High Dimensional Origin Destination Calibration Using Metamodel Assisted Simultaneous Perturbation Stochastic Approximation

The huge traffic data generated by intelligent transportation system (ITS) leads to the development of many advanced traffic models. These traffic models consist of many adjustable parameters which need to be calibrated before they are used in practice. This paper focuses on the offline calibration of origin-destination (OD) input parameters of a simulation-based traffic model to match its output with sensor data. An improved Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm called Metamodel Assisted Simultaneous Perturbation Stochastic Approximation (MSPSA) is proposed in this paper to calibrate high-dimensional OD parameters within a tight computational budget. The proposed MSPSA combines the gradient of SPSA with the gradient of a differentiable metamodel function to improve the calibration efficiency. An integer program is also used to fine-tune the OD estimates. The proposed MSPSA algorithm is tested on a simple synthetic toy network and complex road network of Kuala Lumpur (KL), Malaysia. The proposed MSPSA algorithm is compared against SPSA and state-of-the-art Weighted Simultaneous Perturbation Stochastic Approximation (WSPSA) in both transportation networks. For KL network, synthetic and real-world sensor measurements are used as ground truth references to evaluate the performance of each approach. Based on the simulation results, the proposed MSPSA algorithm is able to gain at least 50% of improvement as compared to SPSA and WSPSA in both synthetic and real-world scenarios.