In prior research, a statistically cheap method was developed to monitor transportation network performance by using only a few groups of agents without having to forecast the population flows. The current study validates this "multi-agent inverse optimization" method using taxi GPS probe data from the city of Wuhan, China. Using a controlled 2062-link network environment and different GPS data processing algorithms, an online monitoring environment is simulated using the real data over a 4-hour period. Results show that using only samples from one OD pair, the multi-agent inverse optimization method can learn network parameters such that forecasted travel times have a 0.23 correlation with the observed travel times. By increasing to monitoring from just two OD pairs, the correlation improves further to 0.56.
@article{arxiv.1911.03779,
title = {Empirical validation of network learning with taxi GPS data from Wuhan, China},
author = {Susan Jia Xu and Qian Xie and Joseph Y. J. Chow and Xintao Liu},
journal= {arXiv preprint arXiv:1911.03779},
year = {2021}
}