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  • Hongyu Sun, Henry X Liu, Heng Xiao, Rachel R He, Bin Ran
  • 2002
The traffic forecasting model, when considered as a system with inputs of historical and current data and outputs of future data, behaves in a nonlinear fashion and varies with time of day. Traffic data are found to change abruptly during the transition times of entering or leaving rush hours. Accurate and real-time models are needed to approximate the(More)
The tensor completion problem is to recover a low-n-rank tensor from a subset of its entries. The main solution strategy has been based on the extensions of trace norm for the minimization of tensor rank via convex optimization. This strategy bears the computational cost required by the singular value decomposition (SVD) which becomes increasingly expensive(More)
  • Henry X Liu, Xuegang Ban, Bin Ran, Pitu Mirchandani
The contents of this report reflect the views of the authors who are responsible for the facts and the accuracy of the data presented herein. The contents do not necessarily reflect the official views or policies of the State of California. This report does not constitute a standard, specification, or regulation. ABSTRACT Dynamic traffic assignment (DTA)(More)
This paper addressed a framework of a traffic prediction model which could eliminate the noises caused by random travel conditions. In the meantime, this model can also quantitatively calculate the influence of special factors. This framework combined several artificial intelligence technologies such as wavelet transform, neural network, and fuzzy logic. In(More)
  • Yang Cheng, Xiao Qin, Jing Jin, Bin Ran, Jason Anderson, Y Cheng +3 others
  • 2012
are steady-state models, in which traffic demands are assumed to be constant and the input and output flows reach equilibrium (2, 10). Further improvement includes providing queue length in small time stamps on the basis of vehicle arrival and departure profiles, first applied in the software TRANSYT (11). This approach was later extended and named the(More)