Download or read book Development and Evaluation of a Network wide Traffic Signal Coordination NETSCA Algorithm for Improved Operational Performance written by Peng Liu and published by . This book was released on 2015 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt: Traffic congestion is becoming an increasingly serious issue in urban road networks across the United States and around the world. Over many solutions to traffic congestion, operational improvement is preferred because a properly designed and fine-tuned traffic signal timing plan can significantly improve travel delay of motorists, progression of traffic flow, vehicle emissions, fuel consumption and operating costs. In this dissertation, a traffic signal coordination algorithm, named Network-wide Traffic Signal Coordination Algorithm (NeTSCA), is developed aiming to improve the operational performance of traffic network. The developed algorithm consists of two optimization levels: corridor level and network level. At the corridor level, NeTSCA maximizes the bandwidth along each corridor, taking unbalanced directional traffic flow, residual queue and start-up delay into account. At the network level, NeTSCA aims to maximize total network bandwidth under the major intersection constraints, while taking corridor importance level into account. Offset fine-tuning functionality is designed into NeTSCA as a post-processing optimization step to deal with vehicle speed variation and other complex practical situations.Micro-simulation is utilized to evaluate the effectiveness of NeTSCA on improving network operational performance in comparison with SYNCHRO and a baseline condition under various traffic conditions. Statistical analyses on performance measures obtained from VISSIM micro-simulation platform validate that NeTSCA can effectively improve network-wide operational performance with a decreased average delay, an increased average speed and a reduced average number of stops under most test scenarios. Future work such as real-time optimization capability and field implementation of the developed algorithm are discussed in this dissertation.

Download or read book Projection Algorithm for Improved Corridor Signal Coordination written by Cong Feng and published by . This book was released on 2009 with total page 78 pages. Available in PDF, EPUB and Kindle. Book excerpt: With ever-increasing travel demands on current urban roadway networks and limited financial resources, it has become increasingly challenging for traffic engineers to improve the roadway level of service efficiently and economically (1). Compared with an infrastructure reconstruction project, optimizing the current traffic facilities, especially the traffic signal control systems, is favored by most of traffic engineers. In many cases, advanced traffic control devices and well designed traffic operation strategies can provide effectively relief from congestion with considerably low investment. In recent years, more and more state-of-the-art traffic control systems have been developed to reduce travel delay, smooth traffic flow, monitor traffic incident, and reduce fuel consumption. Many advanced technologies have been integrated in modern traffic control systems, including new vehicle detection devices, video processing hardware, short-range communication equipment, GPS, GIS, etc. In those systems, signal coordination is one of the most important concepts, especially when the system is designed for the entire signalized urban corridor rather than isolated intersection. A corridor signal optimization method called projection algorithm (PA) was developed and tested in this thesis. By using green splits at each intersection and the link length as well as speed limit between intersections as input, PA calculates the offset for each intersection to maximize the green bandwidth along the corridor and it further improves the method by considering the residual queue, start-up delay, unbalanced directional volume in signal optimization. The signal timing plan from this algorithm has been evaluated in VISSIM simulation platform and the effectiveness is discussed. Preliminary results show that PA is able to produce effective signal coordination plans, especially in low and medium volume conditions with competitive output in comparison with plans from other methods. Built on the findings, a comprehensive evaluation of the green bandwidth method has also been conducted for future research and field implementation.

Download or read book Coordination of Traffic Signals in Networks and Related Graph Theoretical Problems on Spanning Trees written by Gregor Wünsch and published by Cuvillier Verlag. This book was released on 2008 with total page 153 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Improving Traffic Safety and Efficiency by Adaptive Signal Control Based on Deep Reinforcement Learning written by Yaobang Gong and published by . This book was released on 2020 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt: As one of the most important Active Traffic Management strategies, Adaptive Traffic Signal Control (ATSC) helps improve traffic operation of signalized arterials and urban roads by adjusting the signal timing to accommodate real-time traffic conditions. Recently, with the rapid development of artificial intelligence, many researchers have employed deep reinforcement learning (DRL) algorithms to develop ATSCs. However, most of them are not practice-ready. The reasons are two-fold: first, they are not developed based on real-world traffic dynamics and most of them require the complete information of the entire traffic system. Second, their impact on traffic safety is always a concern by researchers and practitioners but remains unclear. Aiming at making the DRL-based ATSC more implementable, existing traffic detection systems on arterials were reviewed and investigated to provide high-quality data feeds to ATSCs. Specifically, a machine-learning frameworks were developed to improve the quality of and pedestrian and bicyclist’s count data. Then, to evaluate the effectiveness of DRL-based ATSC on the real-world traffic dynamics, a decentralized network-level ATSC using multi-agent DRL was developed and evaluated in a simulated real-world network. The evaluation results confirmed that the proposed ATSC outperforms the actuated traffic signals in the field in terms of travel time reduction. To address the potential safety issue of DRL based ATSC, an ATSC algorithm optimizing simultaneously both traffic efficiency and safety was proposed based on multi-objective DRL. The developed ATSC was tested in a simulated real-world intersection and it successfully improved traffic safety without deteriorating efficiency. In conclusion, the proposed ATSCs are capable of effectively controlling real-world traffic and benefiting both traffic efficiency and safety.

Download or read book Development of Dynamic Real time Integration of Transit Signal Priority in Coordinated Traffic Signal Control System Using Genetic Algorithms and Artificial Neural Networks written by Mohammad Shareef Ghanim and published by . This book was released on 2008 with total page 454 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Formulation and Evaluation of a Methodology for Network wide Signal Optimization written by Shiow-Min Lin and published by . This book was released on 1999 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: ABSTRACT (cont.): The solution algorithm for the methodology developed in this study is based entirely on dynamic programming, and it is capable of performing network-wide signal optimization. It has been shown more computationally efficient without compromising global optimality. In addition, a heuristic search procedure has been developed. It can significantly reduce the computation and still generate comparable results. Both solution algorithms have been implemented and evaluated in a simulation testing environment, and the simulation results indicate significant improvements compared to a well-timed fixed-time control and an actuated signal. The methodology developed in this study provides a feasible computational framework that can be applied to a dynamic urban traffic control in conjunction with Advanced Traffic Management Systems and Advanced Traveler Information Systems for network-wide signal optimization.

Download or read book Development and Evaluation of an Evolutionary Algorithm for Sustainable Traffic Signal Control Concepts written by Jelka Stevanovic and published by . This book was released on 2010 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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.

Download or read book Reinforcement Learning Based Traffic Signal Control for Signalized Intersections written by Dunhao Zhong and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Vehicles have become an indispensable means of transportation to ensure people's travel and living materials. However, with the increasing number of vehicles, traffic congestion has become severe and caused a lot of social wealth loss. Therefore, improving the efficiency of transport management is one of the focuses of current academic circles. Among the research in transport management, traffic signal control (TSC) is an effective way to alleviate traffic congestion at signalized intersections. Existing works have successfully applied reinforcement learning (RL) techniques to achieve a higher TSC efficiency. However, previous work remains several challenges in RL-based TSC methods. First, existing studies used a single scaled reward to frame multiple objectives. Nevertheless, the single scaled reward has lower scalability to assess the controller's performance on different objectives, resulting in higher volatility on different traffic criteria. Second, adaptive traffic signal control provides dynamic traffic timing plans according to unforeseeable traffic conditions. Such characteristic prohibits applying the existing eco-driving strategies whose strategies are generated based on foreseeable and prefixed traffic timing plans. To address the challenges, in this thesis, we propose to design a new RL-TSC framework along with an eco-driving strategy to improve the TSC's efficiency on multiple objectives and further smooth the traffic flows. Moreover, to achieve effective management of the system-wide traffic flows, current researches tend to focus on the design of collaborative traffic signal control methods. However, the existing collaboration-based methods often ignore the impact of transmission delay for exchanging traffic flow information on the system. Inspired by the state-of-the-art max-pressure control in the traffic signal control area, we propose a new efficient RL-based cooperative TSC scheme by improving the reward and state representation based on the max-pressure control method and developing an agent that can address the data transmission delay issue by decreasing the discrepancy between the real-time and delayed traffic conditions. To evaluate the performance of our proposed work more accurately, in addition to the synthetic scenario, we also conducted an experiment based on the real-world traffic data recorded in the City of Toronto. We demonstrate that our method surpassed the performance of the previous traffic signal control methods through comprehensive experiments.

Download or read book Dynamic Network wide Traffic Signal Optimization written by M. E. Ting Lu and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Network wide Traffic State Analysis written by Ramin Saedi Germi and published by . This book was released on 2020 with total page 197 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Network Fundamental Diagram (NFD) represents dynamics of traffic flow at the network level. It is exploited to design various network-wide traffic control and pricing strategies to improve mobility and mitigate congestion. This study presents a framework to estimate NFD and incorporates it for three specific applications in large-scale urban networks. Primarily, a resource allocation problem is formulated to find the optimal location of fixed measurement points and optimal sampling of probe trajectories to estimate NFD accounting for limited resources for data collection, network traffic heterogeneity and asymmetry in OD demand in a real-world network. Using a calibrated simulation-based dynamic traffic assignment model of Chicago downtown network, a successful application of the proposed model and solution algorithm to estimate NFD is presented. The proposed model, then, is extended to take into account the stochasticity of day-to-day fluctuations of OD demand in NFD estimation.Three main applications of NFD are also shown in this research: network-wide travel time reliability estimation, network-wide emission estimation, and real-time traffic state estimation for heterogenous networks experiencing inclement weather impact. The main objective of the travel time reliability estimation application is to improve estimation of this network-wide measure of effectiveness using network partitioning. To this end, a heterogeneous large-scale network is partitioned into homogeneous regions (clusters) with well-defined NFDs using directional and non-directional partitioning approaches. To estimate the network travel time reliability, a linear relationship is estimated that relates the mean travel time with the standard deviation of travel time per unit of distance at the network level. Partitioning and travel time reliability estimation are conducted for both morning and afternoon peak periods to demonstrate the impacts of travel demand pattern variations.This study also proposes a network-level emission modeling framework via integrating NFD properties with an existing microscopic emission model. The NFDs and microscopic emission models are estimated using microscopic and mesoscopic traffic simulation tools at different scales for various traffic compositions. The major contribution is to consider heterogenous vehicle types with different emission generation rates in the network-level model. Non-linear and support vector regression models are developed using simulated trajectory data of thirteen simulated scenarios. The results show a satisfactory calibration and successful validation with acceptable deviations from underlying microscopic emission model, regardless of the simulation tool that is used to calibrate the network-level emission model.Finally, the NFD application for real-time traffic state estimation in a network experiencing inclement weather conditions is explored. To this end, the impacts of weather conditions on the NFD and travel time reliability relation are illustrated through a scenario-based analysis using traffic simulation. Then, the real-time traffic state prediction framework in the literature is adjusted to capture weather conditions as a key parameter. The extended Kalman filter algorithm is employed as an estimation engine to predict the real-time traffic state. The results highlight the importance of considering weather conditions in the traffic state prediction model.

Download or read book MASTraf a Decentralized Multi agent System for Network wide Traffic Signal Control with Dynamic Coordination written by and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Data Driven Approaches for Robust Signal Plans in Urban Transportation Networks written by Zahra Amini and published by . This book was released on 2018 with total page 67 pages. Available in PDF, EPUB and Kindle. Book excerpt: In urban transportation networks with signalized intersections a robust pre-timed signal plan is a practical alternative to adaptive control strategies, since it has less complexity and an easier implementation process. Recent advances in technology are making data collection at traffic signals economical and data-driven approaches are likely to benefit from the large traffic data. Data-driven approaches are necessary for designing robust timing plans that can satisfy rapid traffic volume fluctuation and demand growth. This dissertation introduces four data-driven approaches for studying and improving traffic conditions at signalized intersections. Firstly, I discuss the development and testing of two algorithms for checking the quality of traffic data and for estimating performance measures at intersections. The first of these algorithms estimates the systematic error of the detector data at signalized intersections by using flow conservation. According to the ground truth data from a real-world network, the algorithm can reduce the error in the data up to 25%. The second algorithm helps in estimating intersection performance measures in real-time by measuring the number of the vehicles in each approach using high resolution(HR) data. An offset optimization algorithm was developed to adjust signal offsets so as to improve the delay in the system. The performance of three real-world networks using the offsets obtained by the algorithm and those obtained from the widely used Synchro optimization tool, are compared using the VISSIM microscopic simulation model. Simulation results show up to a 30% reduction in the average number of stops and total delay that vehicles experience along the major routes when using the proposed algorithms’ optimized offsets. The fourth algorithm estimates the appropriate switching time between designed timing plans during the day based on the traffic profile of the intersection by using the K-means clustering method. In conclusion, these four algorithms extract useful information from HR data about traffic at signalized intersections. Moreover, the algorithms assist in designing robust timing plans for satisfying demand fluctuations at signalized intersection. Lastly, simulation results from real-world networks illustrate the significant improvements that the proposed data-driven approaches can make in the control systems at urban transportation networks.

Download or read book Development of a Performance Prediction Algorithm for Evaluation of a Traffic Adaptive Signal System for Isolated High speed Intersections written by Karl Howard Zimmerman and published by . This book was released on 2003 with total page 494 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Network World written by and published by . This book was released on 2002-04-01 with total page 60 pages. Available in PDF, EPUB and Kindle. Book excerpt: For more than 20 years, Network World has been the premier provider of information, intelligence and insight for network and IT executives responsible for the digital nervous systems of large organizations. Readers are responsible for designing, implementing and managing the voice, data and video systems their companies use to support everything from business critical applications to employee collaboration and electronic commerce.

Download or read book Network World written by and published by . This book was released on 1998-05-11 with total page 68 pages. Available in PDF, EPUB and Kindle. Book excerpt: For more than 20 years, Network World has been the premier provider of information, intelligence and insight for network and IT executives responsible for the digital nervous systems of large organizations. Readers are responsible for designing, implementing and managing the voice, data and video systems their companies use to support everything from business critical applications to employee collaboration and electronic commerce.

Download or read book Computerworld written by and published by . This book was released on 2002-04-01 with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt: For more than 40 years, Computerworld has been the leading source of technology news and information for IT influencers worldwide. Computerworld's award-winning Web site (Computerworld.com), twice-monthly publication, focused conference series and custom research form the hub of the world's largest global IT media network.