Browsing by Subject "Traffic management"
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Item Activation of the I-394 Laboratory for ITS Operational Testing: Phase 2(Center for Transportation Studies, University of Minnesota, 1998-04) Reynhout, Kenneth; Michalopoulos, Panos; Siagian, AlexanderThe key element in improving traffic operations and performing real-time management is the ability to assess the effectiveness of various alternatives prior to implementation. Likewise, the crucial feature for providing this capability is a Traffic Data Management System (TDMS), which gathers data and makes it available for various traffic analysis applications. The purpose of this research is to develop TDMS as part of a Laboratory Environment for TRaffic ANalysis (LETRAN). Such a laboratory environment would provide easy and efficient access to various kinds of traffic data for use in simulation, control, incident detection, and other types of traffic analysis applications to be deployed in a next-generation traffic management center. In addition, a Machine-Vision Laboratory (MVL) will be designed and implemented as part of the Center for Transportation Studies(CTS) Intelligent Transportation Systems Laboratory (ITS Lab). This MVL will use live video feeds from both freeways and arterial streets and provide machine-vision technology for conducting traffic detection, data collection, and group training exercises. Such capabilities will allow for the collection of detailed, accurate, and continuous data for successful model development, calibration, testing and evaluation.Item Activation of the I-394 Laboratory for ITS Operational Testing: Phase I(Center for Transportation Studies, University of Minnesota, 1997-12) Reynhout, Kenneth; Michalopoulos, Panos; Sullivan, Mike; Siagian, AlexanderThis and other related research have two primary objectives: 1. To develop practical operational tools which can be deployed for use in traffic-management and transportationsplanning activities. 2. To develop a laboratory infrastructure which will facilitate future advances in traffic modeling and other ITS initiatives. The objectives of this particular research project are as follows: * A thorough examination and documentation of the 1-394 system's specifications. * Diagnosis of the working condition of the 1-394 system. * Negotiation and implementation of a repair plan * Establishing a communications connection between the ITS Lab and the Traffic Management Center (TMC). * Activation of the 1-394 Lab, which will include a user's guide that describes the steps a user should take to access video, make a connection to the TMC and the 1-394 system, and configure the system for traffic detection and data collection. A completed 1-394 Lab would be a unique and valuable tool for obtaining information that loop-detectors have been unable to supply. This information includes flow dynamics, incident behavior, capacity, and other traffic-flow characteristics. This information is vital for fine tuning operational tools and schemes to be deployed in a future TMC, and will also provide an information foundation for future research and development.Item Development and Application of Demand-Responsive Ramp Metering Control to Improve Traffic Management in Freeway Corridors(1992-01) Stephanedes, Yorgos J.; Kwon, Eil; Chang, Kaikuo; Yao, PingA method is developed for evaluating traffic-responsive ramp metering strategies and improving freeway performance. The method emulates real-time metering and rigorously traces the interactions between automatic rate-selection metering strategies and freeway performance through time. Given a demand pattern and freeway geometrics, it provides assessment of metering strategies that change continuously at very short time intervals. Further, it explicitly treats time delays that can be caused by hardware or introduced by the traffic engineer.Item Development of an Integrated Simulation Package for Freeway Design, Operations and Adaptive Traffic Management. Phase I: Enhancement of the Kronos Simulation Program(1992-01) Michalopoulos, Panos; Kwon, Eil; Lee, Chifung; Mahadevan, Gopalakrishnan; Kang, JeongGyuThe main purpose of this project is to make KRONOS, a microcomputer-based freeway simulation program under development operational by resolving the following problems problems: detailed field testing and adjustments of the program with real freeway data, analysis of sensitivities in simulation models with respect to the variations in input data, and enhancements of the input/output module to be suitable for the Mn/DOT planning environment. In addition, a new simulation module to treat special types of freeway segements incluing merging/diverging of two freeways is developed and incorporated into KRONOS in this project. The major accomplisments made in this project are summarized.Item Development of the Next Generation Stratified Ramp Metering Algorithm Based on Freeway Density(Center for Transportation Studies, 2011-03) Geroliminis, Nikolas; Srivastava, Anupam; Michalopoulos, PanosA new coordinated, traffic-responsive ramp metering algorithm has been designed for Minnesota’s freeways based on density measurements, rather than flows. This is motivated in view of recent research indicating that the critical value of density at which capacity is observed is less sensitive and more stable than the value of capacity, thereby resulting in m ore effective control. Firstly, we develop a methodology to estimate densities with space and time based on data from loop detectors. The methodology is based on solving a flow conservation differential equation (using LWR theory) with intermediate (internal) freeway mainline boundaries, which is fast er and more accurate from previous resear ch using only external boundaries. To capture the capacity drop phenomenon into the first-order model we utilize a fundamental diagram with two values of capacity and we provide a memory-based methodology to choose the appropriate value in the numerical solution of the problem. Secondly, with respect to ramp metering, the main goals of the algorithm are to delay the onset of the breakdown and to accelerate system recovery when ramp metering is unable due to the violation of maximum allowable ramp waiting time. The effectiveness of the new control strategy is being assessed by comparison with the currently deployed version of the Stratified Zone Algorithm (SZM) through microscopic simulation of a real 12-mile, 17 ramp freeway section. Simulations show a decrease in the delays of mainline and ramp traffic, an improvement 8% in the overall delays and avoidance of the maximum ramp delay violations.Item ITMS Operational Test of Advanced Traffic Management and Traveler Information Systems : Integrated Traffic Management System Program in the Twin Cities Metropolitan Area(1992-10)Improving the efficiency of the nation's highways remains as one of the key objectives of the IVHS community ever since the applications of advanced technology to highway transportation began to receive the attention it is now getting. One of the primary initiatives in the drive to satisfy this goal is to better manage and control the movement of traffic in the typically congested urban highway environment. The Twin Cities of Minneapolis and St. Paul already has one of the most advanced traffic control and management systems in the nation. Mn/DOT has a significant data collection and processing capacity as well as the ability to disseminate information via changeable message signs and highway advisory radio. The Integrated Traffic Management System (ITMS) program aims to integrate the existing traffic management systems and coordinate their operation via a comprehensive communication network.Item Planning, Operation, and Management of Automated Transportation Systems: A Control-Theoretic Approach(2022-12) Wang, ShianWith the advent of emerging technologies like 5G network and wireless communication, automated vehicles (AVs) are expected to become increasingly available to travelers, offering a vast amount of benefits, such as enhanced traffic stability, reduced energy consumption, and optimized parking space allocation, among many others. It is highly anticipated that there will be a transitional period of the the auto market as human-driven vehicles (HVs) are gradually replaced by AVs. Many opportunities and challenges are expected to emerge during this transitioning process. To better prepare a nation for the arrival of AVs, in this dissertation we aim to address interesting yet pressing problems arising from vehicle automation in the context of planning, operation, and management of future transportation systems from a control-theoretic perspective. In view of the inevitable coexistence of HVs and AVs during the transitioning period, we develop a continuous-time dynamical model to capture the interactive temporal evolution of the market share of these two types of vehicles. A discrete choice model is constructed and incorporated into the dynamical model for describing the likelihood of customers choosing HVs or AVs. To achieve a desired temporal integration of AVs into the auto market, monetary subsidies and investment in AV-specific infrastructure are considered as decision variables to promote the adoption of AVs. Further, an optimal control problem is formulated with the objective of achieving a desired market penetration rate (MPR) at the end of any given finite planning horizon, while minimizing the cost of AV subsidies and infrastructure investment. The time-dependent optimal AV integration policy is determined by solving the formulated optimization problem, allowing a government agency to subsidize AV purchases and invest in future transportation infrastructure in an adaptive manner. The proposed approach is observed to be effective and robust under various demand patterns, such as increasing, decreasing, and stochastic demands. A systematic cost-benefit analysis with sensitivity analysis is conducted to evaluate the desirability of AV integration. The promising results provide significant managerial insights for government agencies into developing long-term strategic planning policies for the integration of AVs. Although appropriate incentive policies could accelerate the adoption of AVs, the MPR is expected to remain relatively low in the next thirty years or so, resulting in a predominantly human-driven mixed traffic flow consisting of HVs and AVs. Uniform traffic flow has been shown to be unstable in certain flow regimes due to collective behavior of human drivers, causing the well-observed stop-and-go waves. These traffic waves can arise even in the absence of merges, bottlenecks, or lane changing, and likely result in more energy consumption and emissions. Taking advantage of vehicle automation, we develop an approach to smoothing unstable traffic flow via optimal control of a small proportion of AVs in a predominantly human-driven traffic flow. These controlled AVs act as mobile actuators in mixed-autonomy traffic without changing the way HVs normally operate. We develop a general framework to describe mixed traffic flow with its dynamics abiding by car-following principles. Based on this framework, we synthesize optimal feedback controllers for AVs with the objective of minimizing speed disturbance, thereby resulting in smoother traffic. Following the necessary conditions of optimality prescribed by the Pontryagin's minimum principle, we present a computational algorithm for determining the optimal AV control strategy. The general framework is further illustrated using the intelligent driver model (IDM) and optimal velocity with relative velocity (OVRV) model for HVs and AVs, respectively, to show the effectiveness of the proposed approach on traffic smoothing, as well as the improvement on vehicle fuel economy and emissions. While the optimal AV controller synthesized above is shown to be effective in smoothing unstable mixed traffic, its performance on improving traffic stability is yet to be proven analytically and car-following safety is ensured in a fairly conservative manner. To address these challenging issues, we synthesize appropriate feedback controllers for AVs leveraging nonlinear stability theory. Specifically, we are interested to analytically synthesize appropriate feedback controllers of AVs for smoothing nonlinear mixed traffic in its general functional forms, covering a broad class of deterministic car-following models commonly seen in the literature. Essentially, AVs are controlled to operate in such a way that they closely track a virtual speed profile, i.e., a subtler version of the disturbance resulting from the immediately preceding vehicle. Thus, traffic waves are reduced when propagating backward across controlled AVs. Based on the general functional form of car-following dynamics, we derive a class of effective additive AV controllers that are proven to be able to ensure convergence in speed tracking, leading to smoother traffic. In addition, a set of sufficient conditions is devised for guaranteeing car-following safety. Notably, unlike many existing studies the feedback controllers synthesized require only local traffic information without having to rely on high degrees of vehicle connectivity, and the rate of traffic smoothing is readily tunable, which is useful for practical implementation. The proposed approach is further illustrated with a theoretical IDM and commercially available adaptive cruise control (ACC) vehicles represented by a well-calibrated IDM. In spite of the benefits promised by AVs like enhancing traffic stability shown above, emerging AV technologies open a door for cyberattacks, where a select number of AVs are compromised to drive in an adversarial manner. This could result in a network-wide increase in traffic congestion and vehicle fuel consumption, degrading the performance of transportation systems. Hence, developing effective attack mitigation strategies for AVs is critically important as AVs gradually become a reality. To this end, we derive optimal feedback control law for AVs in the presence of cyberattacks. Notably, attacks are only assumed to have a bounded magnitude (for remaining stealthy) without being subject to any specific probability distribution, which is not only of theoretical interest but also relaxes the assumptions of prior studies. More importantly, to deal with lack of knowledge of malicious attacks, we, for the first time, formulate a min-max control problem to minimize the worst-case potential disturbance to traffic flow. Specifically, under the framework of mixed-autonomy traffic presented before we consider two types of cyberattacks on AVs, namely false data injection attack on sensor measurements and malicious attack on AV control commands. Further, we derive a set of necessary conditions of optimality for the min-max control problem, based on which an iterative computational algorithm is developed for determining the optimal control (driving) strategy of AVs in a decentralized manner. The effectiveness of the proposed approach is demonstrated via numerical simulation considering different levels of attack severity.Item Responding to the Unexpected: Development of a Dynamic Data-Driven Model for Effective Evacuation(Minnesota Department of Transportation, 2009-12) Liu, Henry X.; Jabari, Saif EddinThis research proposes a framework for real-time traffic management under emergency evacuation. A theoretical framework is first proposed for adaptive system control that involves control updating based on real-world traffic data. A heuristic solution framework is then developed to address the computation complexities that come with real-time computations of evacuee routing strategies that aim at minimizing total evacuee exposure time to harm. Further improvements to network traffic throughput are also considered by incorporating officer deployment strategies to critical network intersections. A genetic algorithms based solution scheme is proposed for the combined evacuee routing and officer deployment problem. An evacuation software tool is developed with embedded GIS capabilities that allows users to build evacuation scenarios and run the developed heuristic algorithms. Finally, the quality and efficiency of the developed solution techniques are demonstrated via hypothetical real-world size evacuation scenarios using the software tools.