Creating, Calibrating, and Validating Large-Scale Microscopic Traffic Simulation
Abstract
The challenges of creating, calibrating, and validating a traffic microsimulation are not apparent
until one tries to create their own. Through the development of a traffic microsimulation of the San
Jose Mission district in Fremont, CA, this article shares a blueprint for creating, calibrating, and
validating a large-scale microsimulation of any city. Codes and data are made openly available for
anyone to reproduce the simulation or its creation inside the Aimsun microsimulator. The calibration process enables simulating the movement of 130,000 vehicles through a Fremont subnetwork
with more than 4,000 links using a representative 2019 afternoon six-hour demand. Executing the
simulation on calibrated data gives a linear regression between the simulated and real data with
slope of 0.976 and R^2 of 0.845 across 83 sensors at 15-minute time intervals.
until one tries to create their own. Through the development of a traffic microsimulation of the San
Jose Mission district in Fremont, CA, this article shares a blueprint for creating, calibrating, and
validating a large-scale microsimulation of any city. Codes and data are made openly available for
anyone to reproduce the simulation or its creation inside the Aimsun microsimulator. The calibration process enables simulating the movement of 130,000 vehicles through a Fremont subnetwork
with more than 4,000 links using a representative 2019 afternoon six-hour demand. Executing the
simulation on calibrated data gives a linear regression between the simulated and real data with
slope of 0.976 and R^2 of 0.845 across 83 sensors at 15-minute time intervals.
