this paper presents a parameterization method to optimal trajectory planning and dynamic obstacle avoidance for omni-directional robots. the aim of trajectory planning is minimizing a quadratic cost function while a maximum limitation on velocity and acceleration of robot is considered. first, we parameterize the trajectory using polynomial functions with unknown coefficients which transforms t...

Numerous statistical and data-driven modeling frameworks have estimated rear-end crashes crash-prone events from macroscopic traffic states which are at the heart of flow modelling control. However, existing focus on critical exclude a vast majority safer interactions, essential information with respect to identifying trade-offs between congestion management crash prevention. This study propose...

This paper presents two case studies where a macroscopic model-based approach for traffic state estimation, which we have recently developed, is employed and tested. The estimation methodology is developed for a “mixed” traffic scenario, where traffic is composed of both ordinary and connected vehicles. Only average speed measurements, which may be obtained from connected vehicles reports, and ...

Trajectory data play a fundamental role to an increasing number of applications, such as traffic control, transportation management, animal migration, and tourism. These data are normally available as sample points. However, for many applications, meaningful patterns cannot be extracted from sample points without considering the background geographic information. In this paper we present a fram...