LAUSR

Transportation is getting smarter and smarter, with the prominence of connected automated vehicle technologies in the global auto industry’s near-term growth strategies, of big data analytics and unprecedented access to sensing data of mobility, and of integration of this analytics into the optimization of mobility and transport. These developments are setting off a wave of smart transportation innovations, which are featured by new methods and applications driven by various forms of sensor data such as GPS, CAN bus, LIDA, images, etc. At the same time, complexities surrounding the use, conflation, and processing of disparate data in near real-time is of essence for the design and development of futuristic smart transportation. Computational methods for transportation science are the drivers for smart transportation, and thus essential for a sustainable future of cities concerning urban mobility (people) and transport (freight). The fundamentals of these computational methods are rooted in space and time, and thus at the core of transportation research. A research subject of smart transportation that has become extremely active within the last few years is trajectory computing. Trajectories, sometimes called probe data or location/GPS traces, are a treasure nowadays for understanding travelers, traffic, and locations. Trajectory computing sees many applications in transportation such as map making, traffic information, routing, driver behavior, activity recognition, etc. To tackle the challenges brought up by the numerous and typically imprecise trajectory data, the research community has been exploring solutions to trajectory storage, retrieval, and mining, as well as uncertainty management, map matching, privacy preservation etc. The special issue of Smart Transportation contains five papers on various aspects of smart transportation. They cover ridesharing, human mobility modeling, trajectory computing, routing, and privacy control. GeoInformatica https://doi.org/10.1007/s10707-021-00432-3