The State-of-the-art of Coordinated Ramp Control with Mixed Traffic Conditions

Ramp metering, a traditional traffic control strategy for conventional vehicles, has been widely deployed around the world since the 1960s. On the other hand, the last decade has witnessed significant advances in connected and automated vehicle (CAV) technology and its great potential for improving safety, mobility and environmental sustainability. Therefore, a large amount of research has been conducted on cooperative ramp merging for CAVs only. However, it is expected that the phase of mixed traffic, namely the coexistence of both human-driven vehicles and CAVs, would last for a long time. Since there is little research on the system-wide ramp control with mixed traffic conditions, the paper aims to close this gap by proposing an innovative system architecture and reviewing the state-of-the-art studies on the key components of the proposed system. These components include traffic state estimation, ramp metering, driving behavior modeling, and coordination of CAVs. All reviewed literature plot an extensive landscape for the proposed system-wide coordinated ramp control with mixed traffic conditions.Comment: 8 pages, 1 figure, IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE - ITSC 201

Vision-Based Lane-Changing Behavior Detection Using Deep Residual Neural Network

Accurate lane localization and lane change detection are crucial in advanced driver assistance systems and autonomous driving systems for safer and more efficient trajectory planning. Conventional localization devices such as Global Positioning System only provide road-level resolution for car navigation, which is incompetent to assist in lane-level decision making. The state of art technique for lane localization is to use Light Detection and Ranging sensors to correct the global localization error and achieve centimeter-level accuracy, but the real-time implementation and popularization for LiDAR is still limited by its computational burden and current cost. As a cost-effective alternative, vision-based lane change detection has been highly regarded for affordable autonomous vehicles to support lane-level localization. A deep learning-based computer vision system is developed to detect the lane change behavior using the images captured by a front-view camera mounted on the vehicle and data from the inertial measurement unit for highway driving. Testing results on real-world driving data have shown that the proposed method is robust with real-time working ability and could achieve around 87% lane change detection accuracy. Compared to the average human reaction to visual stimuli, the proposed computer vision system works 9 times faster, which makes it capable of helping make life-saving decisions in time

iTETRIS: An Integrated Wireless and Traffic Platform for Real-Time Road Traffic Management Solutions

Wireless vehicular cooperative systems have been identified as an attractive solution to improve road traffic management, thereby contributing to the European goal of safer, cleaner, and more efficient and sustainable traffic solutions. V2V-V2I communication technologies can improve traffic management through real-time exchange of data among vehicles and with road infrastructure. It is also of great importance to investigate the adequate combination of V2V and V2I technologies to ensure the continuous and costefficient operation of traffic management solutions based on wireless vehicular cooperative solutions. However, to adequately design and optimize these communication protocols and analyze the potential of wireless vehicular cooperative systems to improve road traffic management, adequate testbeds and field operational tests need to be conducted. Despite the potential of Field Operational Tests to get the first insights into the benefits and problems faced in the development of wireless vehicular cooperative systems, there is yet the need to evaluate in the long term and large dimension the true potential benefits of wireless vehicular cooperative systems to improve traffic efficiency. To this aim, iTETRIS is devoted to the development of advanced tools coupling traffic and wireless communication simulators

Federated Embedded Systems – a review of the literature in related fields

This report is concerned with the vision of smart interconnected objects, a vision that has attracted much attention lately. In this paper, embedded, interconnected, open, and heterogeneous control systems are in focus, formally referred to as Federated Embedded Systems. To place FES into a context, a review of some related research directions is presented. This review includes such concepts as systems of systems, cyber-physical systems, ubiquitous computing, internet of things, and multi-agent systems. Interestingly, the reviewed fields seem to overlap with each other in an increasing number of ways

Evaluating the Impacts of Accelerated Incident Clearance Tools and Strategies by Harnessing the Power of Microscopic Traffic Simulation

Traffic incidents cause Americans delay, waste fuel, cause injuries, and create toxic emissions. Transportation professionals have implemented a variety of tools to manage these impacts and researchers have studied their effectiveness, illustrating a wide range between different tools and locations. To improve this state of knowledge, this dissertation sought to 1) identify prominent and effective incident management strategies, 2) model six selected incident management strategies within five highway corridors in South Carolina, and 3) apply benefit-cost analysis to evaluate the impact of various combinations of these strategies. To meet these objectives, the author evaluated published literature of the selected strategies, administered a nationwide survey of these strategies, conducted traffic simulation, and performed benefit-cost analysis. The literature review guided the author to fill gaps in knowledge regarding the effectiveness and expense of identified strategies. The nationwide survey identified effective incident management tools, the extent of their adoption, and their common problems. The author then applied PARAMICS traffic simulation software to evaluate the impact of six tools at five sites on metropolitan interstates throughout South Carolina. Finally, benefit-cost analysis was used to evaluate the benefits against costs at each study site. The survey provided many insights into both the effectiveness and collaboration within and among traffic incident management agencies and guided the author in selecting tools for evaluation. While the simulation study found that as the severity and duration of incident increases, so does the potential benefit of incident management tools, the frequency of incidents also produces significant impact on annual benefits. The benefit-cost analysis indicated that while all the incident management tools evaluated provided more benefits than costs, freeway service patrols and traffic cameras produced the highest return for incidents of varying severity. It was also found more advantageous to select one expensive but efficient incident management technology, rather than engage in the incremental deployment of various systems that might provide redundant benefits. Departments of transportation across the United States see the need to manage incidents more efficiently, consequently this dissertation developed data and analysis to compare benefits with costs to aid decision makers in selecting tools and strategies for future incident management endeavors