionally, the full manifestation of connected vehicles (CVs) is fast approaching with efforts in place to accelerate the adaptation of CVs and their infrastructures. CV technologies have enormous potential to improve traffic mobility and safety. CVs can provide abundant traffic data that is not otherwise captured by roadway detectors or other methods of traffic data collection. Since the observation is independent of any space restrictions and not impacted by queue discharge and buildup, CV data offers more comprehensive and reliable data that can be used to estimate various traffic signal performance measures. This thesis proposes a conceptual CV simulation framework intended to ascertain the effectiveness of CV trajectory-based measures in characterizing an arterial corridor incident, such as a vehicle crash. Using a four-intersection corridor with vii different signal timing plans, a microscopic simulation model was created in Simulation of Urban Mobility (SUMO), Vehicles in Network Simulation (Veins) and Objective Modular Network Testbed in C++ (OMNeT++) platforms. Furthermore, an algorithm for CVs that defines, detects and disseminates a vehicle crash incident to other vehicles and a roadside unit (RSU) was developed. In the thesis, it is demonstrated how visual performance metrics with CV data may be used to identify an incident. This thesis proposes that traffic signals performance metrics, such as progression quality, split failure, platoon ratios, and safety surrogate measures (SSMs), may be generated using CV trajectory data. The results show that the recommended approaches with access to CV trajectory data would help both performance assessment and operation of traffic control systems. Unlike the current state of the practice (fixed detection technology), the developed conceptual framework can detect incidents that intersection-vicinity-limited does not capture detectors while requiring immediate attention.
Author: Lawrence A. Klein Publisher: CRC Press ISBN: 1351800973 Category : Technology & Engineering Languages : en Pages : 574
Book Description
An intelligent transportation system (ITS) offers considerable opportunities for increasing the safety, efficiency, and predictability of traffic flow and reducing vehicle emissions. Sensors (or detectors) enable the effective gathering of arterial and controlled-access highway information in support of automatic incident detection, active transportation and demand management, traffic-adaptive signal control, and ramp and freeway metering and dispatching of emergency response providers. As traffic flow sensors are integrated with big data sources such as connected and cooperative vehicles, and cell phones and other Bluetooth-enabled devices, more accurate and timely traffic flow information can be obtained. The book examines the roles of traffic management centers that serve cities, counties, and other regions, and the collocation issues that ensue when multiple agencies share the same space. It describes sensor applications and data requirements for several ITS strategies; sensor technologies; sensor installation, initialization, and field-testing procedures; and alternate sources of traffic flow data. The book addresses concerns related to the introduction of automated and connected vehicles, and the benefits that systems engineering and national ITS architectures in the US, Europe, Japan, and elsewhere bring to ITS. Sensor and data fusion benefits to traffic management are described, while the Bayesian and Dempster–Shafer approaches to data fusion are discussed in more detail. ITS Sensors and Architectures for Traffic Management and Connected Vehicles suits the needs of personnel in transportation institutes and highway agencies, and students in undergraduate or graduate transportation engineering courses.
Author: Zhitong Huang Publisher: ISBN: Category : Languages : en Pages : 173
Book Description
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 :
Book Description
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: Amit Kumar Tyagi Publisher: John Wiley & Sons ISBN: 1394303580 Category : Computers Languages : en Pages : 628
Book Description
An essential book on the applications of AI and digital twin technology in the smart manufacturing sector. In the rapidly evolving landscape of modern manufacturing, the integration of cutting-edge technologies has become imperative for businesses to remain competitive and adaptive. Among these technologies, Artificial Intelligence (AI) stands out as a transformative force, revolutionizing traditional manufacturing processes and making the way for the era of smart manufacturing. At the heart of this technological revolution lies the concept of the Digital Twin—an innovative approach that bridges the physical and digital realms of manufacturing. By creating a virtual representation of physical assets, processes, and systems, organizations can gain unprecedented insights, optimize operations, and enhance decision-making capabilities. This timely book explores the convergence of AI and Digital Twin technologies to empower smart manufacturing initiatives. Through a comprehensive examination of principles, methodologies, and practical applications, it explains the transformative potential of AI-enabled Digital Twins across various facets of the manufacturing lifecycle. From design and prototyping to production and maintenance, AI-enabled Digital Twins offer multifaceted advantages that redefine traditional paradigms. By leveraging AI algorithms for data analysis, predictive modeling, and autonomous optimization, manufacturers can achieve unparalleled levels of efficiency, quality, and agility. This book explains how AI enhances the capabilities of Digital Twins by creating a powerful tool that can optimize production processes, improve product quality, and streamline operations. Note that the Digital Twin in this context is a virtual representation of a physical manufacturing system, including machines, processes, and products. It continuously collects real-time data from sensors and other sources, allowing it to mirror the physical system’s behavior and performance. What sets this Digital Twin apart is the incorporation of AI algorithms and machine learning techniques that enable it to analyze and predict outcomes, recommend improvements, and autonomously make adjustments to enhance manufacturing efficiency. This book outlines essential elements, like real-time monitoring of machines, predictive analytics of machines and data, optimization of the resources, quality control of the product, resource management, decision support (timely or quickly accurate decisions). Moreover, this book elucidates the symbiotic relationship between AI and Digital Twins, highlighting how AI augments the capabilities of Digital Twins by infusing them with intelligence, adaptability, and autonomy. Hence, this book promises to enhance competitiveness, reduce operational costs, and facilitate innovation in the manufacturing industry. By harnessing AI’s capabilities in conjunction with Digital Twins, manufacturers can achieve a more agile and responsive production environment, ultimately driving the evolution of smart factories and Industry 4.0/5.0. Audience This book has a wide audience in computer science, artificial intelligence, and manufacturing engineering, as well as engineers in a variety of industrial manufacturing industries. It will also appeal to economists and policymakers working on the circular economy, clean tech investors, industrial decision-makers, and environmental professionals.
Author: Ehsan Bagheri
Author: Ehsan Bagheri
Author: Shrikant Fulari
Author: MD Abu Sufian Talukder
Author: Norris Novat
Author: Lawrence A. Klein
Author: Zhitong Huang
Author: Zhibin Li
Author: Xiao Liang
Author: 
Author: Amit Kumar Tyagi