Nakorn Indra-Payoong

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This paper proposes a vehicle reidentification (VRI) system with self-adaptive time windows to estimate the mean travel time for each time period on the freeway under traffic demand and supply uncertainty. To capture the traffic dynamics in real-time application, interperiod adjusting based on the exponential smoothing technique is introduced to define an(More)
This paper proposes multi-features visual tracking algorithm based on the particle Probability Hypothesis Density filter, which allows accurate and robust tracking under the circumstance of visual tracking. We apply a particle PHD filter implementation to the multiple humans tracking using multi-features observation that exploits skin and head-and-shoulder(More)
The paper proposes a hybrid algorithm for solving bus crew scheduling problem (CSP). The CSP involves an assignment of a number of staff to different scheduled bus services. The problem is normally constrained by a number of operational and practical constraints, such as crew preferences, crew satisfaction, and work-shift, etc. These constraints are(More)
The stochastic cell transmission model (SCTM) was originally developed for stochastic dynamic traffic state modeling under several assumptions, e.g., the independent/uncorrelated assumption of the underlying stochastic processes governing demand and supply uncertainties. However, traffic flow, by nature, is correlated in both spatial and temporal domains(More)
Tracking moving human from a monocular vision using visual tracking is a useful skill for the coming generation of human-machine interface. It is a challenging problem of planning and control in dynamic environment. The methods used in existing moving human tracking that operate from fixed platform or fixed background, are not applicable. In this paper, we(More)
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