A Gaussian sampling heuristic estimation model for developing synthetic trip sets

In this paper, we develop a heuristic model based on Gaussian processes to determine synthetic sets of trips in urban networks, considering only supply-related information. This is an alternative to the benchmark method used in the literature, which consists of repeating several trials of Monte Carlo simulations and therefore requiring a complex calibration task and large computational resources. The developed heuristic model explicitly leverages the probabilistic nature of Gaussian processes and exploits their properties to iteratively select origin-destination (od) pairs of nodes in the city network. The model then determines the shortest trip in distance for the selected od pairs and appends it to the synthetic set. We discuss the implementation and performance of both the benchmark method and the developed heuristic model on two city networks. We show that the presented model is more robust and computationally efficient than the benchmark method. This is evidenced by its ability to determine synthetic sets with much smaller sizes, naturally reducing the computational burden, when compared to the benchmark method. We also discuss how the choice of the kernel function and calibration of the hyperparameters influence the performance of the presented heuristic model.