Browsing by Subject "Traffic estimation"
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Item Generating Traffic Information from Connected Vehicle V2V Basic Safety Messages(Minnesota Department of Transportation, 2021-03) Chen, Rongsheng; Levin, Michael; Hourdos, John; Duhn, MelissaBasic 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.Item 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, DiConnected 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.