Abstract Manager

Simulation-based dynamic traffic assignment (DTA) has been motivated by a desire to introduce greater realism and fidelity for larger scale applications to better represent traffic congestion, to investigate impacts of complex traffic management and tolling schemes, and to fully capture the time-dependent nature of route choice and travel time along with other relevant metrics.

Calibration of large-scale simulation models, which can be very challenging, involves the adjustment of three distinct types of data: traffic flow parameters, route choice parameters and travel demand matrices. Of these three, travel demand matrices represent the data set that has both the highest dimensionality, and in many cases, the highest uncertainty, particularly for wide-area applications. In many cases, the adjustment of demand matrices plays a critical role in the calibration process to achieve desired goodness-of-fit metrics between model outputs and observed data.

In this paper we present a method for dynamic (time dependent) adjustment of demand matrices for simulation-based dynamic traffic assignment. The method is structured as an iterative procedure, where each iteration includes a matrix adjustment step followed by a full equilibrium DTA run. The method is designed for use with traffic simulation models that strictly respect fundamental traffic flow properties, such as microscopic traffic simulators and certain mesoscopic traffic simulators.

Key traffic flow properties are fixed capacities and spillback effects. As a result of these, it is very common for a model to exhibit low volumes due to high demands which result in high congestion, requiring a reduction in demand (rather than an increase) to produce higher model volumes. The matrix adjustment method has key innovations that allow it to identify and address these types of low-volume cases, and to carefully manage the cases where demand increases are required to avoid triggering new bottlenecks unnecessarily.

The method was tested on a several real-world models that had gone through a basic level of calibration so that the supply-side data, including network coding, traffic control, and traffic flow parameters, were essentially verified. The models were of considerable size both spatially and temporally, with varying levels of congestion. The method was successfully applied across all the tests, consistently providing significant increases in goodness-of-fit statistics. Typical runs varied between 4 and 6 iterations, indicating reasonable run times. The method has considerable value as practical tool for calibrating large, real-world simulation based DTA applications exhibiting significant levels of congestion.