Browsing by Subject "Kalman filtering"

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    Development of On-Line Control Strategies in Freeway Networks, Phase 2: Final Report
    (Minnesota Department of Transportation, 1998-05) Stephanedes, Yorgos J.; Liu, Xiao; Liu, Lu; Michel, Bernard R.
    Most traffic-responsive freeway ramp metering systems select metering rates from predetermined rate libraries. The efficiency of such systems is impaired by the lack of an efficient analysis tool that can evaluate and update the thresholds and rate libraries used by the meter controllers. In this project, a control-emulation method is developed to evaluate various automatic rateselection strategies; the new modeling features of this system are described in detail. Various rate selection strategies (based on neural network processing, exit ramp volume, and real time bottleneck/dynamic zone determination) are described and evaluated in comparison with the current Minneapolis-St. Paul strategy. An online traffic volume predictor based on Kalman filtering is developed, and integrated into the control-emulation module. A simulated annealing optimization algorithm, previously implemented on a supercomputer, is re-implemented on a personal computer and integrated into the simulation module.
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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.