Empirical validation of network learning with taxi GPS data from Wuhan, China
This provides a practical tool for urban planners to monitor network performance with limited data, though it is incremental as it validates an existing method on new data.
The study validated a multi-agent inverse optimization method for monitoring transportation networks using taxi GPS data from Wuhan, China, showing that with samples from one OD pair, forecasted travel times correlated at 0.23 with observed times, improving to 0.56 with two OD pairs.
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.