Machine learning (ML) techniques have been applied for different applications:

– Density-based spatial clustering for analyzing user regularities based on their location-based checkins
– Unsupervised learning for analyzing traffic impacts under arterial road closures
– Exemplar-based support vector machines (SVM) for achieving real-time bus recognition
– Multi-layer feed-forward neural network to learn the inverse model for key process modules
– Ensemble learning for identifying songbird species in field recordings

  • X.-F. Xie, Z. J. Wang. An empirical study of combining participatory and physical sensing to better understand and improve urban mobility networks. Transportation Research Board (TRB) Annual Meeting, Washington, DC, USA, 2015.
  • Y. Gu, Z. (Sean) Qian, X.-F. Xie, S. Smith. An unsupervised learning approach for analyzing traffic impacts under arterial road closures: Case study of East Liberty in Pittsburgh. Transportation Research Board (TRB) Annual Meeting, Washington, DC, USA, 2015.
  • A. Mahendran, S. Smith, M. Hebert, X.-F. Xie. Bus Detection for Adaptive Traffic Signal Control. T-SET UTC Technical Report, Carnegie Mellon University, Pittsburgh, PA, 2014.
  • W.-J. Zhang, J.-L. Liang, X.-F. Xie, L.-L. Tian, Z.-L. Yang. Application of neural network method for process synthesis. International Conference on Machine Learning and Cybernetics (ICMLC), Beijing, China, 2002: 1121-1125. [DOI]