An empirical validation of network learning with taxi GPS data from Wuhan, China

Susan Jia Xu, Qian Xie, Joseph Y.J. Chow, Xintao Liu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)

Abstract

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 multiagent inverse optimization (MAIO) method using taxi GPS trajectory data from the city of Wuhan, China. Using a controlled 2,062-link network environment and different GPS data processing algorithms, an online monitoring environment was simulated using real data over a 4-h period. Results show that using samples from only one origin-destination (OD) pair, the MAIO method can learn network parameters such that forecasted travel times have a 0.23 correlation with the observed travel times. By increasing the monitoring from just two OD pairs, the correlation improved further, to 0.56.

Original languageEnglish
Article number9277883
Pages (from-to)42-58
Number of pages17
JournalIEEE Intelligent Transportation Systems Magazine
Volume13
Issue number1
DOIs
Publication statusPublished - 1 Mar 2021

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

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