Trajectory data reconstruction and simulation-based validation against macroscopic traffic patterns

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Urban traffic signal control with connected and automated vehicles: A survey (2019, methodology): “Instead, such data were usually used to estimate freeway traffic flow states or vehicle travel times (Montanino and Punzo, 2015; Seo and Kusakabe, 2015; Van, 2010).”
Speed or spacing? Cumulative variables, and convolution of model errors and time in traffic flow models validation and calibration (2016, background): “…et al., 2000) against both speed and inter-vehicle spacing for all the 2037 trajectories in the I80-1 ‘reconstructed’ NGSIM dataset (Montanino and Punzo, 2015): Bari, September 14th, 2017 27SIDT 2017Punzo & Montanino, UNINA Specification for macroscopic traffic flow models ✓ Let’s assume: •…”
A critical evaluation of the Next Generation Simulation (NGSIM) vehicle trajectory dataset (2017, methodology): “Montanino and Punzo (2015) is probably the most thorough preceding work and they did a laudable effort to clean the NGSIM data, but their cleaning relies strictly upon the NGSIM vehicle trajectory database.”
Safe, Efficient, and Comfortable Velocity Control based on Reinforcement Learning for Autonomous Driving (2019, methodology): “To enhance data quality, the reconstructed NGSIM I-80 data [15] were utilized.”