With the development of Internet technology, online car-hailing is booming in China, which has profoundly affected people’s travel structures. In order to seek the sustainable development of taxi and online car-hailing services from the perspective of passenger mode choice behavior, the mechanism of passengers’ decision-making procedures and their travel mode choice behaviors were analyzed. To study the influence of latent variable factors on passenger choice behavior, this paper firstly designed a questionnaire, and a structural equation model (SEM) was established for the preliminary study of the relationship between the latent variables and the behavioral intentions using the online survey data. Then, the latent variables were introduced into the Logit model, setting up the SEM-Logit model to explore the mode choice patterns between taxis and online car services. The results showed that the SEM-Logit model with the latent variables is better than a general Logit model in terms of the model precision and hit ratio. Meanwhile, after introducing the latent variables, it was found that convenience, comfort, and economy factors have a significant influence on the model, and the explanatory power of the model increases accordingly.

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To help automated vehicles learn surrounding environments via V2X communications, it is important to detect and transfer pedestrian situation awareness to the related vehicles. Based on the characteristics of pedestrians, a real-time algorithm was developed to detect pedestrian situation awareness. In the study, the heart rate variability (HRV) and phone position were used to understand the mental state and distractions of pedestrians. The HRV analysis was used to detect the fatigue and alert state of the pedestrian, and the phone position was used to define the phone distractions of the pedestrian. A Support Vector Machine algorithm was used to classify the pedestrian’s mental state. The results indicated a good performance with 86% prediction accuracy. The developed algorithm shows high applicability to detect the pedestrian’s situation awareness in real-time, which would further extend our understanding on V2X employment and automated vehicle design.

We describe a method to quickly reconstruct 3D garment model. We first use a depth camera scanning device to quickly obtain 3D point cloud data on the surface of the clothing, pre-process the point cloud data. Then combining the region growth algorithm and Delaunay triangulation algorithm, we propose a surface reconstruction algorithm that takes undirected point clouds as input and generates interpolated surfaces in the form of triangulation to reconstruct the clothing model. We build this systematic 3D garment modeling program. Through our method we obtain a uniformly distributed grid, smooth patches, and also retain the original shape of the clothing point cloud. Our method increases the convenience of garment modelling, reduces the manpower and time for garment modeling, helps expand the garment database of the virtual fitting system, and provides ideas for garment electronic sales.