Abstract: Mobility is central to urbanity, and urbanity is central to our common future as the world’s population crowds into urban areas. This is creating a global urban mobility crisis due to the unsustainability of our 20th century transportation systems for an urban world. Fortunately, the science and planning of urban mobility is transforming away from infrastructure as the solution towards a sustainable mobility paradigm that manages rather than encourages travel, diminishes mobility and accessibility inequities, and reduces the harms of mobility to people and environments. In this essay, I discuss the contributions over the past decade of movement analytics to sustainable mobility science and planning. I also highlight two major challenges to sustainable mobility that should be addressed over the next decade.
Keywords: movement analytics, mobility science, animal movement ecology, sustainable mobility, urbanity
Space-time prisms are physically impossible since they assume infinite acceleration and deceleration. This paper is a first step in resolving this issue. A theoretical description of kinetic prism geometry, but provides insights that can lead to moving objects data analytics with practical applications (e.g., animal movement, vehicle energy consumption and emissions, bicycles and other forms of active transportation)
Abstract: Presently, data concerning moving objects abound. These data mainly consist of time-stamped geographical locations, which are collected by location aware devices, such as Global Positioning System receivers. Space–time prisms are used to model the spatio-temporal space of potential movement in between measured locations (called anchors). They rely on the knowledge of the maximal speed of travel of an object and they capture all space–time paths that respect this speed limit. However, the classic space–time path and prism model is not physically realistic, in the sense that it contains spatio-temporal paths of moving objects can alter their direction and speed instantaneously. Since this is physically impossible, the classical model is not acceptable in applications where mechanics and kinetics are vital. We propose a more realistic version of space–time prisms, in which not only speed but also acceleration is bounded. This additional bound results in a physically realistic model, which we refer to as kinetic prisms. Furthermore, we study how imposing constraints on the speed and heading at anchor points affects the geometry of kinetic prisms. In this paper, we give analytical descriptions of kinetic prisms and algorithms for their construction for movement in one- and two-dimensional space.
Keywords: Spatio-temporal data models, moving-object data, space–time prisms