Macroscopic Modeling and Simulation of Freeway Traffic Flow

Abstract

This paper illustrates the macroscopic modeling and simulation of Interstate 80 Eastbound Freeway in the Bay Area. Traffic flow and occupancy data from loop detectors are used for calibrating the model and specifying the inputs to the simulation. The freeway is calibrated based on the Link-Node Cell Transmission Model and missing ramp flow data are estimated using an iterative learning-based imputation scheme. An adhoc, graphical comparison-based fault detection scheme is used to identify faulty measurements. The simulation results using the calibrated model exhibit good agreement with loop detector measurements with total density error of 3.3% and total flow error of 7.1% over the 23 mile stretch of the freeway under investigation and the particular day for which the ramp flows were imputed.

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“…Traffic density and load -input data indispensable for SCATS -are measured by detectors (inductive loops) installed a short distance ahead of the limit line (Li et al, 2014) on each lane. On the basis of this data it is possible to capture traffic volumes within the network (Kowalski and Wiśniewski, 2017) and their directional structure (Borowska-Stefańska et al, 2021b), which is particularly valuable for both macro- (Hueper et al, 2009) and intermediate-scale analyzes (Bieker et al, 2015;Borowska-Stefańska et al, 2021a). The aforementioned detectors (inductive-loop traffic detectors) are a solution that is most commonly applied to measure road traffic parameters, since they offer good performance regardless of weather conditions (Gajda et al, 2001).…”

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