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2020 New Faculty Research Seminar Series Presents Shirin Noei, Ph.D.

Shirin Noei portraitLongitudinal Dynamics in Traffic Microsimulation

Thursday, Nov. 5, 2020 | 4:30 – 5:30 p.m.

Seminar Zoom Link

 

Presented by Shirin Noei Ph.D., Research Assistant Professor, Center for Energy Systems Research

Abstract: Simulated accelerations and decelerations are sensitive to maximum acceleration and maximum deceleration inputs, particularly for scenarios with significant truck share. Conventional traffic microsimulation tools estimate maximum acceleration and maximum deceleration using simplified mechanistic models, empirical models, or lookup tables, or some even have maximum acceleration and maximum deceleration as user inputs. Simplified models used in most traffic microsimulation tools may have been a necessary compromise long ago due to software architecture, computational speed limitations, or to minimize data input requirements. Unlike conventional traffic microsimulation tools, several simulation tools (e.g., CarMaker and CarSim) can imitate vehicle performance precisely but cannot or do not even intend to simulate large-scale transportation networks due to computational complexities.  Conventional longitudinal controllers rely on constant distance gaps to maximize throughput, constant time gaps to ensure string stability, and constant controller coefficients, potentially reducing throughput or sacrificing safety. This research incorporates driver characteristics, physical, engine, transmission, and drivetrain properties, aerodynamic resistance, rolling resistance, grade resistance, and speed into calculating maximum acceleration, maximum deceleration, minimum safe distance gap, minimum safe time gap, and longitudinal controller coefficients for multiple vehicles at each simulation time step with reasonable accuracy and simulation speed in order to maximize throughput without compromising safety or string stability. Proposed models are verified for fourteen vehicle models operating in autonomous mode over two driving schedules. Results show that maximum acceleration, maximum deceleration, minimum safe distance gap, minimum safe time gap, and longitudinal controller coefficients are sensitive to driver characteristics, vehicle properties, road conditions, and speed.