Off-line Multi-Sensor Multi-Source Calibration of Dynamic Traffic Assignment: Simultaneous Demand-Supply Estimation based on Genetic Algorithms in a High-Performance Computer
Date
2014-01-30
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Abstract
The complexity of transportation systems often dictates the use of detailed simulation-based dynamic traffic assignment (DTA) models to replicate the complex traffic flow dynamics. Recent advancements in computer technology have led to the development of high-fidelity simulation models; however, in order to be used as reliable tools, the simulation input parameters should be properly calibrated in order to replicate prevailing traffic conditions. Thus, this thesis has focused on the off-line simultaneous calibration of demand and supply parameters of the DTA model in a microscopic context that can capture the interactions between all parameters. The demand parameters include dynamic OD flows, while the route choice and driver behavior model parameters are considered as supply parameters. The calibration process has been formulated as a multi-objective optimization problem that incorporates the traffic data from multiple sources, ranging from traditional loop detector data to traffic data from recent emerging technologies, and allocates relative weights to different terms of the objective function.
A genetic algorithm (GA) is selected as a suitable solution algorithm for the resulting nonlinear stochastic optimization problem. The application of the proposed methodology was implemented in a synthetic case study as well as a complex network in the business district core of downtown Toronto, Ontario, Canada. For this network, the emerging traffic surveillance data from in-vehicle navigation system technology provide an enrich source of disaggregate speed data.
The empirical results from various experiments support the hypothesis that the incorporation of the in-vehicle navigation system speed data can significantly improve the calibration accuracy and minimize the dependency of the calibration process on the historical OD flows. The quality of the solution and convergence speed of a GA is further enhanced by dividing the GA population into multiple demes and running the GA on a high-performance computer (HPC) cluster with multiple processors (i.e. parallel distributed GA, PDGA).In addition, this research takes a further step towards analyzing the temporal variations of the driving behavior of travelers, especially during different time intervals of peak periods.
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Engineering--Civil
Citation
Omrani, R. (2014). Off-line Multi-Sensor Multi-Source Calibration of Dynamic Traffic Assignment: Simultaneous Demand-Supply Estimation based on Genetic Algorithms in a High-Performance Computer (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/27165