Browsing by Author "Levin, Michael"

Now showing 1 - 8 of 8
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    Book review: Feminism Unmodified. By Catherine MacKinnon ; Gender and History. By Linda J. Nicholson.
    (University of Minnesota Law School, 1988) Levin, Michael
    Book review: Feminism Unmodified. By Catherine MacKinnon. Cambridge, Ma.: Harvard University Press. 1987. Pp. 315 ; Gender and History. By Linda J. Nicholson. New York, N.Y.: Columbia University Press. 1986. Pp. x, 238. Reviewed by: Michael Levin.
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    Book review: Life in the Balance: Exploring the Abortion Controversy. By Robert N. Wennberg.
    (University of Minnesota Law School, 1986) Levin, Michael
    Book review: Life in the Balance: Exploring the Abortion Controversy. By Robert N. Wennberg. Grand Rapids, Mich.: William B. Eerdmans Publishing Co. 1985. Pp. xi, 184. Reviewed by: Michael Levin.
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    Book review: The Morality of Groups: Collective Responsibility, Group-Based Harm, and Corporate Rights. By Larry May.
    (University of Minnesota Law School, 1989) Levin, Michael
    Book review: The Morality of Groups: Collective Responsibility, Group-Based Harm, and Corporate Rights. By Larry May. Notre Dame, Indiana: University of Notre Dame Press. 1987. Pp. xii, 200. Reviewed by: Michael Levin.
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    Correspondence
    (University of Minnesota Law School, 1988) Shane, Peter M.; Levin, Michael
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    Generating Traffic Information from Connected Vehicle V2V Basic Safety Messages
    (Minnesota Department of Transportation, 2021-03) Chen, Rongsheng; Levin, Michael; Hourdos, John; Duhn, Melissa
    Basic Safety Message (BSM) containing data about the vehicle's position, speed, and acceleration. Roadside receivers, RSUs, can capture BSM broadcasts and translate them into information about traffic conditions. If every vehicle is equipped with awareness, BSMs can be combined to calculate traffic flows, speeds, and densities. These three key parameters will be post-processed to obtain queue lengths and travel time estimates. The project team proposed a traffic state estimation algorithm using BSMs based on the Kalman filter technique. The algorithm's performance was tested with BSMs generated from several arterial in a microscopic simulation model and BSMs generated with radar data collected on freeway sections. Then the project team developed a traffic monitoring system to apply the algorithm to a large-scale network with different types of roads. In the system, computers could remotely access the online server to acquire BSMs and estimate traffic states in real-time.
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    Improving intersection safety through variable speed limits for connected vehicles
    (Center for Transportation Studies, University of Minnesota, 2019-05) Levin, Michael; Chen, Rongsheng; Liao, Chen-Fu; Zhang, Tab
    Autonomous vehicles create new opportunities for innovative intelligent traffic systems. Variable speed limits, which is a speed management systems that can adjust the speed limit according to traffic condition or predefined speed control algorithm on different road segments, can be better implemented with the cooperation of autonomous vehicles. These compliant vehicles can automatically follow speed limits. However, non-compliant vehicles will attempt to pass the moving bottleneck created by the compliant vehicle. This project builds a multi-class cell transmission model to represent the relation between traffic flow parameters. This model can calculate flows of both compliant and non-compliant vehicles. An algorithm is proposed to calculate variable speed limits for each cell of the cell transmission model. This control algorithm is designed to reduce the stop-and-go behavior of vehicles at traffic signals. Simulation is used to test the effects of VSLs on an example network. The result shows that VSL is effective at reducing the energy consumption of the whole system and reduce the likelihood of crash occurrence.
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    Non-linear spacing policy and network analysis for shared-road platooning
    (Center for Transportation Studies, University of Minnesota, 2019-08) Levin, Michael; Rajamani, Rajesh; Jeon, Woongsun; Chen, Rongsheng; Kang, Di
    Connected vehicle technology creates new opportunities for obtaining knowledge about the surrounding traffic and using that knowledge to optimize individual vehicle behaviors. This project creates an interdisciplinary group to study vehicle connectivity, and this report discusses three activities of this group. First, we study the problem of traffic state (flows and densities) using position reports from connected vehicles. Even if the market penetration of connected vehicles is limited, speed information can be inverted through the flow-density relationship to estimate space-and time-specific flows and densities. Propagation, according to the kinematic wave theory, is combined with measurements through Kalman filtering. Second, the team studies the problem of cyber-attack communications. Malicious actors could hack the communications to incorrectly report position, speed, or accelerations to induce a collision. By comparing the communications with radar data, the project team develops an analytical method for vehicles using cooperative adaptive cruise control to detect erroneous or malicious data and respond accordingly (by not relying on connectivity for safe following distances). Third, the team considers new spacing policies for cooperative adaptive cruise control and how they would affect city traffic. Due to the computational complexity of microsimulation, the team elects to convert the new spacing policy into a flow-density relationship. A link transmission model is constructed by creating a piecewise linear approximation. Results from dynamic traffic assignment on a city network shows that improvements in capacity reduces delays on freeways, but surprisingly route choice increased congestion for the overall city.
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    Pressure-based dispatch for shared autonomous vehicles
    (Center for Transportation Studies, University of Minnesota, 2019-08) Levin, Michael; Kang, Di
    Shared autonomous vehicle (SAV) technology is rapidly maturing, with two companies (Uber and Waymo) already testing SAV services in cities in the US. Due to the point-to-point service and lack of a driver, SAV service costs could be similar to that of personal vehicles, resulting in major mode choice changes for daily travel. A major issue for SAV operators is the SAV dispatch problem, i.e., how to optimally assign vehicles to waiting passengers. SAV dispatch is essentially a vehicle routing problem, which is NP-hard, and is complicated by fleets measured in thousands of vehicles in typical cities. Previous studies have attempted to quantify the number of passengers served per SAV using agent-based simulation studies on realistic networks, with a variety of results that highly depend on the heuristic chosen for SAV dispatch. Ideally, the optimal SAV dispatch strategy would serve as many passengers as any other policy. This project created a max-pressure dispatch policy, which was analytically proven by showing stability in the number of unserved passengers through a Lyapunov function. Essentially, the work analytically compared the serviceable demand from the max-pressure dispatch to the demand that could be served by any other dispatch policy. The max-pressure policy relied on a planning horizon; as the horizon grows to infinity, the policy becomes arbitrarily close to any sequence of SAV movements that can serve given demand rates.