Reconstruction of a Car-Running Pedestrian Accident Based on a Humanoid Robot Method.

Due to the characteristics of multibody (MB) and finite element (FE) digital human body models (HBMs), the reconstruction of running pedestrians (RPs) remains a major challenge in traffic accidents (TAs) and new innovative methods are needed. This study presents a novel approach for reconstructing moving pedestrian TAs based on a humanoid robot method to improve the accuracy of analyzing dynamic vehicle-pedestrian collision accidents. Firstly, we applied the theory of humanoid robots to the corresponding joints and centroids of the TNO HBM and implemented the pedestrian running process. Secondly, we used rigid-flexible coupling HBMs to build pedestrians, which can not only simulate running but also analyze human injuries. Then, we validated the feasibility of the RP reconstruction method by comparing the simulated dynamics with the pedestrian in the accident. Next, we extracted the velocity and posture of the pedestrian at the moment of collision and further validated the modeling method through a comparison of human injuries and forensic autopsy results. Finally, by comparing two other cases, we can conclude that there are relative errors in both the pedestrian injury results and the rest position. This comparative analysis is helpful for understanding the differences in injury characteristics between the running pedestrian and the other two cases in TAs.

Mining Spatial Co-Location Patterns With a Mixed Prevalence Measure.

A co-location pattern indicates a subset of spatial features whose instances are frequently located together in proximate geographical space. Most previous studies of spatial co-location pattern mining concern what percentage of instances per feature are involved in the table instance of a pattern, but neglect the heterogeneity in the number of feature instances and the distribution of instances. As a result, the deviation may be occurred in the interest measure of co-locations. In this article, we propose a novel mixed prevalence index (MPI) incorporating the effect of feature-level and instance-level heterogeneity on the prevalence measure, which can address some dilemmas in existing interest measures. Luckily, MPI possesses the partial antimonotone property. In virtue of this property, a branch-based search algorithm equipped with some optimizing strategies of MPI calculation is proposed, namely, Branch-Opt-MPI. Comprehensive experiments are conducted on both real and synthetic spatial datasets. Experimental results reveal the superiority of MPI compared to other interest measures and also validate the efficiency and scalability of the Branch-Opt-MPI. Particularly, the Branch-Opt-MPI performs more efficiently than baselines for several times or even orders of magnitude in dense data.