Reconstructing vehicle trajectories to support travel time estimation

Zheng Li, Robert Kluger, Xianbiao Hu, Yao Jan Wu, Xiaoyu Zhu

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


The primary objective of this study was to increase the sample size of public probe vehicle-based arterial travel time estimation. The complete methodology of increasing sample size using incomplete trajectory was built based on a k-Nearest Neighbors (k-NN) regression algorithm. The virtual travel time of an incomplete trajectory was represented by similar complete trajectories. As incomplete trajectories were not used to calculate travel time in previous studies, the sample size of travel time estimation can be increased without collecting extra data. A case study was conducted on a major arterial in the city of Tucson, Arizona, including 13 links. In the case study, probe vehicle data were collected from a smartphone application used for navigation and guidance. The case study showed that the method could significantly increase link travel time samples, but there were still limitations. In addition, sensitivity analysis was conducted using leave-one-out cross-validation to verify the performance of the k-NN model under different parameters and input data. The data analysis showed that the algorithm performed differently under different parameters and input data. Our study suggested optimal parameters should be selected using a historical dataset before real-world application.

Original languageEnglish (US)
Pages (from-to)148-158
Number of pages11
JournalTransportation Research Record
Issue number42
StatePublished - May 1 2018

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Mechanical Engineering


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