Although stable in the short term, individual travel behavior generally tends to change over the long term. The ability to detect such changes is important for product and service providers in continuously changing environments. The aim of this paper is to develop a methodology that detects changes in the patterns of individual travel behavior from vehicle global positioning system (GPS)/global navigation satellite system (GNSS) data. For this purpose, we first define individual travel behavior patterns in two dimensions: a spatial pattern and a frequency pattern. Then, we develop a method that can detect such patterns from GPS/GNSS data using a clustering algorithm. Finally, we define three basic pattern-change scenarios for individual travel behavior and introduce a pattern-matching metric for detecting these changes. The proposed methodology is tested using GPS datasets from three randomly selected anonymous users, collected by a Chinese automotive manufacturer. The results show that our methodology can successfully identify significant changes in individual travel behavior patterns.

Here, we elucidate the significant impact of carbon nanotubes (CNTs) on the electrochemical behavior of Mg-based amorphous composite materials that were reinforced with CNTs while using pressure die casting. The addition of 3 vol % CNTs led to an increase in the compressive strength of Mg-based amorphous material from 812 MPa to 1007 MPa, and the fracture strain from 1.91% to 2.67% in the composite. Interestingly, the addition of CNTs significantly contributed to the enhancement of corrosion resistance of Mg-based glass by ~30%. The superior mechanical properties are primarily related to the fact that the addition of CNTs hindered the growth of shear bands (cracks), while the high corrosion resistance is related to inferior wettability and the bridging effect between adherent corrosive oxide film and the matrix that provided enhanced corrosion resistance.