Adaptive Traffic Signal Optimization Using Bluetooth Data

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Adaptive Traffic Signal Optimization Using Bluetooth Data

Author : Amir Zarinbal Masouleh
Publisher :
ISBN 13 :
Total Pages : 118 pages
Book Rating : 4.:/5 (18 download)

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Book Synopsis Adaptive Traffic Signal Optimization Using Bluetooth Data by : Amir Zarinbal Masouleh

Download or read book Adaptive Traffic Signal Optimization Using Bluetooth Data written by Amir Zarinbal Masouleh and published by . This book was released on 2017 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt: Traffic congestion continues to increase in urban centers causing significant economic, environmental, and social impacts. However, as urban areas become more densely developed, opportunities to build more roadways decline and there is an increased emphasis to maximize the utilization of the existing facilities through the use of more effective advanced traffic management systems (ATMS). In recent years, Bluetooth technology has been adapted for use as a sensor for measuring vehicle travel times along a segment of roadway. However, using Bluetooth technology in ATMS such as advanced traffic signal controls has been limited in part because there has been little research conducted to investigate the opportunity to estimate intersection control delay and desired travel time using Bluetooth collected data. Furthermore, until recently, there has been a lack of tools, such as simulation, to predict the behavior of the system before it is developed and deployed. Therefore, the main intention of this research was to develop a robust framework for adaptive traffic signal control using Bluetooth detectors as the main source of data. In this thesis, I addressed the aforementioned challenges and proposed the first integrated framework for real time adaptive traffic signal management using Bluetooth data through the following steps: 1. I have developed a state of the art simulation framework to simulate the Bluetooth detection process. The simulation model was calibrated and validated using field data collected from two custom built Bluetooth detectors. The proposed simulation framework was combined with commercially available traffic microsimulation models to evaluate and develop the use of Bluetooth technology within ATMS. 2. I proposed two methods for estimating control delay at signalized intersections using Bluetooth technology. Method 1 requires data from a single Bluetooth detector deployed at the intersection and estimates delay on the basis of the measured Bluetooth dwell time. This model has a high level of accuracy when the queue length is less than the effective range of the Bluetooth detectors but performs poorly when queues exceed the detector range. Method 2 uses Bluetooth measured travel time to estimate control delay. This method requires data from two Bluetooth detectors, one at the intersection and one upstream. This method can be used regardless of the length of queues. The methods were evaluated using a custom-built simulation framework and field data. The results show that the proposed methods provide an accuracy of mean absolute error equal to 3 seconds, indicating that they are suitable for estimating control delay at signalized intersections. 3. I proposed a method to dynamically optimize green splits of signalized intersection on the basis of control delay estimated from Bluetooth detector data. Evaluation of the proposed method using a simulated hypothetical intersection demonstrated that proposed method is able to provide reduction in average delay in comparison with fixed signal timing and fully actuated controller. 4. I proposed and evaluated a method to estimate the desired travel time of the platoon in real-time and then use this estimate to optimize the offset of signalized intersections in a corridor. The proposed method has been evaluated using simulated data for a range of traffic demands and weather conditions and results indicate that the proposed method can provide up to a 75% reduction in average vehicle delay for vehicles discharging in the coordinated phase in comparison with fixed offset timing in different weather conditions.

Multi-perspective System-wide Analyses of Adaptive Traffic Signal Control Systems Using Microsimulation and Contemporary Data Sources

Download Multi-perspective System-wide Analyses of Adaptive Traffic Signal Control Systems Using Microsimulation and Contemporary Data Sources PDF Online Free

Author : Abhay Dnyaneshwar Lidbe
Publisher :
ISBN 13 :
Total Pages : 157 pages
Book Rating : 4.:/5 (974 download)

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Book Synopsis Multi-perspective System-wide Analyses of Adaptive Traffic Signal Control Systems Using Microsimulation and Contemporary Data Sources by : Abhay Dnyaneshwar Lidbe

Download or read book Multi-perspective System-wide Analyses of Adaptive Traffic Signal Control Systems Using Microsimulation and Contemporary Data Sources written by Abhay Dnyaneshwar Lidbe and published by . This book was released on 2016 with total page 157 pages. Available in PDF, EPUB and Kindle. Book excerpt: The primary function of traffic signals is to assign the right of way to vehicular and pedestrian traffic at intersections. Effective traffic signal system reduces congestion, increases intersection capacity, and improves other traffic related performance measures such as safety and mobility. To ensure these goals are met, traffic signals require updated timings to maintain proper operation. These updated signal timings impact not only traffic performance, but overall transportation system efficiency. Because traditional signal timing plans may not accommodate variable and unpredictable traffic demands, a more proactive approach is necessary to ensure properly timed and maintained traffic signals. Adaptive traffic control systems (ATCS) continually collect data and optimize signal timing on a real time basis thereby reducing the aforementioned drawbacks of traditional signal retiming. Understanding and characterizing how these systems are working is important to transportation engineers, and evaluating these systems can provide useful insights. The objective of this dissertation is to develop evaluation methodologies (both operational and economical) for adaptive traffic signal control that go beyond the traditional assessments that use traffic measures of effectiveness (MOEs). Case studies are conducted for Sydney Coordinated Adaptive Traffic System (SCATS) implementations in Alabama, which are useful in objective evaluations of ATCS (in general) for both their current and future operational environments by using microsimulation techniques and/or field data from contemporary data sources. The study contains detailed comparative analyses of traffic operations of the study corridors for existing peak hour traffic conditions under the previous time-of-day (TOD) plan and similar peak hour conditions after SCATS implementation. Although simulation analysis using VISSIM traffic microsimulation software is the primary methodological technique used for evaluating comparative performances, arterial data from other sources (Bluetooth MAC Address Matching and crowdsourced travel data) are also used to perform the evaluations, which is a novel application for this context. While past studies have considered either the arterial or its side-streets performances in their evaluations, this work explored a system-wide approach looking at the composite performance of both dimensions together. Finally, for transportation agencies which operate within budget constraints, it is important to know the real worth of attaining the benefits from ATCS implementations. The last chapter of this dissertation extends the evaluation methodology to include benefit-cost analysis (BCA) by evaluating the ATCS performance for both current and future traffic conditions. This information will be helpful for transportation agencies, planners, and practitioners to understand and justify their ATCS investment and also serve as a guideline for their future ITS projects.

Enhanced Traffic Signal Operation Using Connected Vehicle Data

Author : Ehsan Bagheri
Publisher :
ISBN 13 :
Total Pages : 168 pages
Book Rating : 4.:/5 (13 download)

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Book Synopsis Enhanced Traffic Signal Operation Using Connected Vehicle Data by : Ehsan Bagheri

Download or read book Enhanced Traffic Signal Operation Using Connected Vehicle Data written by Ehsan Bagheri and published by . This book was released on 2017 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Data-driven Adaptive Traffic Signal Control Via Deep Reinforcement Learning

Author : Tian Tan
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (119 download)

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Book Synopsis Data-driven Adaptive Traffic Signal Control Via Deep Reinforcement Learning by : Tian Tan

Download or read book Data-driven Adaptive Traffic Signal Control Via Deep Reinforcement Learning written by Tian Tan and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Advanced Traffic Signal Control Using Bluetooth Detectors

Author : Jordan Hart-Bishop
Publisher :
ISBN 13 :
Total Pages : 86 pages
Book Rating : 4.:/5 (13 download)

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Book Synopsis Advanced Traffic Signal Control Using Bluetooth Detectors by : Jordan Hart-Bishop

Download or read book Advanced Traffic Signal Control Using Bluetooth Detectors written by Jordan Hart-Bishop and published by . This book was released on 2018 with total page 86 pages. Available in PDF, EPUB and Kindle. Book excerpt: Traditionally signal timing plans are developed for expected traffic demands at an intersection. This approach generally offers the best operation for typical conditions. However, when variation in the traffic demand occurs, the signal timing plan developed for typical conditions may not be adequate resulting in significant congestion and delay. There have been many techniques developed to address these variations and they fall into one of two categories: (1) if the variations follow a consistent temporal pattern, then a set of fixed-time signal timing plans can be developed, each for a specific time of the day; (2) if the variations cannot be predicted a priori, then a system that measures traffic demands and alters signal timings in real-time is desired. This research focuses on improving the latter approach with a novel application of Bluetooth detector data. Conventional traffic responsive plan selection (TRPS) systems rely extensively on traffic sensors (typically loop detectors or equivalent) to operate, which are costly to install and maintain, and provide information about traffic only at the points which they are installed. This thesis explores the use of Bluetooth detectors as an alternative data source for TRPS due to their ease of installation and capability to provide information over an area rather than at a single point. This research consists of simulated and field traffic data associated with Bluetooth detectors. The field and simulated traffic data were from a section of Hespeler Road in Cambridge Ontario, bounded by Ontario Highway 401 to the north and Highway 8 to the south. The study corridor is approximately 5.0 kilometres long, and consists of 14 signalized intersections. In order to determine the potential of Bluetooth detectors as a data source, several measures of performance were considered for use in a Bluetooth-based system. The viability of each one was assessed in microsimulation experiments, and it was found that Bluetooth travel time was the most accurate at identifying true traffic conditions. On the basis of the simulation results a field pilot study was designed. Bluetooth detectors and conventional traffic detectors were installed at study intersections along the Hespeler Road corridor to measure real traffic conditions. From these measurements an algorithm was developed to determine when traffic conditions varied from the expected conditions. The final stage of the research evaluated the proposed algorithm using a controlled simulation environment with known atypical traffic patterns. It was found that the algorithm was capable of identifying the atypical conditions that were simulated based on field conditions. The key findings of this research are that (1) Bluetooth detectors are able to provide measured travel times from individual vehicles with sufficient accuracy, and with sufficient sample sizes, that the aggregated travel time information can be used to identify the traffic conditions at a signalized intersections; and (2) these measurements can be used instead of data from conventional traffic detectors, to determine when to switch from time of day fixed time traffic signal control to TRPS control.

Improving Performance of Coordinated Signal Control Systems Using Signal and Loop Data

Author : Meng Li (Writer on traffic signs and signals)
Publisher :
ISBN 13 :
Total Pages : 138 pages
Book Rating : 4.F/5 ( download)

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Book Synopsis Improving Performance of Coordinated Signal Control Systems Using Signal and Loop Data by : Meng Li (Writer on traffic signs and signals)

Download or read book Improving Performance of Coordinated Signal Control Systems Using Signal and Loop Data written by Meng Li (Writer on traffic signs and signals) and published by . This book was released on 2010 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Swarm-intelligence Based Adaptive Signal System

Author : Jonathan Corey
Publisher :
ISBN 13 :
Total Pages : 192 pages
Book Rating : 4.:/5 (899 download)

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Book Synopsis Swarm-intelligence Based Adaptive Signal System by : Jonathan Corey

Download or read book Swarm-intelligence Based Adaptive Signal System written by Jonathan Corey and published by . This book was released on 2014 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Adaptive Traffic Signal Control Using Deep Reinforcement Learning for Network Traffic Incidents

Author : Tianxin Li (M.S. in Engineering)
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (141 download)

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Book Synopsis Adaptive Traffic Signal Control Using Deep Reinforcement Learning for Network Traffic Incidents by : Tianxin Li (M.S. in Engineering)

Download or read book Adaptive Traffic Signal Control Using Deep Reinforcement Learning for Network Traffic Incidents written by Tianxin Li (M.S. in Engineering) and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Internet of Things and Fog Computing-Enabled Solutions for Real-Life Challenges

Author : Anil Saroliya
Publisher : CRC Press
ISBN 13 : 1000805395
Total Pages : 205 pages
Book Rating : 4.0/5 (8 download)

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Book Synopsis Internet of Things and Fog Computing-Enabled Solutions for Real-Life Challenges by : Anil Saroliya

Download or read book Internet of Things and Fog Computing-Enabled Solutions for Real-Life Challenges written by Anil Saroliya and published by CRC Press. This book was released on 2022-12-29 with total page 205 pages. Available in PDF, EPUB and Kindle. Book excerpt: In today’s world, the use of technology is growing rapidly, and people need effective solutions for their real-life problems. This book discusses smart applications of associated technologies to develop cohesive and comprehensive solutions for the betterment of humankind. It comprehensively covers the effective use of the Internet of Things (IoT), wireless sensor network, wearable sensors, body area network, cloud computing, and distributed computing methodologies. The book comprehensively covers IoT and fog computing sensor supported technologies or protocols including web of things, near-field communication, 6LoWPAN, LoRAWAN, XMPP, DDS, LwM2M, Mesh Protocol, and radio-frequency identification. The book- Discusses smart applications to develop cohesive and comprehensive solutions for real-life problems. Covers analytical descriptions with appropriate simulation and prototype models. Examines the role of IoT and fog computing technologies during global emergency situations. Discusses key technologies including cloud computing, 5G communication, big data, artificial intelligence, control systems, and wearable sensors. The text is primarily written for graduate students, and academic researchers working in diverse fields of electrical engineering, biomedical engineering, electronics and communication engineering, computer engineering, and information technology.

Development of a Phase-by-phase, Arrival-based Delay-optimized Adaptive Traffic Signal Control Methodology with Metaheuristic Search

Download Development of a Phase-by-phase, Arrival-based Delay-optimized Adaptive Traffic Signal Control Methodology with Metaheuristic Search PDF Online Free

Author : Michael Shenoda
Publisher :
ISBN 13 :
Total Pages : 106 pages
Book Rating : 4.3/5 (555 download)

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Book Synopsis Development of a Phase-by-phase, Arrival-based Delay-optimized Adaptive Traffic Signal Control Methodology with Metaheuristic Search by : Michael Shenoda

Download or read book Development of a Phase-by-phase, Arrival-based Delay-optimized Adaptive Traffic Signal Control Methodology with Metaheuristic Search written by Michael Shenoda and published by . This book was released on 2006 with total page 106 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Traffic Signal Timing Optimization with Connected Vehicles

Author : Wan Li
Publisher :
ISBN 13 :
Total Pages : 145 pages
Book Rating : 4.:/5 (111 download)

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Book Synopsis Traffic Signal Timing Optimization with Connected Vehicles by : Wan Li

Download or read book Traffic Signal Timing Optimization with Connected Vehicles written by Wan Li and published by . This book was released on 2019 with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Deep Reinforcement Learning Approach to Multimodal Adaptive Traffic Signal Control

Author : Soheil Mohamad Alizadeh Shabestary
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (133 download)

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Book Synopsis Deep Reinforcement Learning Approach to Multimodal Adaptive Traffic Signal Control by : Soheil Mohamad Alizadeh Shabestary

Download or read book Deep Reinforcement Learning Approach to Multimodal Adaptive Traffic Signal Control written by Soheil Mohamad Alizadeh Shabestary and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Application of the Stochastic Optimization Method in Optimizing Traffic Signal Control Settings

Author : Byungkyu Park
Publisher :
ISBN 13 :
Total Pages : 42 pages
Book Rating : 4.3/5 (555 download)

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Book Synopsis Application of the Stochastic Optimization Method in Optimizing Traffic Signal Control Settings by : Byungkyu Park

Download or read book Application of the Stochastic Optimization Method in Optimizing Traffic Signal Control Settings written by Byungkyu Park and published by . This book was released on 2008 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Adaptive Traffic Signal Control Using Approximate Dynamic Programming

Author : Chen Cai
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (116 download)

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Book Synopsis Adaptive Traffic Signal Control Using Approximate Dynamic Programming by : Chen Cai

Download or read book Adaptive Traffic Signal Control Using Approximate Dynamic Programming written by Chen Cai and published by . This book was released on 2010 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Flow-based Adaptive Split Signal Control

Author : Airton Gustavo Kohls
Publisher :
ISBN 13 :
Total Pages : 104 pages
Book Rating : 4.:/5 (648 download)

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Book Synopsis Flow-based Adaptive Split Signal Control by : Airton Gustavo Kohls

Download or read book Flow-based Adaptive Split Signal Control written by Airton Gustavo Kohls and published by . This book was released on 2010 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the last 35 years many adaptive traffic signal control systems have been developed presenting alternative strategies to improve traffic signal operations. However, less than 1% of all traffic signals in the United States are controlled by adaptive systems today. The extensive infrastructure necessary including reliable communication and complex calibration leads to a time consuming and costly process. In addition, the most recent National Traffic Signal Report Card indicated an overall grade of D for the nation's traffic signal control and operations. Recent economic adversity adds to the already difficult task of proactively managing aged signal timing plans. Therefore, in an attempt to escape the status quo, a flow based adaptive split signal control model is presented, having the principal objective of updating the split table based solely on real-time traffic conditions and without disrupting coordination. Considering the available typical traffic signal control infrastructure in cities today, a non centralized system is proposed, directed to the improvement of National Electrical Manufacturers Association (NEMA) based systems that are compliant with the National Transportation Communications for Intelligent Transportation System Protocol (NTCIP) standards. The approach encompasses the User Datagram Protocol (UDP) for system communication allowing an external agent to gather flow information directly from a traffic signal controller detector status and use it to better allocation of phase splits. The flow based adaptive split signal control was not able to consistently yield significant lower average vehicle delay than a full actuated signal controller when evaluated on an intersection operating a coordinated timing plan. However, the research proposes the ability of an external agent to seamless control a traffic signal controller using real-time data, suggesting the encouraging results of this research can be improved upon.

Adaptive Traffic Light Control in Wsn-Based Its

Author : Binbin Zhou
Publisher : LAP Lambert Academic Publishing
ISBN 13 : 9783848425426
Total Pages : 156 pages
Book Rating : 4.4/5 (254 download)

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Book Synopsis Adaptive Traffic Light Control in Wsn-Based Its by : Binbin Zhou

Download or read book Adaptive Traffic Light Control in Wsn-Based Its written by Binbin Zhou and published by LAP Lambert Academic Publishing. This book was released on 2012-04 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: The conventional surveillance methods used in ITS to detect real-time traffic data, e.g. video image processing and inductive loops detection, have several shortcomings, such as limited coverage and high costs of implementation and maintenance. Wireless Sensor Networks (WSNs) offer the potential of providng real-time traffic data without these drawbacks. Hence, in the past decade, WSNs have been applied to ITS to improve the performance of ITS. Controlling traffic lights plays a key role in ITS. We investigate how to design methods and algorithms for adaptive traffic light control in a WSN-based ITS. We propose models and schemes for adaptive traffic light control for both isolated intersections and multiple intersections. The proposed approaches take advantage of real-time traffic information collected by WSNs to achieve high system throughput, low waiting time and few stops for the vehicles. We have implemented the proposed schemes on our testbed for Intelligent Services with WSNs, iSensNet to evaluate and demonstrate the performance. Our experimental results show that our approaches can deal with different traffic conditions in an effective manner.

ITS Sensors and Architectures for Traffic Management and Connected Vehicles

Author : Lawrence A. Klein
Publisher : CRC Press
ISBN 13 : 1351800965
Total Pages : 518 pages
Book Rating : 4.3/5 (518 download)

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Book Synopsis ITS Sensors and Architectures for Traffic Management and Connected Vehicles by : Lawrence A. Klein

Download or read book ITS Sensors and Architectures for Traffic Management and Connected Vehicles written by Lawrence A. Klein and published by CRC Press. This book was released on 2017-08-07 with total page 518 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.